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Newsletter of HoloGenomics
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Newsletter of HoloGenomics
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Genomics, Epigenomics integrated into Informatics:
HoloGenomics
The Decade of Genomic Uncertainty is Over
Skip to HoloGenomics Table of Contents
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Eric Lander (Science Adviser to the President and Director of Broad Institute) et al. delivered the message
on Science Magazine cover (Oct. 9, 2009) to the effect:
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For archived HoloGenomics News articles in the year of 2010 click here.
For archived HoloGenomics News articles before 2010 click here.
Articles before Hologenomics (before mid-2008) are listed in Archives, bottom here.
Non-coding RNA .. is acutely regulated .. in schizophrenia..!
Genome Misunderstood for Fifty Years - Low levels of RNA linked to schizophrenia
The Sidney Morning Herald
April 30, 2013
[The science article by Mattick et al. (The long non-coding RNA Gomafu is acutely regulated in response to neuronal activation and involved in schizophrenia-associated alternative splicing) and its popular coverage below heralds a new era. Not only in Genomics, but also in a now rapidly merging Genomics & Neuroscience; "Our understanding of both the genome and the brain will remain partial and disjointed until we reach a unification of the intrinsic mathematics of structuro-functional geometry of both – as the first is without question a foundation of the second" [Pellionisz et al 2012].
Gone are the decades of empty “debates” over the “genes and junk governed by dogma”. All are surpassed by the paradigm of The Principle of Recursive Genome Function [2008], opening the floodgate to recursive algorithms "permitted" to make genome regulation software enabling). After half a Century, especially marked by ENCODE 2012, the question is no longer if the human genome is mostly not, or to whatever percentage it still may be, "Junk". Mattick won a "case of vintage champagne" by his bet with Birney - thus the number-game of "exact percentage" is one of those pseudo-scientific un-answerable questions, like exactly how many stars the Universe contains (by the time you could count them, some stars would die and others would be born - or ad absurdum "how many angels can dance on the tip of a needle").
Science moves beyond detractors to the real software-enabling question of the algorithms of "genome regulation" - by that whatever percent.
The article by Mattick et al. dashes false hopes that for specific diseases we can, by necessity, find "a gene as a culprit" (we should keep in mind that the "gene" itself is undefined since ENCODE 2007). Cancer is already admitted to be the ultimate "genome regulation disease" with too many genomic defects for the un-aided researcher/therapist. False hopes are abandoned to find any single "cancer gene", and thus the same might be true to so many other syndromes not yet addressed in the era of a new paradigm. The sober recognition remains that therapy up to a cure before all calls for an understanding "genome regulation" - for medicine to save costs measured in human life and dollars. Next to cancer, autism & schizophrenia as well as scores of auto-immune diseases have already been linked to massive "fractal defects" implicating vast arrays of "non-coding DNA". IP is already available by appointment for consultation leading to licensing of fractal utilities.
Mattick et al. provides now independent experimental evidence for "long non-coding RNA" linked to one of the most serious diseases (schizophrenia) falling into the domain of neuroscience, that is can no longer be reserved to exclusive search for any imaginary "schizo-gene". Instead, Mattick et al. challenge our understanding of genome regulation, with the long-neglected "RNA system" so wisely put into a laser-beam focus. This is in the larger context of the assessment after the first decade of the Human Genome Project; ''Spending lots of money to generate huge data sets without any real effort to getting to new knowledge or understanding has been a huge frustration,'' ... "'It's now easy with the new technology to generate a lot of different data, but there are very few groups or scientists generating knowledge out of this data. We're at a frighteningly unsophisticated level of genome interpretation.''. - comment by AJP]
[Public relation coverage of new results by Mattick et al. below - AJP]
Australian scientists have described how some neurons in the brain switch off certain genes as part of normal brain function, a process that appeared disturbed in people with schizophrenia.
While most people were familiar with the role DNA played instructing the body's cells to produce proteins, a far greater amount of genetic material, called non-coding RNA, was involved in regulating the expression of the genome in response to external cues, such as when neurons fire in the brain.
One of the research leaders, John Mattick, said in the brain, sections of these genes, known as long non-coding RNA, once activated, guided the behaviour of neurons.
"In the brain these processes are soft-wired [thus should be "softwared" ASAP - AJP] and they respond and adapt to external cues," said Professor Mattick, the executive director of the Garvan Institute of Medical Research.
Over the past two decades Professor Mattick and other others have shown the body's vast amounts of non-coding RNA are not junk as previously thought, but essential to regulating the function of other genes.
"People have misunderstood the structure of human genetic programming for the past 50 years," he said.
"The evidence is very strong that most of the human genome is active in producing RNA, most of which controls and regulates the genome in very precise ways during development."
In this study, Professor Mattick and Dr Guy Barry from the Institute for Molecular Bioscience at the University of QLD, found that when they activated specific neurons, the level of long non-coding RNA, known as Gomafu, inside the cell dropped dramatically, which signalled to other parts of the cell to perform specific functions.
After a few hours, the Gomafu levels return to normal, ready to be activated again.
But in the post-mortem brains of people with schizophrenia, the researchers found abnormally low levels of Gomafu.
While other experiments with colleagues from Johns Hopkins University showed these non-coding genes could form strong bonds with other proteins inside the neuron that have previously been implicated in schizophrenia.
"You can imagine that if the schizophrenic brain is firing differently – and Gomafu levels are consistently lower – it would cause havoc within the cell, with all sorts of genes and proteins free floating and available to act, where in a normal brain they would be tethered to Gomafu."
The team, whose findings were published in the journal Molecular Psychiatry, plan to study these process in more detail in human cells.
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[Below, landmarks of vision by Mattick are highlighted over the past decade. Mattick, upon receiving a Prize, gave an Interview with excerpts below. He emphasized that both ENCODE 2007 and ENCODE 2012 handled "Central Dogma" as a "tabu subject", though precisely the reversal of the Dogma ("uncontestable truth" :-) "permitted" the class of algorithms, see The Principle of Recursive Genome Function by Pellionisz 2008- AJP]
HUGO Matters
Interview with John Mattick
MARCH 21, 2013
Q: The activity shown in the non-coding regions of the genome from last year’s ENCODE papers seemed to catch the mainstream media’s attention, but it must have been old news for you. How did you start looking into non-coding RNA and what did you think of the new data?
A: It’s been obvious for 35 years now that most of the genome is transcribed to RNA. That brings up two possibilities—the non-coding transcriptions are either junk, meaning that the genome is full of rubbish, or that another type of information is being put into the system. The second possibility struck me as much more interesting than the assumption that it’s all junk, and it became clear as crystal to me as more data came in over time that it’s much more likely to be functional than not.
The ENCODE project looked at things that have been on the table for years, but it’s nice to get some extra detail. Unfortunately, many still seem to cling to the notion that most genome biology in humans is driven by proteins. ENCODE is curiously silent about the implications of the massive transcription of RNA and the signatures of functional organization across these non-coding regions, preferring perhaps to duck the question of whether it is all relevant or largely “transcriptional” noise.
The intellectual and cultural problem is that if this non-coding RNA is functional—and all the emerging evidence points in this direction—the entire conception of gene regulation has to be reconstructed. The field has assumed for a long time that protein regulators, transcription factors of various sorts, drive the regulation of the system. But now we have to figure massive amounts of regulatory RNA into our understanding. Transcription factors are very powerful stage-specific effectors of gene expression, but my feeling is that much more information is required to supervise architectural organization—the shapes and positions of different muscles, bone and organs.
Q: Architecturally? You mean a larger regulatory framework?
A: Yes, people haven’t really considered whether additional information is needed for developmental architecture, and how the transcription factors might be integrated into this larger narrative.
The genome has an outpouring of RNA during development, with over 90 percent of the genome differentially transcribed in different cells at different stages. The major function of these transcripts appears to be to orchestrate the superstructure of the genome in a very precise way, by directing the site-specificity of the epigenetic complexes that modify the DNA and the proteins around which it is wrapped—an extraordinarily complex secondary code. Exploring that is a journey we’ll have to go on to understand development.
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[Almost immediately after ENCODE 2012, Mattick wrote about "Rocking the Foundations"..]:
Rocking the foundations of molecular genetics
John S. Mattick
Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
.. the finding of extraordinarily dynamic noncoding transcription in complex organisms suggests that the long held idea that gene expression is primarily controlled by combinatoric interactions between cis-acting transcription factors and their cognate binding sites is also incorrect, but rather that RNA may be the computational engine of the evolution and ontogeny of developmentally complex and cognitively advanced organisms
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[Mattick laid down in his landmark 2004 Scientific American paper his thesis that the non-coding RNA as a genome regulatory system of differentiated multicellular organisms must be a quantum leap beyond genome regulation in prokaryotes]:
The Hidden Genetic Program of Complex Organisms
John Mattick
Scientific American, 2004
...Pioneering biologist Jacques Monod summarized the universality of the central dogma as “What was true for E. coli would be true for the elephant.”
Monod was only partly right. A growing library of results reveals that the central dogma is woefully incomplete for describing the molecular biology of eukaryotes. Proteins do play a role in the regulation of eukaryotic gene expression, yet a hidden, parallel regulatory system consisting of RNA that acts directly on DNA, RNAs and proteins is also at work. This overlooked RNA-signaling network may be what allows humans, for example, to achieve structural complexity far beyond anything seen in the unicellular world.
...Throughout evolution, therefore, the complexity of prokaryotes may have been limited by genetic regulatory overhead, rather than by environmental or biochemical factors as has been commonly assumed. This conclusion is also consistent with the fact that life on earth consisted solely of microorganisms for most of its history. Combinatorics of protein interactions could not, by themselves, lift that complexity ceiling.
Eukaryotes must have found a solution to this problem [The dual (covariant and contravariant) valences of RNA system, and the RNA system "acting as a genomic ancestor of cerebellar neural networks - Pellionisz et al, 2013 provides the mathematical apparatus to formulate the vision of the RNA system yielding coordinated regulation, along with Eigenstates of genome regulation (evident also by RNA interference) in a software-enabling manner - AJP]. Logic and the available evidence suggest that the rise of multicellular organisms over the past billion years was a consequence of the transition to a new control architecture based largely on endogenous digital RNA signals. It would certainly help explain the phenomenon of the Cambrian explosion about 525 million years ago, when invertebrate animals of jaw-dropping diversity evolved, seemingly abruptly, from much simpler life. Indeed, these results suggest a general [mathematical, within that, metrical and fractal geometrical - AJP] rule with relevance beyond biology: organized complexity is a function of regulatory information - and, in virtually all systems, as observed by Marie E. Csete, now at Emory University School of Medicine, and John C. Doyle of the California Institute of Technology, explosions in complexity occur as a result of advanced controls and embedded networking.
The implications of this rule are staggering.We may have totally misunderstood the nature of the genomic programming and the basis of variations in traits among individuals and species. The rule implies that the greater portion of the genomes in complex organisms is not junk at all - rather it is functional and subject to evolutionary selection.
--
[Mattick is an outstanding experimenter with an exceptional vision. He is a leader, not necessarily because he might have said it sooner than others that "Junk" was a misnomer. (The first appears to be Boyer, see facsimile, who in the next second after Ohno argued in 1972 that "Junk" was in the human genome "for the importance of doing nothing", immediately proclaimed Ohno's -mistaken- argument "suspect" - and the ignorant misnomer gained ground not for reasons of good science but for the simplistic convenience of - temporarily(?) - disregarding the "non-genic" 98.7% of human genome.)
The brilliance of Mattick is also evident in his determination - having won a "case of vintage champagne" over the percentage of non-coding that is demonstrably "functional". Now he considers, as everybody should sooner or later, the entire "numbers' game" good for an occasional betting, but irrelevant. Instead, towering over most, actually shows WHAT kind of "genome regulation" the "non-coding RNA" might be doing, opening the door to software-enabling unifying solutions, suitable for industrialization of genomics. After all, DNA transcription is very energy-consuming (in each and every cell of a differentiated organism), and not only "natural selection" would eliminate "junk" if it would be there "for the purpose of doing nothing" as Ohno erred, but the transcription of DNA into RNA, if it is not "coding" would be a waste of energy that organisms would could use more purposefully. - AJP]
Metaphores of Fractal Dynamics to Multi-dimensional Systems (Public domain hints)
This columnist (AJP) is on record since priority date of Aug. 1st, 2002 that the genome (not just its "genic" parts) is fractal and growth of fractal organisms is regulated by a multivariate fractal recursive iteration [the decade of fractogene]. FractoGene theory and thus utility have been built ever since based on mathematical rigor, "fractal genome governs growth of fractal organisms".
As glanced in the above "timeline", the Intellectual Property of FractoGene, flying into the face of the two dogmas (Junk and Genes plus Central Misunderstanding), was too early for publication at the emergence of concept and its utility (2002) thus was filed to USPTO. After slightly over a decade, the parent-patent is now available by appointment for consultations possibly leading to licensing of FractoGene (improvements on "best methods" of utility after submission of application are not revealed).
Now, after a decade of a tumultuous paradigm-shift, the author realizes even more acutely than at the time of "Eureka-moment" (2002), how frightening it could be to introduce the "double heresy of FractoGene". The author expresses anew that scientific progress over patently obsolete dogmas was not intended as an offense against anyone. It was intended as an effort to help those hundreds of millions of people suffering from genome regulation diseases (with cancer is the second and perhaps most dreadful killer, and schizophrenia, autism, auto-immune diseases and other genome (mis)regulation syndromes are enormously challenging). Also, it was to help develop a new generation of "antibiotics" and defense-agents by shutting down harmful extrinsic genomes, with a mathematical understanding of genome regulation permitting functioning and non-threatening (synthetic) genomes, among others to unleash innovative solution for energy, agriculture. With genome sequencing having become a commodity (with a growing glut that threatens to suffocate the ecosystem of unfolding industrialized genomics), software-enabling mathematical understanding of "genome regulation" is seen as an intellectual/entrepreneurial/social challenge calling for cohesive collective breakthrough unprecedented in history (perhaps with the exception of quantum physics leading to nuclear age).
For the author's "fair share" invested over the decade in human life and own resources, IP beyond the issued patent can not be freely disclosed. However, to provide ALREADY PUBLIC DOMAIN "hints" to metaphorically illustrate how fractals (not unlike the "Cambrian explosion") escalate in complexity when one proceeds from the "linear integers" (Fibonacci numbers) to "2D plane of real- and imaginary numbers" (Mandelbrot set), and to the "3D analogue of Mandelbrot set, Mandelbulb", see the following public domain YouTube/Wikipedia materials, and consult publications of the Geometrical Unification of Genomics and Neuroscience, and Industrialization of Genomics, with numerous independent "proof of concept" experimental results referenced, how "fractal defects" cause misregulation of genome function; making clinical applications e.g. in cancer therapy available.
Readers may wish to view a truly spectacular 54-minute NOVA special on fractals first. Not only to learn more about fractals, but perhaps to arrive at a similar impression of my "Eureka-moment" on FractoGene, 8,280,641 ("My God! Of course!") - though a key stipulation for any US patent is the "non-obviousness" (prior to the "how come I did not think of this before?" realization).
For a step-by-step approach, a first and simplest specific metaphorical illustration shows that a self-sustained recursive algorithm can "regulate" by a single number an easily defined stream of positive integers (called Fibonacci numbers). The algorithm F(n)=F(n-1)+F(n-2) is utterly simple. Starting from 0 and 1, let the next marked number be the sum of the previous two. Thus, after 0 and 1, the next number will be 1 (again), and likewise by the same recursive iteration we'll mark along the way 2, 3, 5, 8 and so on. The marked integers are known in Western cultures as the Fibonacci-series (India knew all this in ancient sanskrit prosody ages before, in ancient times). What is so "regulated" about the (marked) Fibonacci-series? It is most beautifully stable, since after rapid convergence of the iterative recursion you can even forget the very simple "rule" ("add the previous two numbers"). Suffice to know ANY Fibonacci-number, you can independently generate the next one - just multiply the given number by a constant (called Golden Ratio, PHI=1.6218...). The ratio is so stable that after the first dozen iterations it zooms to a 4 decimal digit precision.
As the above montage from the public domain YouTube shows (the long presentation by Keith Devlin is introduced by Jim Simons, see lower left) that a single number ("Golden Ratio") can "regulate" not just a series of numbers, but also can "optimize" e.g. fractals; such that the branchlets optimally cover, without overlap, a surface. This can help illuminate the utility of FractoGene; since e.g. a "wrong number" for the governing ratio would result in pathology of e.g. a neural dendritic tree.
Let's go from the line of integers to the 2-dimensional plane, to arrive at the best known and most beloved fractal, the "Mandelbrot set". Again, the algorithm just could not be simpler; Z(n)=Z(n-1)^2 +C. (To generate a new number, the previous should be squared, adding a C constant, see a still picture from the (NOVA special) public domain YouTube. (where C is a constant, but the non-mathematically minded viewer may gloss over the subtlety that Z is a complex number, thus the zillions of subsequent points are on the 2D plane erected by the two axes of real numbers and imaginary numbers):
[View NOVA Special on fractals, also explaining the Mandelbrot set here].
We all have seen (or can view in the NOVA special YouTube) the mind-boggling "dynamics" of "self-similar repetitions" occurring at any resolution of "zoom". If one encounters such an "incredibly complex-looking dynamism" may really wonder "can we ever solve what makes it so miraculous?". ("Complexity is the mind of the bewildered" - the Mandelbrot set arises from a simple algorithm; "There is nothing simpler than a problem solved", said Faraday - AJP]
Now let us leap one step further. What could be the analogue of a 2D Mandelbrot set in THREE DIMENSIONS?? Is the unfolding 3D time-process even more mind-boggling?? YOU BET!
[View the "Animated Mandelbulb" vimeo in this blog].
Above is a still-frame from the Public Domain YouTube of the "Mandelbulb" , see a good description what it is, in this public domain explanation. Yes, looking at the "uncontrolled" burgeoning three-dimensional "structure" one can be outright frightened how "similar" it looks - to an "uncontrolled growth called cancer"!
For those who think we can win WWII (the second war against cancer, after Nixon's first hundred billions of dollars thrown at the menace) one tends to agree with David Haussler's 4-minute video (with transcript) : "It’s not comprehensible by an unaided human mind, but it would be with the power of a computer-aided analysis".
Computer power is plentiful, but computers do not understand "knowledge"; can only be programmed by algorithms. - Consult Pellionisz, by appointment; holgentech_at_gmail_dot_com or Four-Zero-Eight 891-7187.
Francois Jacob, Nobelist with Monod and Lwoff in 1965 for Operon gene regulation dies at 92
New York Times
By WILLIAM YARDLEY
Published: April 25, 2013
François Jacob, left, and Jacques Monod in 1971. They helped discover how genes are regulated.
Dr. François Jacob, a French war hero whose combat wounds forced him to change his career paths from surgeon to scientist, a pursuit that led to a Nobel Prize in 1965 for his role in discovering how genes are regulated, died on April 19 in Paris. He was 92.
The French government announced his death.
Dr. Jacob said he had been watching a dull movie with his wife, Lysiane, in 1958 when he began daydreaming and was struck with an idea of how genes might function. “I think I’ve just thought up something important,” he told her [See excerpts below from his Biography on his Nobel "Eureka Moment" - AJP].
Seven years later, Dr. Jacob shared the Nobel Prize in Physiology or Medicine with Dr. Jacques Monod and Dr. André Lwoff, his colleagues at the Pasteur Institute in Paris, for their discovery that cells can switch on and switch off certain genetic information. Their work, which focused on bacteria, increased understanding of how genes could be selectively deployed by an organism. “They’re all there in the egg. But how does the egg know when to turn from one type of cell type to another?” Richard Burian, a professor emeritus of philosophy and science studies at Virginia Tech, said of the question asked by Dr. Jacob and his colleagues. “There must be some kind of signal.”
Their discovery, considered central to the development of molecular biology, offered new insight into how people inherit traits, how they grow and develop, and how they contract and fight diseases.
“The discoveries have given a strong impetus to research in all domains of biology with far-reaching effects spreading out like ripples in the water,” Sven Gard, a member of the Nobel Committee for Physiology or Medicine, said when the three men were awarded the prize, according to the Nobel Web site. “Now that we know the nature of such mechanisms, we have the possibility of learning to master them.”
François Jacob was born on June 17, 1920, in Nancy, France. He had begun studying medicine when World War II began. France was occupied by Nazi Germany’s forces in 1940, and Dr. Jacob, whose grandfather had been a four-star general in the French Army, fled to England by boat in 1940 and joined the Free French Army led by Charles de Gaulle.
He worked as a medical officer and fought with Allied forces in North Africa and in France, where he was seriously wounded in a German air raid. He received numerous high military honors, including the Cross of War and the Cross of the Liberation.
Dr. Jacob returned to medical school after the war, completing his studies in 1947, but damage to his hands from his combat wounds prevented him from becoming a surgeon. At a loss for what career to pursue, he was encouraged to try research and, though he had little training in it, he found a place at the Pasteur Institute in 1950. (He earned a doctorate in science at the Sorbonne in 1954.)
Working with other scientists at Pasteur, he quickly distinguished himself by identifying how bacteria adapt to drugs and bacterial viruses. It was a time of great discoveries in genetics. In 1953, James D. Watson and Francis Crick published their groundbreaking work on the double helix structure of DNA. At the Pasteur Institute, Dr. Jacob began working with Dr. Monod, and they soon had a breakthrough of their own. By means of a series of innovative experiments, they established that the transfer of genetic information could be controlled through two different types of genes, regulatory genes and structural genes, with the former controlling the expression of the latter.
“What mattered more than the answers were the questions and how they were formulated,” Dr. Jacob later wrote. “For in the best of cases, the answer led to more questions. It was a system for concocting expectation, a machine for making the future. For me, this world of questions and the provisional, this chase after an answer that was always put off to the next day, all that was euphoric. I lived in the future.”
...
Dr. Jacob became laboratory director at the Pasteur Institute in 1956 and four years later was appointed head of its new department of cell genetics. In 1964, he joined the Collège de France, where a chair of cell genetics was created for him.
Dr. Jacob married Lysiane Bloch, known as Lise, a pianist, in 1947. They had four children. After her death, he married Geneviève Barrier in 1999. Information about survivors was unavailable.
Dr. Jacob’s inquiries included matters moral and philosophical as well as cellular. He once wrote that he wanted to discover “the core of life.”
“What intrigues me in my life is: How did I come to be what I am?” he wrote in his 1988 autobiography, “The Statue Within.” “How did this person develop, this I whom I rediscover each morning and to whom I must accommodate myself to the end?"
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[In his biography "The Statue Within", Francois Jacob recalls his immediate perception of Crick's "Central Dogma" pp. 287-288 - AJP]:
"...the star at this London colloquium [p.286; "Society for Experimental Biology London in September 1957 - AJP] was the talk by Francis Crick. We had come across the name of Francis Crick in the notes in Nature on the structure of DNA. At the time no one in the attic had ever heard of him. Everyone, on the other hand, knew Jim Watson and his uncommon personality. So Crick had seemed like some sort of residual appendage to Watson. Some months later, however, when Crick made an appearance at the Pasteur Institute to give a seminar, it was immediately clear to all that Francis was not simply Jim's appendage; that a character as strong-minded, as mentally acute had no need of a coach. Tall, florid, with long sideburns, Crick looked like the Englishman seen in illustrations to nineteenth-century books about Phileas Fogg or the English opium eater. He talked incessantly, with evident pleasure and volubly, as if he was afraid he would not have enough time to be sure it was understood. Breaking up his sentences with loud laughter. Setting off again with renewed vigor at a speed I often had trouble keeping up with. ... He had no taste for experimentation, for manipulation. But no one contributed more than he to the working out of the body of hypotheses that in the 1990s and 1660s guided experiments and made it possible to foresee the general outlines of what was to come. .. For example, the so-called sequence hypothesis, according to which the sequence of the bases in a segment of nucleic acid suffices to define the sequence of amino acids in the corresponding protein. And then, it took a sure footing, an acute sense of publicity, to baptize Central Dogma - that is to say, incontestable truth - a hypothesis that was unsupported by any serious argument, but that, restricting the limits of the possible, sharpened the field of research. According to the Central Dogma, the information defining the sequence could go from nucleic acids to protein, but never in the reverse direction. Once in the protein, it could never get back out: neither back to the nucleic acids nor to other proteins. If, then, it was necessary to find the details of the machine that enabled the nucleic sequence to translate into a protein sequence, it was pointless to look for another machine for a reverse translation. Such a machine did not exist. To go straight to the point, not to worry about details, at least to begin with; such struck me as the lesson to be drawn from Francis's talk."
[Dr. Jacob also recalls in his Biography his "Nobel Eureka-moment", p. 297 -AJP]
"Late July 1958. A Sunday in Paris. The children have gone off on vacation. Lise and I have stayed at home. She is at the piano in the next room, practicing a sonata. For my part, I am trying to get started on a lecture that I must give in New York... With no desire to write this lecture, I go round in my study, chewing over vague hypotheses, possible experiments. At the end of the afternoon, fed up, weary, we decide to go to the movies. A film of no interest. Slumped in my seat, I dimly perceive in myself associations that continue to form, ideas for proceeding. .. I am invaded by a sudden excitement mingled with a vague pleasure. It isolates me from the theater, from my neighbors whose eyes are riveted to the screen. An suddenly a flash. The astonishment of the obvious. How could I not have thought it sooner? Both experiments - that of conjugation done with Elie on the phage, erotic induction; and that done with Pardee and Monod on the lactose system, the PA JA MA - are the same! Same situation. Same result. Same conclusion. In both cases, a gene governs the formation of a cytoplasmic product, of a repressor blocking the expression of other genes and so preventing either the synthesis of the galactosidase or the multiplication of the virus. In both cases, one induces by inactivating the repressor, either by lactose or by ultraviolet rays. The very mechanism that must be the bases of the regulation."
Comment by Philip Ball
Nature, 25 APRIL 2013
VOL 496 | NATURE | 419
On the 60th anniversary of the double helix, we should admit that we don’t fully understand how evolution works at the molecular level, suggests Philip Ball.
This week’s diamond jubilee of the discovery of DNA’s molecular structure rightly celebrates how Francis Crick, James Watson and their collaborators launched the ‘genomic age’ by revealing how hereditary information is encoded in the double helix. Yet the conventional narrative - in which their 1953 Nature paper led inexorably to the Human Genome Project and the dawn of personalized medicine - is as misleading as the popular narrative of gene function itself, in which the DNA sequence is translated into proteins and ultimately into an organism’s observable characteristics, or phenotype.
Sixty years on, the very definition of ‘gene’ is hotly debated. We do not know what most of our DNA does, nor how, or to what extent it governs traits. In other words, we do not fully understand how evolution works at the molecular level.
That sounds to me like an extraordinarily exciting state of affairs, comparable perhaps to the disruptive discovery in cosmology in 1998 that the expansion of the Universe is accelerating rather than decelerating, as astronomers had believed since the late 1920s. Yet, while specialists debate what the latest findings mean, the rhetoric of popular discussions of DNA, genomics and evolution remains largely unchanged, and the public continues to be fed assurances that DNA is as solipsistic a blueprint as ever. [Not totally - Google Tech Talk 2008 was rather forthright, with 15,571 views to date, The Principle of Recursive Genome Function 2008 peer-reviewed science paper was downloaded by tens of thousands, with "nolo contendere" - AJP)]
The more complex picture now emerging raises difficult questions that this outsider knows he can barely discern. But I can tell that the usual tidy tale of how ‘DNA makes RNA makes protein’ is sanitized to the point of distortion. Instead of occasional, muted confessions from genomics boosters and popularizers of evolution that the story has turned out to be a little more complex, there should be a bolder admission - indeed a celebration - of the known unknowns.
DNA DISPUTE
A student referring to textbook discussions of genetics and evolution could be forgiven for thinking that the ‘central dogma’ devised by Crick and others in the 1960s - in which information flows in a linear, traceable fashion from DNA sequence to messenger RNA to protein, to manifest finally as phenotype - remains the solid foundation of the genomic revolution. In fact, it is beginning to look more like a casualty of it.
Although it remains beyond serious doubt that Darwinian natural selection drives much, perhaps most, evolutionary change, it is often unclear at which phenotypic level selection operates, and particularly how it plays out at the molecular level.
Take the Encyclopedia of DNA Elements (ENCODE) project, a public research consortium launched by the US National Human Genome Research Institute in Bethesda, Maryland. Starting in 2003, ENCODE researchers set out to map which parts of human chromosomes are transcribed, how transcription is regulated and how the process is affected by the way the DNA is packaged in the cell nucleus. Last year, the group revealed1 that there is much more to genome function than is encompassed in the roughly 1% of our DNA that contains some 20,000 protein-coding genes - challenging the old idea that much of the genome is junk. At least 80% of the genome is transcribed into RNA.
Some geneticists and evolutionary biologists say that all this extra transcription may simply be noise, irrelevant to function and evolution2. But, drawing on the fact that regulatory roles have been pinned to some of the non-coding RNA transcripts discovered in pilot projects, the ENCODE team argues that at least some of this transcription could provide a reservoir of molecules with regulatory functions - in other words, a pool of potentially ‘useful’ variation. ENCODE researchers even propose, to the consternation of some, that the transcript should be considered the basic unit of inheritance, with ‘gene’ denoting not a piece of DNA but a higher-order concept pertaining to all the transcripts that contribute to a given phenotypic trait3.
According to evolutionary biologist Patrick Phillips at the University of Oregon in Eugene, projects such as ENCODE are showing scientists that they don’t really understand how genotypes map to phenotypes, or how exactly evolutionary forces shape any given genome.
COMPLEX CODE
The ENCODE findings join several other discoveries in unsettling old assumptions. For example, epigenetic molecular alterations to DNA, such as the addition of a methyl group, can affect the activity of genes without altering their nucleotide sequences. Many of these regulatory chemical markers are inherited, including some that govern susceptibility to diabetes and cardiovascular disease4. Genes can also be regulated by the spatial organization of the chromosomes, in turn affected by epigenetic markers. Although such effects have long been known, their prevalence may be much greater than previously thought .
Another source of ambiguity in the genotype-phenotype relationship comes from the way in which many genes operate in complex networks. For example, many differently structured gene networks might result in the same trait or phenotype6. Also, new phenotypes that are viable and potentially superior may be more likely to emerge through tweaks to regulatory networks than through more risky alterations to protein-coding sequences7. In a sense this is still natural selection pulling out the best from a bunch of random mutations, but not at the level of the DNA sequence itself.
One consequence of this complex genotype-phenotype relationship is that it may impose constraints on natural selection. If the same phenotypes can result from many similarly structured gene networks, it might take a long time for a ‘fitter’ phenotype to arise. Alternatively, mutations may accumulate, free from selective‘weeding’,thanks to the robustness of networks in maintaining a particular phenotype. Such hidden variation might be unmasked by some new environmental stress, enabling fresh adaptations to emerge. These sorts of constraints and opportunities are poorly understood; evolutionary theory does not help biologists to predict what kinds of genetic network they should expect to see in any one context.
Researchers are also still not agreed on whether natural selection is the dominant driver of genetic change at the molecular level. Evolutionary geneticist Michael Lynch of Indiana University Bloomington has shown through modelling that random genetic drift can play a major part in the evolution of genomic features, for example the scattering of non-coding sections, called introns, through protein-coding sequences. He has also shown that rather than enhancing fitness, natural selection can generate a redundant accumulation of molecular ‘defences’, such as systems that detect folding problems in proteins10. At best, this is burdensome. At worst, it can be catastrophic.
In short, the current picture of how and where evolution operates, and how this shapes genomes, is something of a mess. That should not be a criticism, but rather a vote of confidence in the healthy, dynamic state of molecular and evolutionary biology.
A PROBLEM SHARED
Barely a whisper of this vibrant debate reaches the public [just click on Google Tech Talk YouTube - AJP]. Take evolutionary biologist Richard Dawkins’ description in Prospect magazine last year of the gene as a replicator with “its own unique status as a unit of Darwinian selection”. It conjures up the decades-old picture of a little, autonomous stretch of DNA intent on getting itself copied, with no hint that selection operates at all levels of the biological hierarchy, including at the supraorganismal level2, or that the very idea of ‘gene’ has become problematic.
Why this apparent reluctance to acknowledge the complexity? One roadblock may be sentimentality. Biology is so complicated that it may be deeply painful for some to relinquish the promise of an elegant core mechanism. In cosmology, a single, shattering fact (the Universe’s accelerating expansion) cleanly rewrote the narrative. But in molecular evolution, old arguments, for instance about the importance of natural selection and random drift in driving genetic change, are now colliding with questions about non-coding RNA, epigenetics and genomic network theory. It is not yet clear which new story to tell. [For Informatics experts, some of it is extremely clear. "Exons" of human DNA amount to information to specify a stamp-size jpg; plainly and painfully clear that more genomic information is needed to specify a human being. Also, all IT-savvy experts agree that "Complex System Theory" is called for. However, few identified that the mathematics of the complex system at hand is FRACTALS - AJP]
Then there is the discomfort of all this uncertainty following the rhetoric surrounding the Human Genome Project, which seemed to promise, among other things, ‘the instructions to make a human’. It is one thing to revise our ideas about the cosmos, another to admit that we are not as close to understanding ourselves as we thought.
There may also be anxiety that admitting any uncertainty about the mechanisms of evolution will be exploited by those who seek to undermine it. Certainly, popular accounts of epigenetics and the ENCODE results have been much more coy about the evolutionary implications than the developmental ones. But we are grown-up enough to be told about the doubts, debates and discussions that are leaving the putative ‘age of the genome’ with more questions than answers. Tidying up the story bowdlerizes the science and creates straw men for its detractors. Simplistic portrayals of evolution encourage equally simplistic demolitions.
When the structure of DNA was first deduced, it seemed to supply the final part of a beautiful puzzle, the solution for which began with Charles Darwin and Gregor Mendel. The simplicity of that picture has proved too alluring. For the jubilee, we should do DNA a favour and lift some of the awesome responsibility for life’s complexity from its shoulders.■
Philip Ball is a freelance science writer based in London. e-mail: p.ball_at_btinternet_dot_com
HUGO 2013, Part 1: What does it all mean?
Illumina Blog [on HUGO 2013 in Singapore]
Posted by Kahlil Lawless on Thu, Apr 18, 2013
After several days of fascinating and varied talks by a multitude of talented researchers at the Human Genome Meeting/International Congress of Genetics, my head is swimming - and I doubt that I’m the only one. Midway through the conference, we celebrated ten years since the Human Genome Project was officially completed. At the time, this milestone was heralded as the dawn of the genomic era where researchers and clinicians would be able to leverage this new level of understanding to produce incredible discoveries, and deliver significant benefits to the world. Ten years on and the discoveries have been incredible, but once again we appear to have underestimated the phenomenal complexity of biological systems and our ability to understand and manipulate them. Our fancy new tools and databases have unleashed upon us a tsunami of new information, and the question on everyone’s lips is - what does it all mean?
Sample Size of One
The first setback was coming to the realisation of how limited a resource our Human Genome reference sequence actually is, and the emphasis here is on the singular - it is one genome, compiled from several different individuals. The diversity of Earth’s 7 billion humans is not represented in this database, and the more distantly related a person is from this reference, the less useful it becomes. This problem has spawned duplicate genome projects the world over. In our region, the Pan-Asian Population Genomics Initiative (PAPGI) has tackled this issue with gusto, with collaborators from the middle east to south east Asia and everywhere in between have been generating vast datasets and public resources that will become critical reference and research tools1.
Dotting i’s and Crossing t’s
A second potential source of concern is that the human genome reference, even after 19 consecutive revisions, has proved a moving target. Each time it gets better, but still errors remain. Stephan Schuster’s presentation on the RP11 genome assembly showed more than 80 new regions not found in the reference, some containing whole genes.
We Are Not Alone...
One of the hottest emerging fields is metagenomics, as evidenced by a standing room only crowd at the HUGO session to hear Martin Hibberd proclaim that the “future is biomes”. He demonstrated a strong link between diseases such as Age-Related Macular Degeneration and eye flora, while others such as Kyu Young Song from Korea set themselves on a course to understanding the role of microbial genomes in complex diseases such as Inflammatory Bowel Syndrome (which I don’t need to see any more photos of - thank you very much).
DNA is History
Literally. The history of an organism, or even of a single cell, is indelibly inked on its DNA. Julian Parkhill from the Sanger Institute presented his sequencing results on cholera strains for the last 100 years. He was able to show that rather than independent environmental events leading to regional epidemics, virtually all strains could be traced back to strains emerging from south Asia. This analysis showed conclusively that the cholera outbreak in Haiti was not the result of earthquake-damaged sanitation systems, but rather the arrival of Nepalese peacekeepers stationed upstream of Haiti’s main river. He also demonstrated this on a smaller scale tracing transmissions of methicillin-resistant Staph aureus (MRSA) in a hospital outbreak in the UK2. On a smaller scale, the mutational variants that make such analysis possible can also be tracked within an individual over time.
Patrick Tan used comprehensive understanding of the rates and types of somatic mutation to plot the accumulation of variation within a single lifespan, and to pinpoint sudden mutational events that are significant contributors to cancer. Through studying mutation types and frequencies, they were even able to elucidate which mutagens individuals had been exposed to, and determine if these exposures were the causative agent in particular cancers. Needless to say, from here I’m now going to studiously avoid Chinese herbal remedies that may contain wild ginger, as aristolochic acid appears to confer a cancer risk equivalent of smoking ten packs a day...
One Man’s Junk is Another’s Treasure
Geneticists have been eating their words ever since the day when 99% of the genome was proclaimed to be ‘junk’ DNA. This should have become apparent the moment we found out the human genome only had 20,000 genes, far less than anyone expected. Since then, researchers have identified GWAS loci where there were no genes at all, alternative splicing, antisense, small and long non-coding RNA, DNA methylation, a multitude of chromatin regulation mechanisms, DNA-DNA interactions….the list goes on. With the benefit of hindsight, we now say “it’s not the number of genes, it’s what you do with them”, and the study of gene regulation has risen to the fore.
As John Mattick said in his talk on long ncRNA and their role in disease, “the human genome is a zip-file extraordinaire”. Decoding this is proving much more challenging than sequencing the DNA itself. Regulation is dynamic, responsive, and often unpredictable. Due to perennial changing state of organisms, and the myriad of regulatory mechanisms in play, applying a reductionist scientific approach to regulation has proved a significant challenge.
Understanding these systems requires cross-disciplinary approaches, and at HUGO this year it wasn’t uncommon to see a talk employ everything from DNA-Seq, RNA-Seq, ChIP-Seq, proteomics, microscopy, genetic engineering and everything in between to gain an understanding of a single mechanism. Joseph Takahashi’s 20-year journey to understand mammalian cellular circadian rhythms illlustrates the intense cross-disciplinary paths that lead to success.
So, Why do We Care?
At some stage, we all hope this understanding can be applied in some useful manner (at least that’s what the funding bodies are hoping, otherwise I’m sure none of the presented work would ever have been done). That was the other half of HUGO this year, and comprised many success stories from the world of applied genomics, and how the tsunami of data can be ridden successfully into clinic beach while avoiding the submerged rocks of misdiagnosis and treatment. Stay tuned for my post on HUGO Part 2 – What is it all for?
Ranganathan S, Tongsima S, Chan J, Tan TW, and C Schönbach (2012) Advances in translational bioinformatics and population genomics in the Asia-Pacific BMC Genomics 13 (Suppl 7)S1.
Harris SR, Cartwright E, Tӧrӧk ME, Holden M, Brown N, et al. (2013) Whole-genome sequencing for analysis of an outbreak of methicillin-resistant Staphylococcus aureus: a descriptive study. Lancet Infectious Dis. 13(2) 130–136.
Image courtesy of US Department of Energy Genome Program’s Biological and Environmental Research Information System
Comments
"Geneticists have been eating their words ever since the day when 99% of the genome was proclaimed to be ‘junk’ DNA". Actually, at the minute Ohno finished his "scientific conclusion" (1972) the very first question seriously doubted the validity of his "argument" that non-genic DNA can not be of any use. (See facsimile at http://www.junkdna.com). Likewise, when Crick laud mouthed his "Central Dogma", Jacques Monod (see his autobiography) immediately discredited (laughed) his "Dogma" (his Operon, Nobel in 1965 was not followed-up for many decades). The problem is, that some of us put genomics on mathematically sound new foundation - see e.g. The Principle of Recursive Genome Function (2008), submitted almost immediately after the 2007 ENCODE admission. However, though nobody could say a word against it, there are precious few, who actually WANT to follow-through with disruptive events. (For 7 years, nobody cited "The Double Helix" - and though genome function is without the slightest doubt is recursive, perhaps a dozen workers cited over the last five years the principle itself. Now, there are over 30 independent experimental papers to show that cancers, autism, schizophrenia, etc. massively rearrange the genome that has been proven fractal, billions of dollars on "cancer therapy" are reluctant to acknowledge the breakthrough in the fear of what the paradigm-shift may harbor for some. Meanwhile, billions are wasted, and hundreds of millions die of miserable deaths. Something is not fair. - Pellionisz
Posted @ Thursday, April 18, 2013 12:12 PM by Andras J. Pellionisz
Can Cancer Cells Solve the Puzzle of Junk DNA?
Matt Ridley
The Wall Street Journal
April 14, 2013
The usually placid world of molecular biology has been riven with two fierce disputes recently. Although apparently separate, the two conflagrations are converging.
The first row concerns the phrase "junk DNA." Coined in 1972 by the geneticist Susumu Ohno, it is an attempt to explain why vast stretches of animal genomes, far more in some species than in others, seem to serve no purpose. Genes of all kinds and their control sequences make up maybe 9% of the human genome at the very most. The rest may be nonfunctional "junk," mainly there because it is good at getting itself duplicated. Yet the phrase has always caused a surprising amount of offense. Reports of the discrediting of junk-DNA theory have been frequent [This is the easy part - see this column since 2004 - AJP].
Why does it matter? [This is eminently doable, as well - requires a twist, however - AJP]. Partly because scientists want to know if they are right to focus on part of the genome, ignoring the rest, but mainly because the issue tests an evolutionary theory about how DNA sequences can proliferate even if they do not benefit the body.
Late last year, a huge team of scientists running a consortium called Encode published an analysis of the human genome that they said showed some kind of activity in 80% of the genome. They later conceded that perhaps 20% is actually functional, yet insisted the phrase "junk DNA" could now be "totally expunged from the lexicon."
According to Dan Graur of the University of Houston and his colleagues, even this is a wild overestimate—not least because it uses a "causal role" definition of function that is all wrong, as if you were to describe among the heart's functions adding 10.5 ounces to the weight of the body, along with pumping blood. After a few exchanges, the Encode team leader Ewan Birney conceded that in hindsight, the team overstated its conclusions. But he added that whatever the interpretation, the Encode data are sound.
Are they? Here's where the junk-DNA row meets the other conflagration in molecular biology. All the Encode data were derived from cancer-cell lines. [See comment below! - AJP]. To describe human cancer cells as having the human genome looks increasingly unwise. Most cancer cells have extra chromosomes, fragmented and rearranged DNA and unusual patterns of gene activity.
As if to illustrate the point, last month a consortium of scientists based in Heidelberg, Germany, analyzed and sequenced the genome of one type of HeLa cell, an immortal laboratory cell line widely used since 1952. They described a genome that looks like a bomb has gone off in it. There are three copies of most chromosomes, yet only one copy of many genes. Hefty chunks have been reshuffled to other chromosomes, and some chromosomes have suffered from "chromothripsis," which one of the scientists describes as being "blown apart and stuck back together in a random order." A person with this genome could never be born.
Yet—and here's the source of the controversy—the HeLa cell line was derived from the tumor that killed a poor, black tobacco farmer named Henrietta Lacks in Baltimore in 1951. As Rebecca Skloot has documented in a remarkable best seller ("The Immortal Life of Henrietta Lacks"), the medical community never got her consent and treated her family with tactless disrespect for years—until Ms. Skloot's book began to make a difference.
Not enough of a difference, apparently. The German team did not seek the consent of the Lacks family before publishing the HeLa sequence, claiming it revealed nothing specific about Ms. Lacks's own genome. "Your claim is so wrong that I don't know where to start," replied one geneticist. The sequence has since been unpublished.
So here's the paradox: A cancer genome like HeLa may not be sufficiently representative of human genomes to resolve the junk DNA question, but may still give away private information about the human being from whom it derived.
[The towering genome publicist Matt Ridley could not be reached to creatively resolve what he believes as a paradox of ENCODE. Their 2012 report stated "We integrate results from diverse experiments within cell types, related experiments involving 147 different cell types" (see list online for ENCODE CELL TYPES ) . Many, but certainly not "all" genomes investigated by ENCODE were cancerous. Therefore, since after the 2007 and now 2012 ENCODE conclusions many workers focus on the best continuation, this columnist submits that MATHEMATICAL (see article below...) comparison of the "Junk" of control versus "Junk" of cancerous cells is rather revealing. A couple of dozen such efforts are referenced in this recent review; moreover how The Principle of Recursive Genome Function might be violated by "fractal defects" is explained. Thus, perhaps a better title would have been "How 'Junk DNA' Solves the Puzzle of Cancer" - Pellionisz]
Editorial by Edison T. Liu
Science 29 March 2013:
Vol. 339 no. 6127 p. 1493
DOI: 10.1126/science.1237835
One of the greatest challenges in the study of cancer has been confronting the mindset that the disease is too complex to address effectively: too many tissue types, too many etiologies, too much genetic chaos. [While the ordinary meaning of "chaos" is "disorder", in the mathematical sense of nonlinear dynamics "chaos" and "fractal" can be a strictly deterministic or probabilistic, albeit not trivial ORDER - AJP]. In the 1970s, the oncogene concept provided temporary relief from this angst, because it was thought that a limited number of genes (proto-oncogenes), when activated through mutations, might turn a normal cell into a cancerous one. But what briefly seemed tidy quickly became messy again with the discovery of other classes of genes that normally protect against cancer. Most recently, advanced genome sequencing technologies have revealed a surprising fact: Every tumor contains hundreds to thousands of mutations, most of which affect only a small percentage of the cancers in any tumor type (see the Review by B. Vogelstein et al., p. 1546). In addition, the high degree of cancer cell heterogeneity in each tumor suggests that we are studying a moving target that can readily dodge treatments through continual mutation. And the genetic uniqueness of every cancer raises the question of whether standardized cancer treatment protocols can ever achieve broad efficacy.
The good news is that we are now armed with dramatically more information and substantially more powerful tools to grapple with cancer's complicated nature. By necessity, past research focused on one gene at a time, and so our mindsets were necessarily restricted. Now that we can access the complete, precise genomic information about any cancer, future advances will depend on exploiting the natural genetic complexity of this disease. This will require much more than annotating lists of mutations. What is needed is the ability to detect all of the relevant components of a system and describe the complexity observed in a mathematical manner so that models can be computed. We then need to reconstruct this complexity in experimental systems and perturb those systems to test their characteristics. Ultimately, this could reveal higher-order rules that explain the possible actions of each particular cancer in terms of its gene networks.
Going forward, a transition to a systems dynamics view requires a different empiricism. A major change will be viewing each type of cancer as an experimental system by itself. Rather than analyzing many thousands of tumors somewhat superficially, we may need very deep analyses of a few well-characterized tumors, using multiple approaches for which diverse data sets can be integrated (complete DNA and RNA sequences, whole-genome epigenetic information, etc.). The tumors to be analyzed can be selected on the basis of their exceptional differences in behavior, such as sensitivity versus resistance to chemotherapy, and aggressiveness versus latency. The analyses required to validate the behavior of that cancer could be aided by using genetically engineered mouse models and patient-derived xenografts. The goal would be to generate a “whole-system” understanding of each cancer and provide a more nuanced approach to developing therapies.
But perhaps this view of each type of cancer as a unified but static unit is too simplistic. Instead, each cancer could be considered an evolutionary experiment involving a genetically plastic population of cells undergoing selection within a tumor. The genomic composition of single cells in an individual cancer before and after treatment may best uncover the genetic fluxes that lead to therapeutic resistance. Thus, a small group of individuals entering a “proof-of-concept” study could have their tumors genomically analyzed, the tumors' therapeutic sensitivity tested in patient-derived xenografts, and the in vivo tumor clonal response to a tailored anticancer treatment monitored by detailed analyses of circulating tumor DNA.
Which experimental approach will be the best for advancing understanding of cancer biology and improving clinical outcomes is, of course, debatable. But we are very fortunate to be in a position today to test each of the many possibilities.
GPU Advances Genomic Science - A Possible Nobel Prize?
3/25/2013 by: Gil Russell
Lieberman Aiden’s research utilizes a branch of mathematics first described by David Hilbert in 1891 – Hilbert space-filling curve is a continuous fractal space-filling curve (now called Hilbert’s curve). Lieberman Aidan has added the third dimension and applied a function, which computes the most likely fold combination on the genome of a given chromosomal ordering...
...Another discovery occurred when the results of assembling the genomes by computation revealed that they naturally formed into compartmentalized departments known as “fractal globules” versus “equilibrium globules”, which was formerly the accepted theory. Lieberman Aidan’s theory advances this idea further with the discovery of the “fractal globule” which unfolds without knotting, remains organized on unfolding and follows a -1 power rule...
What we discovered is two really interesting facts:
One, is that as genomes specialize in function they’re actually folding. As cells specialize, the genomes inside them are actually folding into new configurations that enable that cell to be in one state versus another.
And, (two) we also got a sense of how folds like the fractal globule can enable the genome to keep this unbelievably long stretch of information fully accessible at all times whenever it is needed.
...learn a lot more about how genomes fold and unravel a lot more of this mystery of what is going on when a cell specializes and how does that go wrong in a disease like cancer.
...Simply put, it takes 6 to 7 years to develop and test a complex algorithm before deciding whether it is even marketable
...A complete understanding of cancer and its underlying causes now does not seem to be quite so far away as it did just a week ago. In fact, we are siding on the optimistic side that it could come a lot sooner..
[Some readers may wonder if GPU technology, or Hilbert (1891) might be nominated by this optimistic piece of journalism for a Nobel. Regrettably, neither is likely to pass the scrutiny of Stockholm, as there is no category for "technology" and scientists must be alive. Breakthroughs towards understanding genome (mis)regulation (e.g. in cancers), however, are highly likely to be peppered by Nobels all along (how many was received for developing quantum physics, when the splitting of the atom flew in the face of the central dogma that "atom" is the smallest unit that "can not split"?). Prize and/or Progress, another point readers may wonder about why all (coding or not :-) bits of the "long stretch of [DNA] information" should be "fully accessible at all times whenever it is needed"? A dedicated copy of the manuscript of The Principle of Recursive Genome Function" (2008, elaborating the FractoGene concept and utility of algorithm about 6 years after its priority date of 2002) was put in the hand of Dr. Lander in the fall of 2007 (UCSF lecture). With ENCODE 2007 just weeks ago concluded, the classic dogmas no longer held - recursive (fractal) algorithms were no longer a "lucid heresy". In the same week ENCODE 2012 concluded once more (with 350+ top scientists as co-authors in 30 leading articles), the FractoGene patent-utility was issued. Having tested the core algorithms for so many years, implementation in hospital setting in the WWII against cancers is an opportunity explored as we speak - Pellionisz, holgentech_at_gmail_dot_com]
Complete Genomics acquired by BGI of China on Sequoia Capital in Silicon Valley, USA
Dream Driven: Exclusive Interview with Dr. Jun Wang, Executive Director of BGI
ChinaBio Today
publication date: Mar 19, 2013
author/source: Richard Daverman, PhD|
ChinaBio® Today recently had the chance to interview Dr. Jun Wang, a co-founder of China’s sequencing giant, BGI-Shenzhen. Dr. Wang was named Executive Director of the company at the young age of 32. The company works with many of the big pharma and has established several major international partnerships. The driving force behind these relationships, according to Dr. Wang, is always the science.
“We are a dream-driven organization,” he says. The dream is the promise of genomic science to improve human life, and BGI seems willing to enter any partnership that will advance genomic science, even if it doesn’t necessarily represent a profit opportunity.
In 2012, Dr. Wang was named one of “Nature’s 10: Ten People Who Mattered this Year,” an international group of scientists selected by Nature who are making a major impact in life science.
BGI got its start in 1999, when it participated in the Human Genome Project, contributing 1% of the work. At the time, it was known as Beijing Genomics Institute. Since then it has moved its headquarters to Shenzhen and become known as BGI. It has also grown into the world’s largest sequencing company, with many “firsts” in its short 13-year history.
One of these is the acquisition of Complete Genomics, the first acquisition of a public US company by a Chinese firm to date, and an indication of the increasing globalization of the Chinese life science industry. The acquisition was finalized on March 18, 2013.
A special thanks to Dr. Samantha Du, Managing Director of Sequoia Capital China, for helping arrange the interview. Sequoia recently participated in BGI’s $222 million capital round in September, 2012, which helped fund BGI’s acquisition U.S. Complete Genomics.
ChinaBio: Dr. Wang, I read your biography and was very impressed by your background. You have accomplished a great deal at BGI in a very short period of time. How did you get started with BGI?
Dr. Jun Wang: I joined the team before BGI started. I was the one doing the computing work at the institute, taking care of data analysis, hardware and software and that sort of thing.
ChinaBio: You were heading up the bioinformatics side of things initially. Your role has expanded pretty significantly since then.
Dr. Jun Wang: At that time, BGI was basically in Beijing and it had a subsidiary in Hangzhou. I was head of the Hangzhou center for one year and then headed the Beijing office for another two or three years. I became the Executive Director of BGI in 2008.
ChinaBio: How would you say BGI has changed your life?
Dr. Jun Wang: In my bachelor’s work, I was working on artificial intelligence. That also is a multi-disciplinary field that is based on computing science, biology and mathematics. At the time, the human genome project was an interesting niche in my career. We didn’t know the future when we started. We formed BGI by ourselves and were going to try to do 1% of the project. After we finished the project, a lot of people asked us what would be next step.
We are the true believers, believing that genomics leads to a new future, a new world, that genomics will change a lot of things. We have to prove that. We have to prove that genomics will be useful. We want to do something that is good for society. We know genomics technology will change the world, but we have to prove it. We want ordinary people to have the services and products that come from genomics knowledge.
ChinaBio: You do whole genomes of individuals, but aren’t you focused on doing much deeper science?
Dr. Jun Wang: We are doing prenatal testing for Down’s syndrome and deafness, for example. So people can take advantage of BGI now. There is a big difference biotech and IT. They both want to change world. But biotech must know the world before it can change the world. It is important to bring the understanding of the human genome down to DNA level or molecular level. Based on that, you can apply the findings to ordinary life. We know the gene and phenotype relationship of Down’s syndrome. We can test maternal blood for that, test the genes and offer the value of genomic knowledge. This is just one example of what can be done. We know the biology and so we can make a difference in ordinary people’s lives.
ChinaBio: When I was looking back at BGI’s accomplishments over the past thirteen years, there is quite a list, starting with the 1% of the human genome, and now you have over 3,000 employees in China and the US.
Dr. Jun Wang: We have close to 5,000 employees now.
ChinaBio: And looking at your list of locations, you have many locations in China, including Beijing, Shanghai, Hangzhou, Wuhan, Shenzhen, plus Boston and other locations in the US.
Dr. Jun Wang: Our headquarters is in Shenzhen. Our international headquarters is in Hong Kong, but those two campuses [Shenzhen and Hong Kong] are just a half hour’s driving apart. We have lots of centers in China. We actually cover most major cities in China. We have two labs in the US: the east coast is Children’s Hospital of Philadelphia, and west coast is UC-Davis. We also have a lab in Copenhagen.
ChinaBio: How have you been able to grow so quickly? Is there a specific business model behind that that has allowed you to grow?
Dr. Jun Wang: First of all, we are not a profit-driven institution. We have efficient needs to set up a lab or office where we will do work. At these locations, people have research needs or healthcare needs – they need us to work with them and to develop something useful. In Copenhagen, we have several big projects with Danish researchers, so we set up a lab. In Philadelphia, the Children’s Hospital of Philadelphia wanted more clinical services for newborn babies in the future and it wanted to do research on children’s disorders, so we needed a lab there.
So basically, we are not profit-driven, but goal-driven or dream-driven. We have a dream together. We have projects together and we are going to work together, so we need a lab. In most cases, we send back samples to Hong Kong. Then they can perform the experiments. That is also a model. We don’t really care what kind of model we are using, but we care about getting the job done most efficiently.
ChinaBio: I like the expression dream-driven. It’s a great way of describing what you are doing. One of the reasons you’ve been able to accomplish all this in such a short time would seem to be because of your government support. The China government has given, or committed, billions to BGI, which makes it easier to do the work. Now you have raised $222 million from VCs, including Sequoia Capital China, and we know Sequoia well enough to know they are going to expect some return on their investment. Are you going to have to change how you operate in the future because of this? Will you have to be profit-driven as opposed to purely dream-driven?
Dr. Jun Wang: First of all, I have to clarify. We didn’t really receive billions of dollars from the government. We have received loans from commercial banks in China. They don’t give the money for free; we have to pay them back. We are not a government-funded institution, but a private one. We do also receive research grants from the central government. They are competitive grants. In the China system, people could get them, if they are competitive enough. And we are getting research grants from our research entities. But again, that doesn’t mean we are government-funded institute.
ChinaBio: You’re right. If you look at what’s been published, it seems like BGI is dependent on government support.
Dr. Jun Wang: We are one of the few institutions in China research that [is making a profit and thus] also pays tax. The after-tax profits are used to fund research. We are putting hundreds of millions of dollars into research. We could become rich. But we don’t think that way. We invest all the money back into research because we want to do good things for society. We have bigger dreams. So to clarify, in regards to the VCs like Sequoia, the reason we are doing this fund raising is we needed the money to acquire a US public company called Complete Genomics.
There are different parts to BGI. We have research parts like BGI Research, BGI Tech Service, BGI Healthcare, BGI Agriculture to do molecular breeding, and we recently started an Environmental Protection division to reduce carbon emissions, lower water waste and other environmental work.
ChinaBio: You mentioned Copenhagen University, Children’s Hospital of Philadelphia and UC-Davis. You have been very active with cross-border partnerships with other research institutes and hospitals, such as Johns Hopkins, University of Edinburgh, and Autism Speaks, among others. These are probably dream-driven projects, I guess, and these bring in scientific value, but do they also bring profits to BGI?
Dr. Jun Wang: Lots of them are pure scientific collaborations. Genomics is international. We can’t do it all. We need all kinds of expertise all over the world to do the job. Doing scientific research has no national borders; scientists can talk freely anywhere. So we talk to each other freely. If we have a common goal or dream, we can pool all our research together to do a great project together.
ChinaBio: With Children’s Hospital of Philadelphia, where you are working to develop a more effective therapy of sub-types of pediatric brain tumors. Will you be focusing more on rare disease and individual medicine? Is this a corporate strategy to move toward rare diseases?
Dr. Jun Wang: Very rare tumors are often inherited, and they’re often easier to detect compared to other tumors. BGI has different groups working on different projects, but we don’t want to emphasize one area over another.
ChinaBio: What about the data that comes out of all this research? You have amassed a tremendous amount of data over the years. Is that something you plan to leverage going forward and maybe develop new diagnostic tests or identifying maybe other applications for new drugs?
Dr. Jun Wang: Lots of the data have been published. All of the research is publicly available. Sometimes we work with private partners, whose data must be kept private. We do that on a case-by-case basis.
ChinaBio: So, in some cases, you are building a proprietary database from the data?
Dr. Jun Wang: We are project based. We are trying to organize all of the data that BGI is developing into a central database. BGI has just announced a journal called GigaScience. The journal is coupled with a freely available database so we are trying to organize all of BGI’s data into that database.
ChinaBio: And that will be available for no cost?
Dr. Jun Wang: Yes, it will be freely available. There is also a category for projects where companies don’t want to release the data. So we respect that.
ChinaBio: You have relationships with corporate entities like Merck and Novo Nordisk and others, where that would apply?
Dr. Jun Wang: Yes, it also happens in some academic relationships with professors who want to keep their research private. We also respect that. We are flexible. But for ourselves, we want to share as much data as possible because the whole genomics world needs to be shared. People have to work together on the data, mine it, and develop something good.
ChinaBio: Is this a correct to say that BGI generates data and makes it widely available to others, but does not necessarily mine it or extract the next level of value from the data?
Dr. Jun Wang: No. We absolutely will mine data. We absolutely seek to extract as much knowledge by ourselves as possible. But sometimes you can’t do it alone because your capacity or your ability is limited. We need help from others, and sometimes you want to put the data on the web to benefit society. But we will absolutely do the annotation and the mining by ourselves. Also, if you want to publish a paper, you need to mine the data. You can’t just publish the data, you need to tell the story of the data.
ChinaBio: From a business perspective, what is the value you are generating for BGI or your market, such as the maternal genetic tests you mentioned, from the data?
Dr. Jun Wang: So for a given cancer tumor, we get sequencing down to a single cell. We try to figure out the driving genes of the tumor and then discover drug targets for the tumor. In the future, we will be able to offer personalized cancer services based on that.
Another thing we are doing is associating dietary studies with different diseases, such as diabetes, so we are looking at nutritional patterns with these diseases to see how nutrition affects the metabolic process. There are a lot of scientific stories. All the data has stories behind it. And eventually those scientific stories will converge into medications in the market.
ChinaBio: Getting back to your corporate relationships with big pharmas like Merck and Novo Nordisk, is that what you are doing with them, identifying opportunities for new drug development?
Dr. Jun Wang: These are strategic partnerships. We are trying to develop something useful for the next generation of pharmaceutical solutions.
ChinaBio: Can you be more specific? Are these relationships focusing on personalized medicine, for example?
Dr. Jun Wang: It’s really all related to how to use new kinds of drug solutions based on the genomic data that has been discovered.
ChinaBio: With respect to the acquisition of Compete Genomics, the company was supposed to be a profitable company, but they didn’t make it. The two benefits of the acquisition would seem to be their presence in the US and CG’s technology. Is that right?
Dr. Jun Wang: We already have a presence in the US, so we didn’t need to buy CG for that. It was mostly the technology and their R&D [capabilities]. But, once again, it is because we share the same dream. They want to sequence one million human genomes. And we want to do that too. And we believe that with the technology and BGI’s downstream capability, we can work together for that.
ChinaBio: You have developed a very impressive array of data that spans agricultural, plants and microbes. In the future, will you focus on human genomic questions or what area do you see as future direction?
Dr. Jun Wang: We think the other areas are also important and we have subsidiaries for each, though we expect healthcare will be important and also agriculture.
ChinaBio: In your early years at BGI, how did you initiate the contact with the human genome project and how did you convince them that you were capable? After all, you were the only participant from a developing country.
Dr. Jun Wang: Maynard Olson at the University of Washington was one of the people behind the human genome project, and two co-founders of BGI graduated from there. That was an easy go for BGI to be part of the Human Genome Project. It was important for the project to be international. The idea was “We do it together, we share it together.” Everybody owns the human genome. Everybody shares the outcome of the human genome. Everybody sequenced the human genome together.
ChinaBio: The whole world of genomics is evolving very rapidly. Where do you see BGI in five or ten years?
Dr. Jun Wang: This is a long journey for BGI. We don’t have a very specific definition for what we should achieve in five or ten years. We want to develop something good for society. We want to use our genomics technology to do the job. We will continue to work in all genomics areas and develop as many products as possible.
But we do have some specific goals for healthcare, agriculture, BGI Research and BGI Tech in mind. We will continue on this very long journey. We are dream-driven and dreams are not that easy to make true.
ChinaBio: What about you personally – do you have goals for yourself in five or ten years?
Dr. Jun Wang: I don’t really separate my personal ego from BGI’s goals. I have to think of the two together.
ChinaBio: Dr. Du, let me direct the final question to you. Dr. Wang has a very long-term perspective and, as he says, is dream driven. But VCs have limited life for their funds. What is Sequoia expecting from BGI in terms of return?
Dr. Samantha Du: That’s a billion dollar question. We share their dream. We think the company has a lot of promise and a big future, and we will be very patient in working with them, not only for the dream but the return for the investors.
Disclosure: none.
[There is a complex set of issues, thus this columnist will not publicly comment on the interview. Andras Pellionisz, inventor & developer of the FractoGene Patent & Trade Secrets Portfolio can be consulted at HolGenTech_at_gmail_dot_com]
A Genetic Code for Genius? In China, a research project aims to find the roots of intelligence in our DNA; searching for the supersmart.
Wall Street Journal
February 15, 2013
By GAUTAM NAIK
At a former paper-printing factory in Hong Kong, a 20-year-old wunderkind named Zhao Bowen has embarked on a challenging and potentially controversial quest: uncovering the genetics of intelligence.
Mr. Zhao is a high-school dropout who has been described as China's Bill Gates. He oversees the cognitive genomics lab at BGI, a private company that is partly funded by the Chinese government.
At the Hong Kong facility, more than 100 powerful gene-sequencing machines are deciphering about 2,200 DNA samples, reading off their 3.2 billion chemical base pairs one letter at a time. These are no ordinary DNA samples. Most come from some of America's brightest people—extreme outliers in the intelligence sweepstakes.
The majority of the DNA samples come from people with IQs of 160 or higher. By comparison, average IQ in any population is set at 100. The average Nobel laureate registers at around 145. Only one in every 30,000 people is as smart as most of the participants in the Hong Kong project—and finding them was a quest of its own.
"People have chosen to ignore the genetics of intelligence for a long time," said Mr. Zhao, who hopes to publish his team's initial findings this summer. "People believe it's a controversial topic, especially in the West. That's not the case in China," where IQ studies are regarded more as a scientific challenge and therefore are easier to fund.
The roots of intelligence are a mystery. Studies show that at least half of the variation in intelligence quotient, or IQ, is inherited. But while scientists have identified some genes that can significantly lower IQ—in people afflicted with mental retardation, for example—truly important genes that affect normal IQ variation have yet to be pinned down.The Hong Kong researchers hope to crack the problem by comparing the genomes of super-high-IQ individuals with the genomes of people drawn from the general population. By studying the variation in the two groups, they hope to isolate some of the hereditary factors behind IQ.
Their conclusions could lay the groundwork for a genetic test to predict a person's inherited cognitive ability. Such a tool could be useful, but it also might be divisive.
"If you can identify kids who are going to have trouble learning, you can intervene" early on in their lives, through special schooling or other programs, says Robert Plomin, a professor of behavioral genetics at King's College, London, who is involved in the BGI project.
But critics worry that genetic data related to IQ could easily be misconstrued—or misused. Research into the science of intelligence has been used in the past "to target particular racial groups or individuals and delegitimize them," said Jeremy Gruber, president of the Council for Responsible Genetics, a watchdog group based in Cambridge, Mass. "I'd be very concerned that the reductionist and deterministic trends that still are very much present in the world of genetics would come to the fore in a project like this."
Mr. Zhao is a phenomenon in his own right. In addition to his genetics wizardry, he says his near-fluent English is self-taught. His career as a geneticist began quite humbly—with the cucumber. In 2007, he skipped afternoon classes at his school in Beijing and started an internship at the Chinese Academy of Agricultural Sciences.
He cleaned test tubes and did other simple jobs. In return, the graduate students let him borrow genetics textbooks and participate in experiments, including the sequencing of the cucumber genome. Mr. Zhao was 15 years old; when the study of the cucumber genome was published in Nature Genetics in 2009, he was listed as a co-author.
Tantalized by genomics, Mr. Zhao quit school and began to work full-time at BGI, one of the biggest genomics research centers in the world. It is based in the mainland city of Shenzhen, near Hong Kong. The following year, BGI founded a cognitive genomics unit and named Mr. Zhao as its director.
Mr. Zhao's first foray into the genetics of intelligence was a plan to collect DNA from high-achieving kids at local high schools. It didn't work.
"Parents were afraid [of giving consent] because their children's blood would be taken," says Mr. Zhao. Blood samples are the most efficient way to collect DNA samples.
In the spring of 2010, a theoretical physicist called Stephen Hsu from the University of Oregon visited BGI. Dr. Hsu was also interested in the genetics of cognitive ability, so the pair joined with other colleagues to launch the BGI intelligence project.
One part of the plan called for shifting to saliva-based DNA samples obtained from mathematically gifted people, including Chinese who had participated in mathematics or science Olympiad training camps.
Another involved the collection of DNA samples from high-IQ individuals from the U.S. and other countries, including those with extremely high SAT scores, and those with a doctorate in physics or math from an elite university. In addition, anyone could enroll via BGI's website if they met the criteria.
The Shenzhen government agreed to pay for half the project, and BGI said it would pitch in the other half, says Mr. Zhao.
Most of the samples so far have come from outside of China. The main source is Dr. Plomin of King's College, who for his own research had collected DNA samples from about 1,600 individuals whose IQs were off the charts. Those samples were obtained through a U.S. project known as the Study of Mathematically Precocious Youth, now in its fourth decade.
Dr. Plomin tracked down 1,600 adults who had enrolled as kids in the U.S. project, now based at Vanderbilt University. Their DNA contributions make up the bulk of the BGI samples.
Dr. Hsu embarked on his own marketing drive. When giving science talks at various institutions, including the California Institute of Technology, Taiwan's Academy of Science and Google, GOOG +0.62%he exhorted listeners to sign up for the study.
BGI's website has so far attracted about 500 qualifying volunteers.
The scientific challenge is significant. Consider the genetics of height, which, like intelligence, is a complex trait governed by many different genes, each one with a tiny influence.
Attempts to find height-related genes didn't yield any reliable hits until the number of DNA samples exceeded 10,000. By studying more and more samples, scientists have now identified about 1,000 genetic variations that partly explain why some people are taller than others. Those results are replicable—and they hold true whether a person is from Iceland or Japan.
By comparison, one of the biggest genomic investigations of IQ attempted so far involves only about 5,000 people drawn from the general population. Scientist say that tens of thousands of regular people would have to be studied just to find the first useful IQ gene.
That's where BGI's genomic deep dive comes in. The team will compare the genomes of 2,200 high-IQ individuals with the genomes of several thousand people drawn randomly from the general population. Because most of the supersmart participants being studied are the cognitive equivalent of people "who are 6-foot-9-inches tall," says Dr. Hsu, it should be much easier to identify many key IQ-related factors in their genomes. (Dr. Hsu is now vice president for research and graduate studies at Michigan State University.)
"The genetic basis of intelligence has been ignored for a very long time," says Mr. Zhao. "Our data will be ready in three months' time."
["This kind of research is impossible in the USA" - so would judge some from the forced resignation of Jim Watson as Director of Cold Spring Harbor Laboratories for a causal comment on the perceived IQ of some ethnic groups. Kind of not true. While the SNP-based study by California-based direct-to-customer genome testing company 23andMe can only be compared to the above Chinese full genome study as "apples to oranges", 23andMe runs a test relating to "the ability to learn from mistakes" (based on a single-point mutation Rs1800497). Though the at least partly Chinese government-run study uses the genome of - presently - mostly non-Chinese individuals, one can assume that an ulterior motif of China is to make their population (yes, they can even forcefully select) more intelligent. There seems to be nothing wrong with the goal - though the Western Civilization and law rejects any assumed means of "forceful selection" - in case of humans. (Enforced genomic selection e.g. for higher agricultural yield - see Monsanto - is already a huge business in the USA and Worldwide). The "controversy" reported here is a very strong reminder of the advantages of genomics in China - even if no "forceful selection" would ever take place even there. Let's assume that from the "initial mostly non-Chinese" sample is substituted by an entirely Chinese population-sample. In that case, nobody could even raise the "R-world", since the 9 leading tribes of China are essentially the same race. On the positive side, the homogeneity will certainly accelerate their probing into what they say "we are interested in the diseases of the Chinese people". At the same time, China (led by a monolithic government) is keenly aware of the genomic vulnerability of a homogeneous population - and not only from the viewpoint of an IQ. It does not call for a very high IQ to realize the consequences. - Pellionisz, at hologenomics_at_gmail_dot_com]
(Feb 21) Breakthrough Prize on YouTube (and everywhere... spilling to BRIC)
The Silicon Valley Industrialists, reigniting the culture of science, will have a global impact - see article by Antonio Regalado from Brazil
Breakthrough Prize in Life Sciences ($3 M each, for 5 scientists per year - list of first 11)
February 20, 2013 4:48 AM - General- Awards- Biotechnology- Medical/ Pharmaceuticals
Art Levinson, Sergey Brin and Anne Wojcicki, Mark Zuckerberg and Priscilla Chan, and Yuri Milner Announce the Breakthrough Prize in Life Sciences. 11 Inaugural winners receive US$3 million each for Groundbreaking Achievements in Life Science Research
SAN FRANCISCO, Feb. 20, 2013 /CNW/ - Art Levinson, Sergey Brin, Anne Wojcicki, Mark Zuckerberg, Priscilla Chan and Yuri Milner announced today the launch of the Breakthrough Prize in Life Sciences ("Breakthrough Prize"), recognizing excellence in research aimed at curing intractable diseases and extending human life. The prize will be administered by the Breakthrough Prize in Life Sciences Foundation, a not-for-profit corporation ("Foundation") dedicated to advancing breakthrough research, celebrating scientists and generating excitement about the pursuit of science as a career.
The first 11 recipients of the Breakthrough Prize are:
•Cornelia I. Bargmann
•David Botstein
•Lewis C. Cantley
•Hans Clevers
•Napoleone Ferrara
•Titia de Lange
•Eric S. Lander
•Charles L. Sawyers
•Bert Vogelstein
•Robert A. Weinberg
•Shinya Yamanaka
All prize winners have agreed to serve on the Selection Committee of the Foundation to choose recipients of future prizes.
Founding sponsors of the Breakthrough Prize include Sergey Brin and Anne Wojcicki, Mark Zuckerberg and Priscilla Chan, and Yuri Milner, who collectively have agreed to establish 5 annual prizes, US$3 million each, going forward.
Art Levinson, Chairman of the Board of Apple and Chairman and former CEO of Genentech, will serve as the Chairman of the Board of the Foundation, while additional directors will include Anne Wojcicki, Mark Zuckerberg and Yuri Milner.
"I am delighted to announce the launch of the Breakthrough Prize in Life Sciences and welcome its first recipients," said Art Levinson. "I believe this new prize will shine a light on the extraordinary achievements of the outstanding minds in the field of life sciences, enhance medical innovation, and ultimately become a platform for recognizing future discoveries. I also want to thank our founding sponsors, Sergey Brin, Anne Wojcicki, Mark Zuckerberg, Priscilla Chan and Yuri Milner. Without their contribution, this prize would not have been possible."
"We are thrilled to support scientists who think big, take risks and have made a significant impact on our lives. These scientists should be household names and heros in society," said Anne Wojcicki.
"Curing a disease should be worth more than a touchdown," said Sergey Brin.
"Priscilla and I are honored to be part of this," said Mark Zuckerberg. "We believe the Breakthrough Prize in Life Sciences has the potential to provide a platform for other models of philanthropy, so people everywhere have an opportunity at a better future."
"Solving the enormous complexity of human diseases calls for a much bigger effort compared to fundamental physics and therefore requires multiple sponsors to reward outstanding achievements," said Yuri Milner.
Going forward, each year's prize winners will join the Selection Committee for future awardees. One of the distinguishing characteristics of the Breakthrough Prize will be a transparent selection process, in which anyone will be able to nominate a candidate online for consideration. Also, the prize can be shared between any number of deserving scientists and can be received more than once. In addition, there are no age restrictions for nominees.
All Breakthrough Prize recipients will be invited to present public talks targeting a general audience. These lectures, together with supporting materials, will be made available to the public, allowing everyone to keep abreast of the latest developments in life sciences, guided by contemporary masters of the field.
About the Breakthrough Prize Foundation:
The Breakthrough Prize in Life Sciences Foundation is a not-for-profit corporation dedicated to advancing breakthrough research in life sciences, celebrating scientists and generating excitement about the pursuit of science as a career. Additional information about the Foundation and the 2013 recipients of the prizes can be found at http://www.breakthroughprizeinlifesciences.org.
About the prize winners:
Cornelia I. Bargmann
Torsten N. Wiesel Professor and Head of the Lulu and Anthony Wang Laboratory of Neural Circuits and Behavior at the Rockefeller University. Howard Hughes Medical Institute Investigator.
For the genetics of neural circuits and behavior, and synaptic guidepost molecules.
David Botstein
Director of the Lewis-Sigler Institute for Integrative Genomics and the Anthony B. Evnin Professor of Genomics at Princeton University.
For linkage mapping of Mendelian disease in humans using DNA polymorphisms.
Lewis C. Cantley
Margaret and Herman Sokol Professor and Director of the Cancer Center at Weill Cornell Medical College and New York-Presbyterian Hospital.
For the discovery of PI 3-Kinase and its role in cancer metabolism.
Hans Clevers
Professor of Molecular Genetics at Hubrecht Institute.
For describing the role of Wnt signaling in tissue stem cells and cancer.
Titia de Lange
Leon Hess Professor, Head of the Laboratory of Cell Biology and Genetics, and Director of the Anderson Center for Cancer Research at the Rockefeller University.
For research on telomeres, illuminating how they protect chromosome ends and their role in genome instability in cancer.
Napoleone Ferrara
Distinguished Professor of Pathology and Senior Deputy Director for Basic Sciences at Moores Cancer Center at the University of California, San Diego.
For discoveries in the mechanisms of angiogenesis that led to therapies for cancer and eye diseases.
Eric S. Lander
President and Founding Director of the Eli and Edythe L. Broad Institute of Harvard and MIT. Professor of Biology at MIT. Professor of Systems Biology at Harvard Medical School.
For the discovery of general principles for identifying human disease genes, and enabling their application to medicine through the creation and analysis of genetic, physical and sequence maps of the human genome
Charles L. Sawyers
Chair, Human Oncology and Pathogenesis Program at Memorial Sloan-Kettering Cancer Center. Howard Hughes Medical Institute Investigator.
For cancer genes and targeted therapy.
Bert Vogelstein
Director of the Ludwig Center and Clayton Professor of Oncology and Pathology at the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Howard Hughes Medical Institute Investigator.
For cancer genomics and tumor suppressor genes.
Robert A. Weinberg
Daniel K. Ludwig Professor for Cancer Research at MIT and Director of the MIT/Ludwig Center for Molecular Oncology. Member, Whitehead Institute for Biomedical Research.
For characterization of human cancer genes.
Shinya Yamanaka
Director of Center for iPS Cell Research and Application, Kyoto University
Senior Investigator, Gladstone Institutes, San Francisco
For induced pluripotent stem cells.
About the participants:
Art Levinson
Arthur D. Levinson is chairman of Genentech, Inc. and a member of the Roche Board of Directors. He has been chairman of Genentech since 1999, and he served as chief executive officer of Genentech from 1995 to 2009. Levinson joined Genentech in 1980 as a research scientist and became vice president, Research Technology in 1989; vice president, Research in 1990; senior vice president, Research in 1992; and senior vice president, Research and Development in 1993.
Art was appointed Chairman of the Board of Apple in November 2011. He had served as a co-lead director of Apple's board since 2005 and a director since 2000. He is Chairman of the Board of Amyris and a director of NGM Biopharmaceuticals, Inc. and the Broad Institute of MIT and Harvard. He was a director of Google, Inc. from 2004 to 2009. He currently serves on the Board of Scientific Consultants of the Memorial Sloan-Kettering Cancer Center, the Industrial Advisory Board of the California Institute for Quantitative Biomedical Research, the Advisory Council for the Princeton University Department of Molecular Biology and the Advisory Council for the Lewis-Sigler Institute for Integrative Genomics.
Art has authored or co-authored more than 80 scientific articles and has been a named inventor on 11 United States patents. Art received his Bachelor of Science degree from the University of Washington and earned a doctorate in Biochemical Sciences from Princeton University.
Mark Zuckerberg
Mark Zuckerberg is the founder chairman and CEO of Facebook, which he founded in 2004 in his college dorm room.
Mark is responsible for setting the overall direction and product strategy for Facebook. He leads the design of Facebook's service and the development of its core technology and infrastructure.
Mark studied computer science at Harvard University before moving the company to Palo Alto, California. In September 2010, Mark donated $100 million to the Newark Public School System to help renovate and revamp the system.
Sergey Brin
Sergey Brin, a native of Moscow, received a Bachelor of Science degree with honors in mathematics and computer science from the University of Maryland at College Park. He is currently on leave from the Ph.D. program in computer science at Stanford University, where he received his master's degree. Sergey is a recipient of a National Science Foundation Graduate Fellowship as well as an honorary MBA from Instituto de Empresa. At Stanford, he met Larry Page and worked on the project that became Google. Together they founded Google Inc. in 1998, and Sergey continues to share responsibility for day-to-day operations with Larry Page and Eric Schmidt.
Sergey's research interests include search engines, information extraction from unstructured sources, and data mining of large text collections and scientific data. He has published more than a dozen academic papers, including Dynamic Data Mining: A New Architecture for Data with High Dimensionality, which he published with Larry Page.
Sergey has been a featured speaker at several international academic, business and technology forums, including the World Economic Forum and the Technology, Entertainment and Design Conference.
Anne Wojcicki
Anne Wojcicki is Co-Founder of 23andMe, a privately held personal genetics company that helps individuals understand their own genetic information through DNA analysis technologies and Web-based interactive tools. By encouraging individuals to access and learn about their own genetic information, 23andMe aims to create a common, standardized resource that has the potential to accelerate drug discovery and bring personalized medicine to the public. Anne has an extensive background in health-care investing, focused primarily on biotechnology companies. She received a bachelor's degree in biology from Yale University.
Yuri Milner
Yuri founded Mail.ru Group in 1999. Under his leadership, Mail.ru Group became the leading European Internet company. Yuri took that business public in 2010, stepping down from his role of Chairman at the beginning of 2012 to focus his efforts on global Internet investments. DST Global, a family of funds investing in Internet companies, was established in 2009 and is one of the largest Internet investors in the world.
Yuri graduated from Moscow State University in 1985 with an advanced degree in theoretical physics and subsequently conducted research at the Institute of Physics at the Russian Academy of Sciences. In 2012 he launched the Fundamental Physics Prize Foundation, a not-for-profit corporation dedicated to advancing knowledge of the Universe at the deepest level by awarding annual prizes for scientific breakthroughs, as well as communicating the excitement of fundamental physics to the public.
SOURCE: Milner Foundation
For further information:
For additional inquiries, contact: Media@breakthroughprizeinlifesciences.org / +1-(415)-671-7676
[A multiple breakthrough. First, the acknowledgement that genomics ("Life Sciences") needs "breakthroughs" - just as Thomas Khun predicted in his bestseller "The Structure of Scientific Revolutions" - closes a chapter (the Decade of Genomic Uncertainty, when The Human Genome Program opened the Pandorra box, and it was found by Encode 2007 and finally by Encode 2012 that the earlier axioms are obsolete). Second, this Prize is created by the families of spectacularly successful INDUSTRIALISTS, who fully understand that without software-enabling algorithms any progress is seriously hindered. Third, there may be an apparent realization that both Internet-search and Social Media, fueling hypergrowth of industries, may have peaked - with the Next Big Thing in genome informatics. Last, but not least, not one of the creators of this Prize relate to the classic culture of science that holds "Curing a disease should be worth more [for a properly appreciative society] than e.g. a touchdown". - Pellionisz, holgentech_at_gmail_dot_com]
Young Chinese Scientists will Map any Genome
Bloomberg Businessweek
By Lauren Hilgers on February 07, 2013
When the workday ends at BGI’s factory in Shenzhen, the headquarters of the largest genome mapping company in the world, it’s like a bell has gone off at math camp. The company’s scientists and technicians spill out of the doorways of the building, baby-faced and wearing jeans and sneakers. Some still have braces. Several young women link arms and skip toward a bus line. Others head next door to the dorm or over to the canteen where young couples are holding hands across plastic trays. “This work we do is tiring and requires focus,” says Liu Xin, a 26-year-old team leader in the bioinformatics division, as he sinks into a couch in one of BGI’s conference rooms. “So it’s good that they allow us to date.”
Liu is one of a small army of recent college graduates at BGI’s largest facility, a former shoe factory. Two gray buildings, the factory and the dorm, are wedged between one of Shenzhen’s industrial zones—a grid of high-rises, apartment buildings, and several hospitals and medical equipment companies—and a lush, jungly hill that’s in the process of being bulldozed. Liu is stocky and serious, glad that he already has a steady girlfriend so he can focus on his career. He arrived at BGI three years ago, a biology major from Peking University with little experience in the study of the genome, the term for the entirety of an organism’s genetic information. Now he’s one of the senior people in his department. He works 12-hour days and oversees the sequencing of multiple genomes at a time. He specializes in plants—his team is currently sequencing a species of orchid. The bioinformatics teams around him are picking through the genomes of animals, microbial organisms, humans, and anything else that comes with a genetic code. “Everyone is just out of college,” he says. “I am now more sophisticated than most of the newcomers.”
Ten years after the mapping of the human genome, BGI has established itself as the world’s largest commercial genetic sequencer. The ranks of China’s college graduates are expanding faster than the country can employ them, and BGI is leveraging this cheap, educated labor pool. At the factory in Shenzhen, more than 3,000 employees (average age, 26) spend their days preparing DNA samples, monitoring sequencing machines, and piecing together endless strings of A’s, C’s, T’s, and G’s, the building blocks of genetic material.
“This is big data analysis,” says Wang Jun, BGI’s 36-year-old executive director. Wang, who regularly wears tennis shoes and untucked polo shirts, has published more than 35 articles in Science and Nature magazines and also teaches at the University of Copenhagen. Genomics, he says, is a new field and experts are being created from scratch. “We don’t need Ph.D.s to do this work,” Wang says. Instead, he believes genomics is best learned the old-fashioned way. “You just throw them in,” he says of BGI’s technicians. “The best way is hands-on experience.”
When the first draft of the human genome was released in 2000 as part of the international Human Genome Project, it seemed inevitable that scientists would soon crack the codes of disease, health, and human development. But the genome has proved more complicated. What scientists produced in 2000 was a long list of nucleotides, the combinations of markers in DNA that specify the makeup of an organism. It was just a list, and only a fraction of it is understood. Scientists were quick to identify fragments of the genome that translate into proteins, which control things like eye color, but these make up only 1.5 percent of the entire thing. As geneticists like to put it, they produced a map without a legend. This is where BGI comes in.
The company was founded in 1999 with state funding to lead China’s participation in the Human Genome Project. “We didn’t think about any business model; we basically didn’t plan further than the human genome,” says Wang, who was brought on in the early days of BGI to provide expertise in computers. China, he points out, was the only developing country working on the international project, and although the BGI team contributed only 1 percent of the finished project, it did it quickly and with little previous experience. “Even Bill Clinton thanked us for our participation,” he says. Wang joined the project when he was just 22 and worked under BGI’s two founders, the scientists Wang Jian, then 45, and Yang Huanming, then 47.
For its next challenge, BGI decided to tackle rice, whose genome is significantly shorter than that of humans but still large enough to impress. “We recruited a bunch of undergraduates, and lots of them had no working experience on any project,” Wang Jun says. The schedule was tight; Wang and his team barely slept. “We can do these kind of crazy things in BGI,” he says. “We can get 100 people together, very fresh, no experience at all, and get it done.”
In 2002, BGI published a paper on the rice project in Science and again attracted attention and money from the Chinese government, though it’s a private company. The company was rewarded with entry into the state-run Chinese Academy of Sciences, a distinction that secured additional funding. As part of CAS, however, BGI was limited to only 90 scientists. Its leaders had their eyes on expansion. “Our boss wanted to buy more sequencing machines,” says Deng Wenxi, a 24-year-old communications officer at the BGI factory. “But the Beijing government would not support us.” In 2007 the company found a solution by way of Shenzhen’s city government, which offered the factory 10 million yuan (about $1.6 million in today’s exchange rates) to cover startup fees and 20 million yuan in annual grants. The company changed its name from Beijing Genomics Institute to BGI Shenzhen and moved to the shoe factory. “Beijing is more strict,” says Deng. “Shenzhen wanted to welcome us.” The factory, she says, actually belongs to the Shenzhen government. When asked about the move, Wang Jun answers the question a little more vaguely, “Well,” he says, “the weather is definitely nicer here.”
Today, BGI organizes its operations into three categories—health care, agriculture, and the environment. When scientists look at the genome, they’re looking for variations from one individual to another, from species to species, or population to population. They’re looking to understand which variations link to specific traits or diseases.
As Wang Jun says, decoding any genome is a big data endeavor, and there’s no other research institution or for-profit sequencing company in the world that has the capacity of BGI. In health care, it offers straightforward sequencing services for universities and corporations globally, which ask BGI to sequence a genome and send it back for analysis. More often than not, BGI works in partnerships to map, analyze, and publish the findings.
When Deng meets me in the morning, the first place we visit is a kind of trophy room on the top floor of the factory where the walls are decorated with copies of Science and Nature magazines, each containing a paper from BGI. The subjects include the company’s part in the ICGC Cancer Genome Projects; its work with 2,000 families to map the genomes of children with autism; its mapping of the epigenetic differences (differences in gene expression not the result of a variation in the genetic code) between 5,000 twins; and a project to increase the number of identified Mendelian, or inherited, genetic disorders.
In addition to linking more disorders to variations in the genome, BGI’s research could change the way medical providers and governments understand and respond to outbreaks of disease. BGI’s partners include GE Healthcare (GE), Merck (MRK), and Novo Nordisk (NVO), and the work they’re doing will help pharmaceutical companies understand why some drugs are more effective in some populations and less so in others. In May 2011, BGI flexed its muscle during a deadly outbreak of E. coli in Germany. As soon as the outbreak began, BGI began to piece together the genome of the strain from samples provided by the University Medical Center Hamburg-Eppendorf. Within five days, the company released sequencing reads on the strain, leading to the crowd-sourced assembly and analysis of the genome. In the future, BGI’s expertise could be applied to viruses.
Wang Jun says BGI’s first goal is to “find ways that genomics can serve society.” The company, he emphasizes, is not state-owned, and the profits it makes are cycled back into research. The company has been steadily increasing its profits in the past few years. In 2011, BGI reported revenue of 1.2 billion yuan. Many projects the company takes on reflect this policy of for-profit science. In agriculture, BGI is mapping genome sequences it considers proprietary and using them to engineer superior strains of rice, millet, and even fish. Technicians do this by using genomic information to breed for certain traits. Hybrid millet, says Deng, could improve yields and help alleviate hunger in Africa. Balsa trees designed by BGI can withstand colder temperatures, which means they could be grown in China. Sharing the trophy room with the BGI published papers is a single large fish, mottled green and gray, swimming in a tank. “That is our hybrid grouper,” Deng says. It grows three times as fast as a regular grouper, she says, and according to a BGI brochure, it tastes better. When I ask Wang how BGI determines which plants and animals to sequence as part of its “1,000 Plants and Animals” project, he answers, “We start with anything tasty.”
The company is also taking part in the sequencing of the earth’s microbiome, meaning all microscopic organisms. This is an effort to identify the functional and evolutionary diversity of microbial organisms across the globe. (BGI has sequenced more than 1,000 such organisms in the human gut.) Many of the plant and animal genomes it has sequenced, such as the giant panda and Liu’s orchid, are beneficial mainly to scientists studying the traits and evolution of animals.
BGI has also made forays into cloning and has invented a simplified technique. Called “handmade cloning,” it cuts costs and makes large-scale cloning more realistic for use in animal and plant research. So far BGI has applied the technique to clone mice, sheep, and a mini pig that glows in the dark. In an office, a slightly desiccated stuffed piglet sits in a small display case. A better-looking piglet, Deng says apologetically, had been misplaced.
“It’s the Wild West,” says George Church, a professor of genetics at Harvard University and an adviser to BGI. “This is a field that has arisen overnight, and the number of discoveries is going up exponentially.” A single genome contains a massive amount of data (a human genome, for example, contains about 3 billion nucleotides, or data points), and a bioinformatics expert’s work requires sifting through, comparing, and testing the information in multiple genomes. While sequencing costs have dropped dramatically in the last 10 years, the process is far from automated. Companies that offer personalized genetic testing, such as 23andMe, typically test only for a sampling of 100 traits and diseases, or about 1/3,000th of the entire genome, Church says. For about $4,000, BGI does the whole thing.
BGI’s electronic sequencers—11 are in Shenzhen, 77 in Hong Kong, and more than 66 scattered throughout the rest of China and the world—are imposing-looking black-and-white boxes, slightly taller than the technicians that run them. They don’t churn out fully formed genomes; rather, they handle fragments, reading each nucleotide from signals emitted as the machine resynthesizes a template DNA strand. These out-of-order sections of the genome require piecing together. Once assembled, a genome sequence still has to be interpreted to find the source of whatever trait or disease a particular study aims to find. This process, even with a reference genome fully in place, is difficult to hand over to a computer program. “The software basically doesn’t exist yet,” Church says.
BGI’s Shenzhen factory is organized so that a genetic sample travels from floor to floor as it goes through the sequencing process. When a sample first arrives—it usually comes in a test tube—it’s taken to the fourth floor, where workers in different colored coats prepare and expand the genetic material (coat color signifies the kind of DNA being handled). Workers bend over tiny vials, mechanically separating genetic material with a syringe. They’re splitting DNA samples into single strands and will soon put them through a chemical process called polymerase chain reaction, or PCR. This will copy a single DNA fragment about 10 million times. Microscopic chains of beads holding the DNA fragments are then loaded onto a sheet with tiny cups and sent to the sequencers on the fifth floor. When the machines are finished, the information is delivered electronically to the second floor, where Liu, the bioinformatics team leader, works.
In a large, open room, more than 1,000 young scientists sit in cubicles, staring at strings of computer code, piecing together sections of whatever genome they’ve been assigned. Liu’s team is slightly apart from the rest. “You’re looking for variants or parts of the genome that are hard to map,” he says. Computer programs have difficulty identifying a new variation unless a spot on the genome has already been pinpointed and entered into the computer program. Recently, with the orchid, Liu’s team had problems interpreting a certain section. Liu was assembling his species of the plant according to an orchid reference genome, and certain sections of the code were just not lining up the way researchers (and the computer) had expected. Trying to tie these sections to certain orchid traits was proving difficult. Liu calls it a “weird region.”
“We had to figure out how to analyze this,” he says. “It required us to try different solutions, look through sets of data that could be important, and figure out why we were having trouble mapping that section.” Researchers tried different solutions and found that some of the orchid’s traits were heterozygous—there were two spots on the genome responsible for their development. Weird regions of a genome, Liu says, are the most exciting part of his job.
A decade ago young people arriving in Shenzhen would have hoped to land a job building iPods or sewing jeans, a wholly different career track from Liu’s colleagues. “This is the virtue of Shenzhen,” Liu says. “People are all coming from other places, they are here trying to make money or to find some opportunities. We all have the same kind of ambitions.” An opening salary at BGI runs around 3,000 yuan ($481) a month. “It’s not great, but it is competitive,” Deng, the communications officer, says.
Executive Director Wang could easily disappear in the crowd of recent graduates on BGI’s campus if it weren’t for his imposing height. He doesn’t like calling BGI a factory—he’s more interested in creating the feel of a college campus. In addition to encouraging dating, BGI promotes the creation of clubs and the enjoyment of free time. “On weekends we like to climb North Mountain,” Deng says, pointing to a hill in the distance. Wang likes to play basketball, and BGI has an annual tournament. According to Deng, Wang’s team always wins. He has a suspicious number of tall people on his team. “We think he might hire people just for the basketball team,” she says, giggling.
After six o’clock, when most of BGI’s staff is done with work, a basketball court outside the dorm quickly gets crowded. Some of Liu’s colleagues from the bioinformatics division stick around to watch the games. One of the dorm’s oldest residents, Tai Shuaishuai, says he’s just taking a break before heading back to work. “For those of us who always stay in the office, the dorm is more convenient,” he says, smiling through braces. Tai is 31, and his first name translates to “handsome handsome.” Like Liu, he’s been at BGI since 2009, an eternity at the Shenzhen factory, and he heads a team using sequencing to improve what he calls “molecular breeding,” the same process responsible for BGI’s grouper. Tai is also responsible for reviewing potential employees.
“China has a lot of universities, but we prefer candidates from the top universities,” he says. “To be a BGIer means you have to be creative as a scientific researcher, and you have to have team spirit. We take a lot of things into consideration—skills, knowledge, educational background, and working style.” According to Tai, an offer made to a potential employee is rarely turned down.
One reason may be that BGI offers employees the chance to study while working. If Liu hadn’t joined BGI, he says, he probably would have pursued graduate studies somewhere else. “I would not be getting this hands-on experience,” he says. “Working here is basically a Ph.D. program.” Nonetheless, he’s starting at the University of Hong Kong this year in a program that will only require that he leave work for a day or two each week.
Most of the employees on the basketball court seem to be participating in one of BGI’s work-study programs. One group of four says they’re still college students, living at BGI on a full-time internship. “It’s just as comfortable as the dorms in our universities,” one says. “And Shenzhen is a great place to be for young people.” On weeknights, he says, karaoke halls offer a discount.
Around 6:30 it begins to rain, and the BGI basketball court empties. Tai ducks into the entrance of the dorm, where a janitor is mopping under fluorescent lights and BGI employees queue up to buy snacks. A couple of people from the bioinformatics team gather around Tai and talk about their plans. “I would like to go abroad to the U.S.,” says a team leader named Gao Zhibo. “Not to get my Ph.D. but just to improve my language skills and my social skills.” Tai, for his part, is hard-pressed to imagine why anyone would ever leave. “Doing scientific study is my passion,” he says. “It’s my belief that science has no limits.”
[Comments are far too numerous and sensitive to fully list here. The remark is most noteworthy by Harvard genomics professor George Church (whom I dubbed "the Edison of genomics"), as an adviser to both BGI and my Silicon Valley HolGenTech, who says (about genome analytics) "the software basically does not exist yet". If anyone, George should know, since the Boston-based genome analytics company he founded (Knome) sells high-performance computer boxes with genome analytics software. (His statement should be quoted when it comes to any due diligence of investors of a list of over 100 "genome informatics companies" and a similar number of "genome service" companies already in existence). As the average age of workers at BGI is a tender 26 years, on average they were 7 years old when the "Internet boom" started (1994) - and most of them never lived in Silicon Valley, and even now do not hold even one Ph.D. degrees. Thus, a brief recollection might be useful by someone who shared pioneering THAT industrial paradigm-shift (at Academia, US Government and Private Industry). "Software" was the least crucial #5 component of "the Internet boom". While software development is expensive in California (compared to the monthly $481 at BGI - in Silicon Valley it is at least one order of a magnitude more expensive), other more important crucial components were 3) science, 4) algorithm, 5) business model (let's us not factor-in the most important #1 component that is explicitly declared for BGI to have been provided by governments; MONEY. Those who would interject that BGI is not "owned", only "funded" by government, an interesting parallel might be that e.g. the CIA is widely known to fund "independent" businesses for tasks that are strategically crucial for the global interests of the USA). Looking at #3-5 components of the Internet boom (compared to the present "Industrialization of Genomics") some major differences are extremely important. The most enormous difference is that "Science" of Internet was essentially a "no brainer", since the Internet-technology (packet-switching) was entirely man-made, thus 100% transparent, remarkably easy to understand and program for. Further, it was actually built to be utterly simple and robust to withstand a nuclear war. In contrast (and Prof. Church would probably enthusiastically second this statement, as well as some 15,000+ others), "scientific understanding of (recursive) genome function only exists at its basics perhaps since 2008" . Thus, very much like nuclear technology first required nuclear PHYSICS, which in turn called for the colossal challenge of development of quantum physics, massive investments into "industrialization of genomics" were too early before gaining basic insights into the intrinsic mathematics of the genome-epigenome regulation. Further to the comparision of Internet- and Genome Informatics, with simple e-mails (or a single gene) there is not much need for search- (or regulation)-algorithms, Internet worked fine without a "search-engine" or regulation of the output of a single gene with "Operon" (Nobel to Jacob and Monod, 1965). With the advent of the Internet-boom there were so many Internet-companies that huge billboards along highway 101 asked "What will all the industries do with the Internet?" - to be answered in a mile or so "What sill all the industries do without the Internet?". The boom gave birth to the brilliant (in retrospect, utterly simple...) Google search-algorithm-patent (and company) - that swept away nearly all others, except those that found at the outset "business models" (eBay, Amazon) that guaranteed their survival. At the beginning of the Internet-boom there were many companies without any "business model" at all. "The name of the game" used to be just the "number of eye-balls" attracted by any given firm. (There were some, who remembers their name, that actually paid for geeks simply to look at, and click at websites - with no utilization of the "captured eye-balls"). A bit similarly, "the big question" now appears to be "how many DNA sequences did you do, how many e.g. cancerous DNA sequences do you store?" With the Industrialization of Genomics, however, there are some obvious "business models", simple as they are, in "Genomically Modified Organisms". We all know (and the article reinforces that impression) that China (BGI) - and perhaps with some more "checks and balances" the USA as well - are very big at that, certainly sustaining a couple of thousand developers at $481 per month. (Science, that would be much more expensive in house, can be acquired). Thus, the secondary most obvious "business model" is a given. (For those wondering, the #1 is "biodefense"). Energy, health and pharma are #3-5 with significant glitches to overcome for global and scalable business models (public comments are not appropriate here) -c.f. Pellionisz; HolGenTech_at_gmail_dot_com.
Non-coding Mutations May Drive Cancer
The majority of human melanomas contain mutations in a gene promoter, suggesting mutations in regulatory regions may spur some cancers.
By Dan Cossins | January 24, 2013
The Scientist
Mutations in the regulatory, or non-coding, regions of the telomerase reverse transcriptase (TERT) gene—a cancer-associated gene that encodes a component of telomerase, an enzyme known to help protect the ends of chromosomes and support cell longevity—may be at the root of most melanomas, according to two papers published today (January 24) in Science.
In both studies, researchers identified mutations that created new binding sites in the TERT promoter for particular transcription factors and resulted in increased transcriptional activity at the TERT promoter, which may in turn lead to increased expression of the gene and the endless cell division characteristic of cancer cells. The findings suggest that mutations in regulatory parts of the genome, in addition to those in protein-coding sequences, may be a key mechanism causing the growth of certain types of cancer.
“I am excited by the finding that regulatory mutations can apparently act as drivers of carcinogenesis,” Elaine Mardis, a cancer geneticist and co-director of the Genome Institute at Washington University, Missouri, who was not involved in the research, said in an email. “This is great news for labs like ours that have always emphasized the importance of whole genome sequencing over exome or targeted sequencing.”
Until recently, sequencing efforts focused almost exclusively on the protein encoding regions of cancer genomes, due to the high cost of whole genome sequencing and the fact that it’s easier to identify effects of mutations in protein-coding genes. As a result, scientists have identified many recurrent mutations in protein-coding regions that contribute to cancer development, but very few in non-coding regions.
To see if tumor genomes also harbor mutations in these under-explored regulatory regions, Franklin Huang and Eran Hodis of Harvard Medical School and colleagues took a closer look at whole genome sequences of malignant melanomas published last May. Sure enough, they found two somatic mutations, which they called C228T and C250T, in the TERT promoter region in 71 percent of the tumors they analysed—making them more common than the known melanoma mutations in the coding regions of the genes BRAF and RNAS.
“The fact that these mutations occur so frequently near what is a very important gene in cancer development was unexpected, but it was staring us in the face,” said Hodis.
Intriguingly, both mutations generated an identical DNA sequence containing a transcription factor binding site, increasing the transcription of reporter genes linked to the TERT promoter by 2- to 4-fold. This led the researchers to propose that these promoter mutations may be driving melanoma development by increasing TERT expression, which is tightly regulated in normal cells. When TERT is over-expressed, cells produce elevated levels of telomerase, prompting the regeneration of chromosome-capping telomeres and resulting in cells that can divide limitlessly. Mutations that increase TERT expression could thus be expected to promote cancerous growth.
“The fact that these mutations create a transcription factor binding site, although we haven’t shown it actually binds a transcription factor, is a clue that this could be one mechanism for how it increases activation of TERT,” said Hodis.
In a separate study, Susanne Horn of the German Cancer Research Center in Heidelberg and colleagues compared the whole genome sequences of tumors from a melanoma-prone family who did not carry two known germline mutations linked to melanoma, and identified a germline mutation in the TERT promoter in individuals with the cancer. Once again, the sequence change created a new binding pattern for certain transcription factors and increased transcription activity.
“[We think] the binding of transcription factors up-regulates the telomerase gene in the developing tumors,” Horn said. “High levels of telomerase may then lead to cells that can divide more often.”
Horn and her colleagues also screened the TERT promoter in 168 cell lines derived from metastatic melanomas from the general population, and found recurrent somatic mutations in 74 percent of them. The majority of those mutations generated new transcription factor binding sites.
Together, the studies show that “although our focus has been on the 1 percent of the genome that codes for proteins, there are potentially important discoveries in the rest of the genome,” said Huang. Indeed, Mardis added, if researchers continue to focus on exome or targeted sequencing of cancer genomes, “we are going to miss the clues available from analysis of the whole genome that may . . . ‘matter’ to driving the cancer growth.”
F.W. Huang et al., “Highly recurrent TERT promoter mutations in human melanoma,” Science, doi: 10.1126/science.1229259, 2013.
S. Horn et al., “TERT promoter mutations in familial and sporadic melanoma," Science, doi: 10.1126/science.1230062, 2013.
[Jacob & Monod (1961, Nobel in 1965) discovered over half a Century ago, that their (in retrospect "primitive") "Operon-regulation" is driven by "promoter" and "operator" sequences, OUTSIDE of genic borders. Should they be alive, they would strongly back Elaine Mardis (who took notice of FractoGene at its Cold Spring Harbor Laboratory presentation, invited by George Church, 2009). Dr. Mardis puts in this article "exomics" decisively into its proper ballpark.
"Genes" are 1.3% of the full human DNA, "exons" are the smaller fraction of 1.3% ("non-coding" introns are usually much longer).
While there is zero question that already at least 3,000 diseases are known to be caused by glitches of "exons", these are typically early-onset and rare diseases. Pharma just came out for a drug for cystic fibrosis, but it is effective for 4% of the patients, and the drug costs over $200 thousand per year.
The real economic driver (mega-billions already spent) is in cancer - where initially they may be nothing wrong with (the hundreds, or even thousands of) genes involved; but their recursive fractal regulation is derailed (sets of genes can stop producing excess proteins). Most cancers are known to be late-onset, rather common diseases. It is widely acknowledged that they are "genome regulation diseases". .
The "new war on cancer", thus narrows on the battlefield of structural variants affecting "genome regulation". With the FractoGene patent now available, utilization of software-enabling algorithms will help provide the "geek power" Dave Haussler is calling for. - Pellionisz, holgentech_at_gmail.com ]
DNA pioneer James Watson takes aim at "cancer establishments"
Reuters/Reuters - Dr. James Watson, co-discoverer of the DNA helix and father of the Human Genome Project, became the first human to receive the data encompassing his personal genome sequence at Baylor College …more of Medicine in Houston in this May 31, 2007 file photo. REUTERS/Richard Carson/Files
By Sharon Begley
NEW YORK | Wed Jan 9, 2013 6:34am EST
NEW YORK (Reuters) - A day after an exhaustive national report on cancer found the United States is making only slow progress against the disease, one of the country's most iconic - and iconoclastic - scientists weighed in on "the war against cancer." And he does not like what he sees.
James Watson, co-discoverer of the double helix structure of DNA, lit into targets large and small. On government officials who oversee cancer research, he wrote in a paper published on Tuesday in the journal Open Biology, "We now have no general of influence, much less power ... leading our country's War on Cancer."
On the $100 million U.S. project to determine the DNA changes that drive nine forms of cancer: It is "not likely to produce the truly breakthrough drugs that we now so desperately need," Watson argued. On the idea that antioxidants such as those in colorful berries fight cancer: "The time has come to seriously ask whether antioxidant use much more likely causes than prevents cancer."
That Watson's impassioned plea came on the heels of the annual cancer report was coincidental. He worked on the paper for months, and it represents the culmination of decades of thinking about the subject. Watson, 84, taught a course on cancer at Harvard University in 1959, three years before he shared the Nobel Prize in medicine for his role in discovering the double helix, which opened the door to understanding the role of genetics in disease.
Other cancer luminaries gave Watson's paper mixed reviews.
"There are a lot of interesting ideas in it, some of them sustainable by existing evidence, others that simply conflict with well-documented findings," said one eminent cancer biologist who asked not to be identified so as not to offend Watson. "As is often the case, he's stirring the pot, most likely in a very productive way."
There is wide agreement, however, that current approaches are not yielding the progress they promised. Much of the decline in cancer mortality in the United States, for instance, reflects the fact that fewer people are smoking, not the benefits of clever new therapies.
GENETIC HOPES
"The great hope of the modern targeted approach was that with DNA sequencing we would be able to find what specific genes, when mutated, caused each cancer," said molecular biologist Mark Ptashne of Memorial Sloan-Kettering Cancer Center in New York. The next step was to design a drug to block the runaway proliferation the mutation caused.
But almost none of the resulting treatments cures cancer. "These new therapies work for just a few months," Watson told Reuters in a rare interview. "And we have nothing for major cancers such as the lung, colon and breast that have become metastatic."
The main reason drugs that target genetic glitches are not cures is that cancer cells have a work-around. If one biochemical pathway to growth and proliferation is blocked by a drug such as AstraZeneca's Iressa or Genentech's Tarceva for non-small-cell lung cancer, said cancer biologist Robert Weinberg of MIT, the cancer cells activate a different, equally effective pathway.
That is why Watson advocates a different approach: targeting features that all cancer cells, especially those in metastatic cancers, have in common.
One such commonality is oxygen radicals. Those forms of oxygen rip apart other components of cells, such as DNA. That is why antioxidants, which have become near-ubiquitous additives in grocery foods from snack bars to soda, are thought to be healthful: they mop up damaging oxygen radicals.
That simple picture becomes more complicated, however, once cancer is present. Radiation therapy and many chemotherapies kill cancer cells by generating oxygen radicals, which trigger cell suicide. If a cancer patient is binging on berries and other antioxidants, it can actually keep therapies from working, Watson proposed.
"Everyone thought antioxidants were great," he said. "But I'm saying they can prevent us from killing cancer cells."
'ANTI-ANTIOXIDANTS'
Research backs him up. A number of studies have shown that taking antioxidants such as vitamin E do not reduce the risk of cancer but can actually increase it, and can even shorten life. But drugs that block antioxidants - "anti-antioxidants" - might make even existing cancer drugs more effective.
Anything that keeps cancer cells full of oxygen radicals "is likely an important component of any effective treatment," said cancer biologist Robert Benezra of Sloan-Kettering.
Watson's anti-antioxidant stance includes one historical irony. The first high-profile proponent of eating lots of antioxidants (specifically, vitamin C) was biochemist Linus Pauling, who died in 1994 at age 93. Watson and his lab mate, Francis Crick, famously beat Pauling to the discovery of the double helix in 1953.
One elusive but promising target, Watson said, is a protein in cells called Myc. It controls more than 1,000 other molecules inside cells, including many involved in cancer. Studies suggest that turning off Myc causes cancer cells to self-destruct in a process called apoptosis.
"The notion that targeting Myc will cure cancer has been around for a long time," said cancer biologist Hans-Guido Wendel of Sloan-Kettering. "Blocking production of Myc is an interesting line of investigation. I think there's promise in that."
Targeting Myc, however, has been a backwater of drug development. "Personalized medicine" that targets a patient's specific cancer-causing mutation attracts the lion's share of research dollars.
"The biggest obstacle" to a true war against cancer, Watson wrote, may be "the inherently conservative nature of today's cancer research establishments." As long as that's so, "curing cancer will always be 10 or 20 years away."
(Reporting by Sharon Begley; Editing by Jilian Mincer and Peter Cooney)
[No reader should have the wrong impression that "The Nobel Laureate Champion of DNA; Dr. Watson" wrote just an Op-Ed on cancer, asking for $1Bn on yet another "Big Science" project (while ditching both the1971 "War on Cancer" - the federal government has spent well over $105 billion on the effort, Kolata 2009b and now withdrew his support from "The Cancer Genome Atlas" project, starting in 2005, with uncounted hundreds of billions of dollars). To dispell any mistaken impressions, his full single-author scientific publication is available from this website: "Oxidants, antioxidants and the current incurability of metastatic cancers"
His approach of seeking a "miracle chemical" (as basic as oxygen) is to be contrasted, however, with the vision of Dave Haussler, who stated recently in an extremely forceful 4-minute video (with transcript) that "cancer is a digital disease and it will have a digital cure".
The entire "fractal school" (yours truly from 1989, 2002 recently summarized) with "big guns" since 2009 "Mr. President, the Genome is Fractal!") outlines a digital approach, based on the principle of recursive genome function 2008. In the same year, the spectacular NOVA Special Video on Fractals illustrated (see below) that cancers are fractal - and as reviewed lately in 2012 clogging the recursive genome function by large CNV-s are linked by dozens of independent experimental evidence to cancer(s) and a slew of genome (mis)regulation diseases.
See the spectacular NOVA Special on Fractals, YouTube 2008
As elaborated in a just-out Springer Textbook Chapter by fractalist co-authors, cancer is unlikely to be treated (let alone cured) without a digital understanding of genome (mis)regulation - and groping for understanding it at the level(s) of full-blown complexity misses the chance of grasping the rudimentary, therefore simple derailment (Faraday: "there is nothing simpler than a problem solved"). Such "don't work the problem, work the causes" approach may call for a better definition of "Data Science" as "Science and Technology of Data Analytics". The present collapse of "DNA Data Generation" because of an oversupply of data while there is no sufficient demand by robuts-enough analytics, is most dramatically manifest by having to sell a crown-jewel of Silicon Valley (Complete Genomics) at greatly depressed valuation to the fiercest global competitor of the USA.
Cancer data generation (without sufficient analytics) may become the "Next Glut". The problem is NOT with the "cancer establishments" - but with the delay how many of us has to perish before fractal analysis of cancerous genomes becomes commonplace - in hospitals. At the field at large one may be astonished that FRACTAL CANCER words already yield 3.7 MILLION hits on Google! - Pellionisz
The Committee on Foreign Investment in the United States recently approved the sale of Complete Genomics, based in Mountain View, California to China-based BGI (formerly known as the Beijing Genomics Institute). Some decried the committee’s investigation as the overarching meddling of government in business affairs while others hailed the committee’s efforts as an important measure necessary to protect national security. In fact, both sides were right.
Attempts to regulate or restrict the export of American biotechnology are likely to backfire and hurt American competitiveness. We’ve seen this pattern before. Efforts by the US government to ban the export of encryption technologies during the 1990’s did little to prevent their use around the world. In fact, just the opposite occurred, it spawned the development of foreign firms in the encryption space and the launch of competing products. In 2001, President George W. Bush banned federal funding for stem cell research, delaying important and potentially life-saving research into illnesses ranging from cancer to Parkinson’s disease. The result: the US fell behind in this area and some of our best scientists went overseas to continue their research unfettered by American political affairs. Regulating dynamic, fast-changing technologies is difficult.
This said, newly emerging technologies ranging from robotics to nanotechnology do raise significant national security concerns, as do the advances in genetics and synthetic biology. Were we to ignore these concerns, we would do so at our own peril. Whether or not we realize it, we are at the dawn of a new information revolution. This time however, the information stored and processed won’t be with 1′s and 0′s on silicon chips, but rather encoded in the operating system of life itself: DNA. Genetic engineering and synthetic biology empowers people to alter the molecular mechanisms of cells and viruses, agents that can replicate and spread, potentially beyond human control. This shouldn’t just be a national security concern. It should be a global security concern.
Though we are in the earliest days of developing the emerging field of synthetic biology, in the coming years, it promises to have massive impact on everything from business to medicine and energy to warfare. The Chinese government and BGI clearly understand this and are pouring tremendous resources into research and development of these biotechnologies. That US Representative Frank R. Wolf, Republican of Virginia, was the only member of Congress known to have publicly expressed concern about BGI’s purchase of Complete Genomics is not just startling. It is also emblematic of how far the rest of Congress is from understanding how quickly the biotech revolution will be upon us and how dramatically it will impact all facets of our world.
For America to remain competitive, the appropriate public policy response is to ban neither research nor international trade, but rather to invest heavily in both. The United States government, through its public funding of DARPA, was responsible for the creation of the Internet and our nation reaped untold wealth as the progenitor of the information revolution. Yet the economic gains realized from the Internet may be dwarfed by coming boom in genetics and biotechnology. What role the United States will play in that brave new world is yet unanswered. In the meantime, it is worth studying the progress being undertaken by other nations, including China, and by companies such as BGI, not as a means of inhibiting their scientific progress, but as a catalyst for driving our own.
[Fine, the first question is "what global security concern?" Consider e.g. the clip below from a peer-reviewed paper that appeared in 2010 by the author of not-very-threatening country of UGANDA:
"...a "bio-weapon" may be developed in the laboratory by continuous cycles in which an acutely fatal retrovirus of zoonotic origin is co-cultured in human cell lines, rendering the human cells permissive to that retrovirus tropism. The same procedure may be used to select an appropriate animal carrier or "vector", say chickens, pigs, or even cows. Herein, shedding of the retroviral bio-agent may be enhanced by vaccination of these vector-hosts if they are appropriately adapted to act as natural hosts. Several other models for retrovirus-based bio-weaponry are possible, including starting with a known human retrovirus such as HIV and recombinantly engineering it to be acutely fatal (say by pseudo-enveloping it with or enabling it to express Ebola/Marburg gp1, 2, the major pathogenic protein of filovirus hemorrhagic fever). Additional modifications such as altering the transmission dynamics of the retrovirus from contact with infected body fluid to air- or water-borne transmission would make it more damaging, though it is not immediately clear how that could be achieved. More peacefully and productively, the mathematical formalism of retrovirology advanced here also underscores strategies for avoiding or mitigating the impact of retrovirus-based bio-weapons, such as the development of therapeutic interventions and avoidance of contact..."
The above (along with the generalized tensor-theory of mathematical, thus software- and synthetic genomics-enabling treatise of retroviruses) can be found in its entirety on the web.
Do we have to fear (author) Misaki Wayengera in Uganda? Probably not. However, what can be done in Uganda, cannot be done just anywhere in the World?
Were physicists (Drs. Szilard and Teller) signaling the Germans who started to mine uranium-ores in the Czech mountains? Yes, leading not only to the Manhattan Project, but also to the facilities of Alamogorodo and later Lawrence-Livermore (the latter today the single facility of the Department of Homeland Defense). Was the Soviet Sputnik a signal of global threat by means of intercontinental ballistic rockets possibly reaching any point of the USA (just as a virus could today)? Yes, (via JFK' historical call) leading to Congress creating an entirely new government-branch of NASA (with a facility of Ames Res. Ctr. in the heart of Silicon Valley - perhaps less important for US government space projects than the host of the summer-school of "Singularity" :-) What NIH, NSF and DARPA could not do thus far for Genome Informatics, can not be realistically expected from the same. This worker, having worked as a National Science Academy "National Research Council" scientist assigned to NASA Ames Res. Ctr., over two decades ago (1990) was at that time somewhat naive to propose their cooperation, still found on the web - but in frustration detoured by the award by the Humboldt Prize by Germany. It is respectfully submitted, that today no single US government Agency, much less any of their "cooperation", could do what only Congress might accomplish, e.g. when bringing to life the Manhattan Project, or NASA (respectively). Significant progress could be achieved by even less; e.g. by switching the Ames Res. Ctr. of Silicon Valley from NASA to the Department of Homeland Defense, with a unified command of the Lawrence-Livermore facility for highly classified genome projects, and deploying the Ames Res. Ctr. (similarly as it was deployed for the "Information Superhighway", a.k.a. "Internet") to spearhead developments comparable how the Chinese Government utilizes a "private company" (Beijing GENOME Institute). Food for thought - Pellionisz, holgentech_at_gmail_dot_com ]
Playing Well with Others (when Industrialization of Genomics is no longer played only in Academia)
Despite the criticism Myriad Genetics receives for its business practices regarding BRCA testing, the company has stayed on message about two things. One, that its exclusive licenses and patents on BRCA 1 and BRCA 2 mutations, which essentially give it a monopoly over the BRCA testing market for hereditary breast and ovarian cancer, is good for business. And two, that the enforcement of its IP rights hasn't been harmful for patients or for researchers looking to develop better tests.
Researchers and patients are, of course, challenging Myriad on these very points in Association for Molecular Pathology et al. v. USPTO et al. In the case, slated for the Supreme Court this year, plaintiffs are challenging patents on BRCA 1 and BRCA 2 mutations held by the University of Utah and exclusively licensed to Myriad, alleging that Myriad's patents are invalid because they claim gene sequences in the body, which are naturally occurring substances and cannot be patented under US law.
While the plaintiffs in AMP v. USPTO assert that Myriad's patent position is stifling research and patient access to innovative tests, industry observers say that most diagnostic companies don't enforce their patents in the manner Myriad has. In the latest article painting Myriad to be a bad industry player, Bloomberg's Robert Langreth highlights the stories of patients who haven't been able to access the most accurate test results and researchers who aren't able to develop and offer the best test science allows because of the company's business practices.
One patient with fallopian tube cancer, Tory Galloway, received a negative result for disease-linked mutations by Myriad's test, and told her four sisters that the disease was unlikely to be hereditary. However, after being tested by the BROCA Cancer Risk Panel at the University of Washington, Galloway found out that her cancer was due to a gene mutation that wasn't part of Myriad's test.
Daily Scan's sister publication Clinical Sequencing News previously reported that the inventors of BROCA — breast cancer genetics pioneer Mary-Claire King and UW's Tom Walsh — excluded BRCA1 and BRCA2 from the panel of genes the targeted sequencing test gauges. The researchers will not add these genes to their test until "the patent situation is regularized," King told CSN.
The Bloomberg piece also discusses what many breast and ovarian cancer clinicians have been saying for some time, that Myriad is not playing nice with other researchers as it keeps its data on variants of unknown significance in a proprietary database. The impact of this practice is illustrated by the example of Runi Limary, who found a cancerous lump in her right breast at the age of 28. Limary had the breast removed and received months of chemotherapy and antibody treatment and, eventually, was able to get Myriad's test after her employer-provided insurance agreed to cover it.
Testing revealed Limary had an extremely rare BRCA1 mutation and Myriad could not tell her definitively if it was harmful or not. Limary, a plaintiff in AMP v. USPTO, believes that if Myriad’s proprietary database was public and more companies were offering BRCA testing, then she might have had more information on what her BRCA mutations meant for her health.
Our sister publication Pharmacogenomics Reporter recently detailed the disagreement among researchers and industry players about whether or not to share gene-disease association data for rare or uncertain markers. Some clinicians and industry players say that sharing of such databases is critical as next-generation sequencing technologies become more commonplace in research and patient care. Others are of the view that the personalized healthcare revolution will be led by those that can interpret patients' molecular data and, therefore, making public the kind of database Myriad has amassed would be foolish from a competitive standpoint.
Unsurprisingly, Myriad falls in the latter camp. Characterizing Myriad as an "unabashed financial success," the Bloomberg piece also quotes CEO Peter Meldrum defending the company's efforts to keep its VUS database secret. Sharing "doesn't make a lot of business sense," Meldrum says.
[Nobelist Jim Watson was right to resist "patenting" - (yes, TWENTY years ago...) when Genomics was in the Academic Discovery Stage. In the new age of Industrialized Genomics, however, huge global investments at the size of Intel, Samsung, Siemens, Roche, China's BGI (etc, etc) are in a dog-fight over even hotly competed "small entities" like Complete Genomics or Illumina. The global competition is to the tune of $Bn-s already (Baylor's "Moon Shot" over cancer is ticketed $3 Bn). Even at the tiny level of an individual, investing decade(s)-worth of efforts to create novel Intellectual Property, not only without any backing at all, but against the strongest of head-winds, extracts an untold saga of time (life), blod and tears - how would substantial global investors be required to give up accepted legal instruments of IP protection (patents)? Quest Diagnostics invested close to a $Bn into genomic patent(s) - without legal protection genomics will be left high and dry with government funds dissipating - if the avalanche of smart money from industrial investors would be left unprotected. - Pellionisz]
The Decade of Genomic Uncertainty is Over (R.I.P. 2002-2012) -
The new era of Genomic Certainty has turned 10 years young.
Now over a "Decade of Genomic Uncertainty" ago, in 2002 Silicon Valley Journalist wrote in the e-version of "San Francisco Chronicle":
"When the human genome was first sequenced in June 2000, there were two pretty big surprises. The first was that humans have only about 30,000-40,000 identifiable genes, not the 100,000 or more many researchers were expecting. The lower -- and more humbling -- number means humans have just one-third more genes than a common species of worm.
The second stunner was how much human genetic material -- more than 90 percent -- is made up of what scientists were calling "junk DNA." The term was coined to describe similar but not completely identical repetitive sequences of amino acids (the same substances that make genes), which appeared to have no function or purpose. The main theory at the time was that these apparently non-working sections of DNA were just evolutionary leftovers, much like our earlobes.
But if biophysicist Andras Pellionisz is correct, genetic science may be on the verge of yielding its third -- and by far biggest -- surprise."
After an arduous agony of an entire decade, with ENCODE kicked in by 2003 and concluded in 2007, the "gene-junk-central dogma" concept, that Craig Venter rightly called "we're at a frighteningly unsophisticated level of genome interpretation"; with ENCODE-2012 the old science establishment of genome function interpretation had finally collapsed.
Now the challenge is how the fractal seeds planted as early as 2002 shoot into the strong stem of clinical applications, especially in fighting cancer, provide an ecosystem for the "industrialization of genomics", as part of "genome based economy".
As listed already, a brief timeline provides a review of formative events of the "Decade of Genomic Uncertainty" - Pellionisz
A Perspective; 2002-2012
from Inception in 2002 to Proofs of Concept and Impending Clinical Applications by 2012
National Medal of Science Awarded to Leroy Hood, MD, PhD, President and Co-founder, Institute for Systems Biology
GENOMICS=INFORMATICS (LeRoy Hood, 2002)
Dr. Leroy Hood was awarded the National Medal of Science in recognition of his visionary work in science and engineering.
Dr. Leroy Hood is the recipient of a 2012 National Medal of Science.
Dr. Hood is a world-renowned scientist, inventor, entrepreneur and visionary. His discoveries have permanently changed the course of biology and revolutionized the understanding of genetics, life, and human health. Dr. Hood developed automated technologies for analyzing proteins and genes that enabled the mapping of the human genome and revolutionized the field of genomics. He is a pioneer in the field of systems biology and has spent his career advancing an interdisciplinary, systems approach to biological research and clinical medicine.
Dr. Hood is being honored for his significant body of work in the study of molecular immunology, biotechnology and genomics. Dr. Hood is a pioneer of the systems approach to biology and medicine that focuses on innovation in health, wellness and disease prevention through P4 (predictive, preventive, personalized and participatory) Medicine. His work in this area is contributing to the emergence of a healthcare system that will deliver more effective clinical care at a lower cost.
“I am deeply honored to receive a National Medal of Science, and am profoundly grateful to the many fantastic colleagues and partners with whom I have worked throughout the years. Transforming human health is my life’s work and I am proud of all we have accomplished.”
– Dr. Leroy Hood
The National Medal of Science is one of the highest honors bestowed by the United States Government upon scientists, engineers and inventors.
Interview: A systematic future?
19 February 2007
Leroy Hood talks to Katherine Vickers about Google, prions and the human genome project.
Who or what originally inspired you to become a scientist?
I guess there were several things. I grew up in Montana, where my father was an electrical engineer for the mountain state telephone company. Very early on, while I was still in high school, he got me involved in the courses he taught on topics like electronic engineering.
During my last three summers of high school I worked at a summer camp that was managed by my grandfather, a geology camp in southwestern Montana for students from Princeton and Yale, and I ended up taking courses with the students that went out for that.
And finally, at high school; three of the best teachers I ever had were math and science teachers. They started me thinking seriously about a career in science. One of them was instrumental in persuading me to go to Caltech as an undergraduate, and that's where my career in science got started seriously.
What is systems biology?
The idea of systems biology is actually very old. 150 years ago people were interested in homeostasis, and that's the idea that you look globally at a system, not just at one protein or one gene at a time, to understand how it functions. But what's new today is that we can now do global analysis of many types of information, for example genomes, RNA, (in theory) proteins, metabolites and things like that. We're also learning how to integrate different levels of such information, which will help the understanding of systems. [Interestingly, the RNA System is largely an enigma, why/how coding- and non-coding RNA, mathematically of covariant and contravariant valences, impact by RNA interference - AJP]
What research projects are you working on at the moment?
"We've developed a much deeper understanding of the kind of changes that occur in prostate cancer, and from these studies we've developed totally new, very-early-on blood diagnostic approaches to detection. "We're working on a series of projects to do with new technological developments. So, we're working with collaborators making nanotechnology devices that we hope, in five years time, will be able to quantitatively measure a thousand blood proteins from a fraction of a droplet of blood.
We're collaborating with a company called Helicos BioSciences Corporation with single strand DNA sequencing, and I see this as the gateway to preventive medicine in the future. Each individual will have their genome sequenced and that will be the basis for future health predictions.
And then we're working on a series of microfluidic devices that will let us analyse single cells, and I think an understanding of single cells is going to be one of the next frontiers in biology.
On the systems biology end we're very interested in using systems approaches to study disease. The disease we've studied for the longest time is prostate cancer. We've developed a much deeper understanding of the kind of changes that occur in prostate cancer, and from these studies we've developed totally new, very-early-on blood diagnostic approaches to detection.
One of the major projects we're working on is prion disease in mice. We want to look at the dynamics of the disease and to see how the biological networks and cells of the brain actually change in correspondence to the pathophysiology of the disease. We found that some of the early diagnostic tests we discovered for prostate cancer are going to work for this infectious disease as well.
The other thing we're doing is developing a series of computational analyses. One example will let us analyse the blood molecular fingerprint of specific organs in mice and humans. From those measurements we can actually deduce the nature of the disease perturbed networks in the corresponding tissues.
What would you say has been the biggest challenge you've faced in your research career?
I would say that managing science, rather than the science itself, has been the biggest challenge. New ideas really need new organisational structures and it is often the newer generations of scientists that are more open to these new ideas. This was the motivation behind setting up the Institute for Systems Biology.
You were involved in the start of the human genome project. How do you think this has influenced science today?
Yes, I was involved, mainly because of our DNA sequencing machine, and was supportive of the start of the genome project. It brought about DNA arrays, promoted the idea of systems biology, and the realisation of the importance of computation and technologies in biology, and the view of biology as an informational science.
How far do you think we are away from personalised and predictive medicine?
"We will see a big change in the way we practise medicine and today's medical students are probably being taught incorrectly."Very close. In the next five to ten years we will have powerful tools for analysing individuals' genomes. There will be billions of measurements made on each patient, and then the problem arises of how to handle all this data. This is something we have approached Google and Microsoft about and we will be working with them. We will see a big change in the way we practise medicine and today's medical students are probably being taught incorrectly. Medicine will become digitalised and this will probably reduce the cost of healthcare and provide better allowances for the developing world. People are going to be living more actively in their 80s, and changes will need to be made in society to reflect these changes.
One final question - if you weren't a scientist what would you be?
I think I would be a writer, that's something I've always been interested in. But actually if I'm honest I couldn't imagine being anything other than a scientist.
[A Compelling Case for Fractal Analysis of Cancer and DNA] Is the Cure for Cancer Inside You?
By DANIEL ENGBER
New York Times
Claudia Steinman saw her husband’s BlackBerry blinking in the dark. It had gone untouched for several days, in a bowl beside his keys, the last thing on anybody’s mind. But about an hour before sunrise, she got up to get a glass of water and, while padding toward the kitchen, found an e-mail time-stamped early that morning — “Sent: Monday, Oct. 3, 2011, 5:23 a.m. Subject: Nobel Prize. Message: Dear Dr. Steinman, I have good news for you. The Nobel Assembly has today decided to award you the Nobel Prize in Physiology or Medicine for 2011.” Before she finished reading, Claudia was hollering at her daughter to wake up. “Dad got the Nobel!” she cried. Alexis, still half-asleep, told her she was crazy. Her father had been dead for three days.
The Nobel Foundation doesn’t allow posthumous awards, so when news of Ralph Steinman’s death reached Stockholm a few hours later, a minor intrigue ensued over whether the committee would have to rescind the prize. It would not, in fact; but while newspapers stressed the medal mishap (“Nobel jury left red-faced by death of laureate”), they spent less time on the strange story behind the gaffe. That Steinman’s eligibility was even in question, that he’d been dead for just three days instead of, say, three years, was itself a minor miracle.
In the spring of 2007, Steinman, a 64-year-old senior physician and research immunologist at Rockefeller University in New York, had come home from a ski trip with a bad case of diarrhea, and a few days later he showed up for work with yellow eyes and yellow skin — symptoms of a cancerous mass the size of a kiwi that was growing on the head of his pancreas. Soon he learned that the disease had made its way into nearby lymph nodes. Among patients with his condition, 80 percent are dead within the first year; another 90 percent die the year after that. When he told his children about the tumor over Skype, he said, “Don’t Google it.”
But for a man who had spent his life in the laboratory, who brought copies of The New England Journal of Medicine on hiking trips to Vermont and always made sure that family vacations overlapped with scientific symposia, there was only one way to react to such an awful diagnosis — as a scientist. The outlook for pancreatic cancer is so poor, and the established treatments so useless, that any patient who has the disease might as well shoot the moon with new, untested therapies. For Steinman, the prognosis offered the opportunity to run one last experiment.
In the long struggle that was to come, Steinman would try anything and everything that might extend his life, but he placed his greatest hope in a field he helped create, one based on discoveries for which he would earn his Nobel Prize. He hoped to reprogram his immune cells to defeat his cancer — to concoct a set of treatments from his body’s own ingredients, which could take over from his chemotherapy and form a customized, dynamic treatment for his disease. These would be as far from off-the-shelf as medicines can get: vaccines designed for the tumor in his gut, made from the products of his plasma, that could only ever work for him.
Steinman would be the only patient in this makeshift trial, but the personalized approach for which he would serve as both visionary and guinea pig has implications for the rest of us. It is known as cancer immunotherapy, and its offshoots have just now begun to make their way into the clinic, and treatments have been approved for tumors of the skin and of the prostate. For his last experiment, conducted with no control group, Steinman would try to make his life into a useful anecdote — a test of how the treatments he assembled might be put to work. “Once he got diagnosed with cancer, he really started talking about changing the paradigm of cancer treatment,” his daughter Alexis says. “That’s all he knew how to do. He knew how to be a scientist.”
First, Steinman needed to see his tumor. Not an M.R.I. or CT scan, but the material itself. The trouble was that most people with his cancer never have surgery. If there’s cause to think the tumor has spread — and there usually is — it may not be worth the risk of having it removed, along with the bile duct, the gallbladder, large portions of the stomach and the duodenum. Luckily for Steinman, early scans showed that his tumor was a candidate for resection. On the morning of April 3, 2007, less than two weeks after his diagnosis, he went in for the four-hour procedure at Memorial Sloan-Kettering Cancer Center, just across the avenue from his office at Rockefeller University.
After two hours on the operating table, his surgeon, Dan Coit, lifted the tumor from his abdomen. It was about two and a half inches long. Coit stitched a short thread across its top and a longer one on the side — an embroidered code to help the pathologists get oriented — and sent the specimen upstairs, wrapped in a towel and nestled in a tray of ice.
Claudia and Alexis were waiting in the lobby, along with Sarah Schlesinger, a longtime friend and member of Steinman’s lab, who is also a board-certified pathologist. It would be her job to manage the disbursement of the tumor to Steinman’s colleagues around the world, so its every nuance could be tested and its fragments incorporated into the drugs that would compose his treatment. When she arrived at the lab upstairs and held the tumor, it was still so warm that she could feel the heat through her latex gloves.
She chopped and sliced the tumor into samples, based on a list that Steinman helped draw up beforehand. A few grams would be placed in screw-top vials filled with a preservative for their RNA. Steinman’s administrative assistant would take another piece to Boston on an afternoon train, and some would go to a former student, Kang Liu, so she could sew confetti-sized squares of the tumor into living mice. If there was any left, they would send it to a researcher in Baltimore named Elizabeth Jaffee, who had mastered the art of culturing pancreatic cancer in a dish.
The mass was big enough that Schlesinger could get through all the items on the list. In the days, weeks and months that followed, Steinman’s cancer was sent to labs in Boston and Baltimore, Toronto and Tübingen, Germany, Dallas and Durham, N.C. With help from friends and former students, he would squeeze every bit of data from his cancer that he could.
Steinman’s last experiment would be, in many ways, the culmination of a new trend in cancer research: designing custom treatments for each patient. When he got sick, Steinman knew that the five-year survival rate for his kind of tumor was, at most, 1 in 10, even at Sloan-Kettering, one of the best oncology centers in the world. Typically, patients live six months. But he also knew that his chances might not be as bad as they looked. The means and medians of his disease were drawn from populations and so did not reflect the fact that every tumor is unique. Even tumors that look the same — cancers starting from a common organ, or a common kind of cell — may behave in different ways: some shrink and some expand; some succumb to chemotherapy. Now doctors can scan each tumor for clues about its DNA and use those clues to determine its strengths and weaknesses. Steinman could have his case described right down to the letters of its genome, in hopes of figuring out which therapies might work best for him.
This “personalized” approach to treating cancer, which subdivides the classic types according to distortions in their genes, has been growing at a rapid pace. In the past few years, laboratories financed by the government have set out to build a comprehensive atlas of the cancer genome — to collect 500 tumors from each of 25 kinds of the disease and then to analyze their DNA and RNA at a cost of more than $100 million a year. The advent of inexpensive genome sequencing has produced a gold rush in the commercial sector, too, with the promise that anyone’s tumor can be sliced and processed and analyzed, until its genetic fingerprint is decoded.
“It was thought a while ago that cancer would be too complex for us to really get our hands around it,” says Raju Kucherlapati, one of the principal investigators on the Cancer Genome Atlas and a professor of genetics at Harvard Medical School. But current research showed that “the total number of major biochemical pathways that are altered is not limitless.” If that’s true, then doctors might use these genomic data to improve their patients’ odds. Instead of applying a one-size-fits-all approach to treatment, they could select a mix of therapies from a standard arsenal, choosing only those that matched the features of a patient’s tumor. “I would venture to say that within the next 10 years, we could see a very significant revolution in the way that we think about and treat cancer,” Kucherlapati says.
The genomic approach that Kucherlapati and others have advanced sees every person’s cancer as a snowflake — a crystal made from several dozen basic shapes. But this idea has lately run across a deeper layer of complexity and one that is only now being outlined in the lab. For a paper published in the spring of 2012, a group of scientists based in London looked at tiny pebbles of disease from four kidney-cancer patients. Instead of limiting their analysis to a single piece of each tumor — one piece of tissue, excised after surgery or drawn out through a needle — the researchers took malignant cells from all over the patients’ bodies. They sliced specimens from more than half a dozen spots on the primary tumor, and then more from places where the cancer had spread: in the lungs, the chest wall and the fat surrounding the kidneys. When they compared the genomes at each location, they found a whole suite of tumor types with only a distant family resemblance, as if each spot and organ had become the home for its own phylum of disease. The growths were related — they had all descended from a common ancestor — but the cancer had mutated in new directions, sprouting a canopy of branches and twigs on its evolutionary tree. Samples drawn right from a kidney — as close as possible to where the tumor started — shared only a third of their mutations with the other offshoots.
A number of recent studies came to similar conclusions. Taken together, they reiterate what has long been known but not quite grasped in such detail: that even a single cancer patient carries a private ecosystem of pathology within her body, a tropical rain forest of disease. If the old chemotherapies and radioactive treatments worked like napalm to blast away the canopy, the new breed of personalized therapies target only specific plants. For some cancers, the more homogeneous ones, they do the job just fine. For others, though, the approach comes up against the relentless rules of Darwinian selection. Wipe out one subtype of a cancer — the clone that seems most aggressive, say, or the one that’s most prevalent in a biopsy — and you may have slowed the disease or thinned it out. But the cells left behind might represent a fitter strain and fill the niche.
Faced with this troubling complexity, doctors have fallen back on treating cancer like a game of Whac-A-Mole: find the harshest clone and knock it down, then repeat the process when the tumor reappears. Or else doctors will attack the tree right at its trunk, by finding those ancestral genes that every species in the body shares. But there’s another way to counter cancer’s biodiversity. Our bodies come equipped with a system custom-built to handle pathogens in all their many forms. If the immune organs could be activated against a cancer, we might find a pathway through the jungle and, maybe, to a cure.
“The work that the immune system does to sculpt itself around a cancer — that’s really the ultimate type of personalized medicine,” says Jedd Wolchok, a cancer immunotherapy expert at Memorial Sloan-Kettering who consulted on Steinman’s treatment. “The immune system’s job is to recognize the signs of danger and then with very exquisite precision to mobilize antibodies” and T-cells “that very, very precisely bind to individual targets.” Once that system locks on to its target, it can make adjustments, too, shaping the response to match the contortions and mutations of a tumor in real time. “It’s a therapy that lives,” Wolchok says, “rather than a medicine that passes in and out of the system.”
That’s the approach Steinman believed in most; it’s the one he was pursuing in his lab for many years before he got sick. But for a cancer vaccine to work, for any vaccine to work, the body has to learn the difference between its healthy cells and the ones that have been transformed into disease. It has to recognize its evil twin. And the part of the immune system that makes that possible, the mechanism by which our cells learn to kill one thing and leave another alone, was the focus of Steinman’s whole career.
The cell Steinman hoped would save his life looks something like a sea anemone or a ruffled shrimp dumpling. But when it’s viewed flat under the microscope, those squiggly sheets of membrane extend in cross section, like long, sinewy arms. That’s how they looked one day at Rockefeller in the early 1970s, when Steinman first spotted them in a dish of cells cultured from a crushed-up mouse spleen. When he announced his finding at a meeting in Leiden, the Netherlands, in 1973, he said those appendages reminded him of his tall and graceful wife. He thought about calling them claudiacytes.
Instead, with the assent of his supervisor at Rockefeller, the cell biologist Zanvil Cohn, Steinman declared his cells “dendritic,” from the Greek dendron for tree. This was, he intuited, a kind of cell that had never before been characterized and that served as the missing link in the body’s adaptive response to pathogens. Over the next few decades, Steinman would devote all of his work to the expansion of this idea: he would show his immune cell was not, as many suspected, just an oddball form of the macrophages, but something else entirely — a sentinel that guards our bodies from infection by teaching the soldiers of the immune system to distinguish their enemies from their friends.
The dendritic cell can lurk in the outer layers of the skin, in the throat, in the lining of the intestines and on any other surface where a bacterium or virus might try to edge its way into our flesh. When the cell grabs hold of something strange, it absorbs that foreign matter, digests it and drapes the macerated bits along its membrane. Then the cell inches its way along lymphatic ducts to the places in the body where immune cells gather and communicate and presents these bits as signs of an invasion.
Few took this work seriously in the early years. Lab mates dismissed Steinman’s spindly plasms; in the late 1970s, he lost his government grants. But the work went on, with Steinman evangelizing for his discovery until he inspired a network of immunologists to join his field. “He loved to see himself as a dendritic cell,” Schlesinger says. In a talk he gave in 2007, after winning the Lasker Award for Basic Medical Research, he waved his arms around in demonstration, like the conductor of a symphony with a dendrite baton.
By the 1990s, his discovery had given life to an old idea: that a more perfect knowledge of our immune system would lead to vaccines for otherwise intractable diseases. If the dendritic cell could be hijacked and put to use, if those markers on its membranes could be manipulated, then doctors might be able to inoculate their patients against H.I.V., tuberculosis or even cancer. Early experiments based on this premise came to little in clinical trials, though; Steinman and his colleagues learned it wouldn’t be enough to load the dendritic cells with antigen, to give the body’s bloodhounds sweaty socks. The cells would need another signal too — something to inspire them to share their message with the rest of the immune system. In the absence of that “go” signal, a dendritic cell might do the opposite of what was intended: it might parade its antigens around the lymph nodes as an example of what should be ignored, not what should be killed. Depending on the context, a dendritic cell could induce action or inaction, immunity or tolerance.
But Steinman never lost faith in his discovery as a vehicle for medicine. When he learned that he was sick, he signed up to have his tumor engineered into three existing, experimental vaccines. Each of these had been in testing for patients with other types of cancer, but Steinman had them customized with samples from his own disease. First he tried one, called GVAX, made from his irradiated cancer cells and fitted with a gene that, upon injection, sounds a warning call that recruits dendritic cells. Then he tried a pair of treatments using dendritic cells that were filtered from his blood, loaded with his cancer’s RNA (in one) or peptides (in the other) and put back into his body. In each case, fragments of his tumor would serve as both the quarry and the bait.
“It was just like the old days,” says Ira Mellman, a former trainee in Steinman’s lab and, by the time Steinman got sick, vice president of research oncology at Genentech in San Francisco. “We were all sitting around discussing what next week’s experiments should look like, except this time the experiments were him.” As the treatment plan took shape, Schlesinger managed reams of paperwork. For access to each experimental drug, Steinman would need to enroll himself in a single-patient, compassionate-use protocol with approval from the Food and Drug Administration. (The government receives around 1,000 applications for these one-person treatments every year and grants almost all of them, as long as the patient has cooperation from doctors and the relevant drug companies.)
Schlesinger also served as Steinman’s physician for the vaccine treatments, administering the shots, taking blood and checking up to see how he was doing. The team kept track of his response to each immune-based treatment as it played out in his T-cells. But the real benchmark, and the better index of his disease, was a carbohydrate protein called CA19-9 — a tumor byproduct that was also measured in his blood. When his levels were going down, it meant the cancer was in retreat. After each phase of his experiment, Steinman plotted out his readings and pasted them into slides on PowerPoint.
The same vaccines that Steinman received have shown promise in other patients. The irradiated-cell approach may increase survival for some patients with metastatic prostate cancer. A team based at Baylor University in Dallas has found encouraging results for reinfused dendritic cells in Stage 4 melanoma. But for the man who would later win the Nobel Prize for discovering dendritic cells, would these treatments work at all?
Steinman stayed in good health for the first few years — he still went for runs in Central Park or along the Charles in Boston — though the numbers from his blood tests were at times disheartening. His T-cells showed some signs of activation: they could recognize the markers from his cancer, but there was no way to tell if they were getting inside his tumor. “He wanted to see a much better response,” says Rafick-Pierre Sekaly, an immunologist at the Vaccine and Gene Therapy Institute of Florida who helped to analyze the data. In between the experimental treatments, Steinman was taking a drug called gemcitabine, a chemotherapy traditionally used in the treatment of pancreatic cancer to which he had a very good response. When he took gemcitabine, his CA19-9 would founder; the cancer would start to disappear. When he switched onto the vaccines, the tumor readings inched back up. “That was so upsetting to him, that he always needed the chemotherapy,” Sekaly says.
When he wasn’t on vaccines or chemotherapy, Steinman tried whatever else he could find. He had his tumor’s genome sequenced, to check for special vulnerabilities. At Genentech, Mellman tested a sample of Steinman’s tumor in a dish against the company’s whole library of pharmaceuticals. “We threw at his cells every drug that we had in development at the time,” he says, including many that hadn’t yet entered clinical trials. Meanwhile, the mice that received pieces of Steinman’s tumor served as minifactories for the production of his cancer and also as his patient-avatars in the lab. When one of Mellman’s drugs showed promise in a dish — a signaling inhibitor called vismodegib — he sent it for a trial in the cancer-ridden mice. When they responded, too, Steinman took it himself. It did not appear to work.
Still, years went by and Steinman’s disease never spread far enough to kill him. Was it just the chemotherapy that kept his tumor growth in check? Or had his custom-made vaccines acted in more subtle ways? It’s now well known that immunotherapies can linger in the body even as a tumor grows, and then start to shrink the tumor later on. It’s also possible that the vaccines and chemo worked in concert. But with no other patients for comparison and so little time between treatments to let the data run their course, the details remained a mystery. Mellman expressed skepticism about the treatment’s efficacy. Schlesinger was more positive, as was Coit, his surgeon. “I mean, look at his course,” Coit said. “The average survival even after a complete resection is measured in months, maybe a year and a half, and yet he kept going and going and going. You can’t help wondering if some of it had to do with this very innovative, novel approach.” As for Steinman himself, he wouldn’t make a claim one way or the other. “He totally, definitely felt that it was helping him,” says his daughter Alexis, but feeling is different from knowing. Though he kept careful notes about his treatment and joked with Schlesinger of writing up his one-man trial for The New England Journal of Medicine — in a case study titled “My Tumor and How I Solved It” — in the end there wasn’t any proof.
“Ralph was this remarkable mixture of optimism and skepticism,” Mellman says. “He always knew how this was going to end, and that he was living on borrowed time.”
At her mother’s suggestion, Alexis Steinman flew to New York, on Sept. 11, 2011, and found her father in a sickly state. For the first time since he had the disease, Steinman had begun to deteriorate. He was coughing so violently, her mother had told her, that she thought he might have broken a rib.
Alone with Alexis, Steinman said: “I have cancer in my bones.” Until then, he lived with his disease in much the way he lived before: working long days in the lab and long nights at his computer; traveling to conferences around the world; treating the lab to Entenmann’s cake. Now, for the first time since his diagnosis, he started losing hope in his treatment plan. He became depressed.
The cancer had stopped responding to gemcitabine, and his CA19-9 readings were out of control. On Sept. 18, he tried one more drug — a targeted therapy that had shown some very modest benefits and seemed well suited to his case, at least according to the data from his cancer genome. But it was too late; the disease had already spread throughout his body.
Steinman started planning for the end. “You know how they have those events in the Caspary” — the auditorium at Rockefeller University — “where somebody comes and plays classical music and they talk about you?” he asked Claudia. “I don’t want any of that.” He also told her that there should be no sitting shiva on his behalf. (“I don’t want people coming to the house for seven days,” he said.) Then he met with his closest friend at Rockefeller, a former grad student named Michel Nussenzweig. They discussed what would happen to Steinman’s students and his postdocs. Some he called himself, apologizing for leaving them before their work was done.
On the night of Sept. 24, Steinman ate dinner with his family in a faculty apartment on the Upper East Side of Manhattan. Claudia was there, and their three children and three grandchildren, too. The next morning, sitting on his bed, Alexis saw that he was finding it very hard to breathe. “I think I need to go to the hospital,” he announced. When they arrived at Sloan-Kettering to see his oncologist, he said, “I don’t think I’m getting out of here.”
He died five days later.
On a sunny day last August, almost one year after Steinman won the Nobel Prize, I saw his tumor for myself. Its cells were plastered to the bottom of a plastic case, 20 million tiny cancers crowded into a space the size of a large matchbox. A few days before my visit, the cancer was taken out of the freezer and left to thaw. As I peered at the last living remnants of Steinman’s body through a low-power scope, Sara Solt, a lab technician at Johns Hopkins, gave me her assessment: “Those handlike substances,” she said, referring to some spears of cytoplasm, “they almost look mean.”
For someone who has never seen a pancreatic cancer cell, though, Steinman’s disease didn’t look so mean at all — not black or jagged, just a bunch of soft-edged pentagons and distorted squares, with a few translucent tendrils jutting from their membranes.
The lab at Hopkins is run by Elizabeth Jaffee, the expert on vaccines for pancreatic cancer who received a part of the tumor for analysis. The vaccine she is testing in the clinic matches one of those Steinman received: it mixes bits of tumor — targets for the patient’s immune response — with a signal that recruits dendritic cells. As we sat together in her office, Jaffee reviewed what remains unknown about the method. It’s not yet clear how best to pick those targets. Steinman could have used a more standardized approach, with certain proteins preselected to maximize response; instead, he went with samples of his own disease, hoping these would give his dendritic cells something more to go on. But his tumor might have yielded a thousand targets for his T-cells, a protein soup swimming with red herrings. We still don’t know which strategy works best, Jaffee told me.
There’s another challenge, too, that Steinman had little chance to work around. Any cancer that has grown big enough to harm your health is one that has already figured out a way to hinder any T-cells that come after it. It has evolved a path around the body’s natural defenses. So it stands to reason that if you want to make an immune-based treatment work, you have to add in some other tumor-fighting drug, one that counteracts the tumor’s schemes for keeping immunity in check. “Vaccines alone are not going to be enough,” Jaffee said. “When in cancer, especially metastatic cancer, has one agent ever cured anybody? It doesn’t do it.”
Scientists have only just begun to understand how a tumor can shield itself from T-cells and to make a set of drugs that work against those mechanisms. When Steinman began his treatment, he and others in the field knew of one drug, called ipilimumab, that could do just this. Taken on its own, the drug appeared to extend the lives of patients with metastatic melanoma by months or even years. Yet the company that makes it, Bristol-Myers Squibb, was trying hard to get approval for single-agent use and wouldn’t allow Steinman to pair the drug with his vaccines. Researchers may have worried that the untested combination could have side effects that would delay its approval. (Citing company policy against discussing individual cases, Bristol-Myers declined to comment on Steinman’s treatment.) So Steinman tried the drug on its own in 2010. Instead of charging up his immune cells to fight off the pancreatic cancer, it knocked his T-cells into overdrive. They attacked his intestines and his pituitary gland, leading to dehydration and diarrhea. He ended up in the hospital.
“One of the problems we have in our field is that it’s very hard to combine two agents,” Jaffee said, referring to the bureaucratic hurdles she has faced in using ipilimumab. When she put the drug together with one of the vaccines that Steinman received, both treatments were enhanced. More than a fourth of those enrolled in her preliminary trial for pancreatic cancer — patients who expected to live for two or three months on average — have now survived for at least a year. Even so, Jaffee had trouble getting enough doses from Bristol-Myers Squibb to start a second, bigger test. The company eventually agreed, after ipilimumab was approved by the F.D.A., but the whole process set her research back by a couple of years. “This is my biggest frustration,” she said.
The same was true for Steinman. As the years went by, he was confronted time and again with the limits of what was understood and what was possible. He hoped to integrate his vaccines with chemotherapy and take the treatments simultaneously rather than in sequence. Jaffee’s lab has shown that this approach can enhance the immune response in a different way than ipilimumab does, by killing off a kind of T-cell that’s friendly to a tumor. Or else he might have combined the immunotherapies with drugs selected on the basis of his tumor’s DNA. But no one really knows how best to put these things together, just as no one really knows which antigens a vaccine should target nor how best to mobilize dendritic cells. Scientists now realize that dendritic cells come in dozens of different forms, some of which may be more effective in vaccines than others.
The disconnect between the extraordinary promise of cancer immunotherapies and the vagaries of their application, between the possible and the merely doable, always bothered Steinman. He used to tell his family that his work on dendritic cells might not be relevant until long after he was dead — that it would take years to determine whether vaccines based on his discovery could truly be effective in the treatment of disease. “All of this stuff was literally developing in real time as Ralph’s disease was developing,” Mellman says, “and the disease was ahead, unfortunately.” If Steinman’s personalized treatments worked at all, it was in spite of everything that was still unknown. “It was a laboratory experiment that worked for a while, we think, but we can’t go back and repeat it, so we’ll never know for sure,” Mellman says.
More experiments are on the horizon. Jaffee is building on Steinman’s work by combining the latest round of immune boosters with a dendritic-cell vaccine. There is progress in immunotherapy for other cancers, too: ipilimumab is being used for treating melanoma, and related drugs are in the pipeline that make a tumor more vulnerable to attack. In 2011, The New England Journal of Medicine published the results of a method known as “adoptive T-cell transfer,” in which T-cells are extracted from the body and reprogrammed to go after cancer cells. This has proved a potent treatment for some patients with advanced leukemia, but it poses greater health risks than the vaccines that rely on dendritic cells. “We’re going to learn a lot over the next 10 years,” Jaffee said, as we walked through the lab. “We’re just at the beginning. This is going to be the start of a whole new field.”
Steinman knew he wouldn’t live to see that field reach its full potential. It has been almost 40 years since he discovered the dendritic cell, and doctors have only now begun to make immunotherapies that work. By all accounts, that sluggish pace was deeply frustrating to Steinman, even before he got sick. “His mind went so fast, and he always wanted everything done yesterday,” Schlesinger says. Years ago, the two of them were on their way to their lab, and Steinman was in a foul mood because a trial they hoped to run was taking longer than expected. After some back and forth about the details, he stopped to consider what he had accomplished in his long career. “He said to me, ‘You know, all this time has gone by, and we haven’t cured cancer or found a vaccine for H.I.V.’ ” And then he paused, and told her, “We’ve got to get to work.”
Daniel Engber writes about science and culture. He has a weekly column in Slate.
The FractoGene Decade [collation of sources is available in .pdf with links]
A Perspective; 2002-2012
from Inception in 2002 to Proofs of Concept and Impending Clinical Applications by 2012
Illumina Stock Leaps On Roche Acquisition Reports
Thu, Dec 20 2012 00:00:00 E 00_WEB
By Kevin Shalvey, Investor's Business Daily
Posted 11:50 AM ET
Genome research firm Illumina's (ILMN) shares were up 7% in early trading Thursday, near 55.75, on reports that Swiss pharmaceuticals company Roche Holding (RHHBY) has agreed to acquire the company.
As first reported in Swiss newspaper L'Agefi, Roche might have already agreed to buy Illumina for $66 per share, which would put the purchase price at more than $8 billion. The story was picked up Thursday by Bloomberg and Reuters. Alan Hippe, Roche chief financial officer, says his company is eyeing acquisitions, Bloomberg reported. [Note the plural in acqusitionS. One wonders how quickly the Land Grab will propagate from consolidating Sequencing to major purchases of Analytics IP - Pellionisz]
Shares of Illumina were trading at 16-month highs, though this isn't the first takeover report for the gene-sequencing firm.
The agreement is said to have happened last week, reports say.
Rumors of the possible acquisition have been bouncing around Wall Street for a few weeks, as had a rumor that GlaxoSmithKline (GSK) might have been interested in buying Illumina, as IBD reported. ["Pharma must go Genomics". After Merck (with BGI) and Roche (with Genentech and now it looks, Illumina), GlaxoSmithKline might well be a component in "Party J". With Sequencing consolidated, the Next Big Thing is Analytics - Pellionisz]
Fractal Organization of the Human T Cell Repertoire in Health and Following Stem Cell Transplantation
Jeremy Meier1*, Allison F Hazlett, MS1*, Kassi Avent, MS2*, Jennifer Berrie2*, Kyle Payne2*, David Hamm, MS3*, Cindy Desmarais, PhD3*, Catherine Sanders, PhD3*, Kevin T Hogan, PhD4*, Steven Grant, MD5, Kellie J Archer, PhD6*, Masoud H Manjili, DVM, PhD2*, Catherine Roberts, PhD1* and Amir A Toor, MD1*
1Internal Medicine, Virginia Commonwealth University, Massey Cancer Center, BMT Program, Richmond, VA
2Microbiology & Immunology, Virginia Commonwealth University, Massey Cancer Center, Richmond, VA
3Adaptive Biotechnologies, Seattle, WA
4Massey Cancer Center, Richmond, VA
5Virginia Commonwealth University, Massey Cancer Center, Richmond, VA
6Biostatistics, Virginia Commonwealth University, Massey Cancer Center, Richmond, VA
T cell repertoire diversity is generated by recombination of variable (V), diversity (D) and joining (J) segments in the T cell receptor (TCR) locus. Further variability and antigen recognition capacity is introduced by nucleotide insertion (NI) in the recombined sequences resulting in a complex repertoire, the organization of which is poorly understood. We postulate that TCR b D, J and V gene segment usage in an individual would result in a TCR repertoire with a fractal, self-similar frequency distribution of T cell clones with respect to gene segment usage. To determine this, the TCR repertoire of donors and recipients of HLA matched-related and unrelated allogeneic stem cell transplantation (SCT) was evaluated by high-throughput (HT) sequencing of the CDR3 region of TCR b.
Ten SCT donor-recipient pairs were selected for HT-TCR b sequencing. cDNA was isolated from T cells obtained from the donors at baseline and recipients at day 100, 1 year post SCT or at the time of graft-versus-host disease (GVHD) diagnosis. HT-TCR β sequencing was performed and analyzed using the ImmunoSEQ analyzer tool (Adaptive Biotechnologies, Seattle, WA). TCR b clone frequencies were used to determine the TCR self-similarity score (SSS = log clone frequency x log scale), evaluating clonal frequency at each gene segment-scale (J, VJ and VJ+NI). This revealed a TCR SSS with a relatively narrow distribution of 1.64 ± 0.1 (mean ± SD) for J, 1.69 ± 0.2 for VJ, and 1.41 ± 0.01 for VJ+NI, which was consistent between all normal stem cell donors analyzed.
Relative proportional distribution (RPD) graphs were then generated to allow for the depiction of the TCR b D, J, V distribution for simultaneous comparison at an individual level. Representative data in Figure 1 shows the relatedness across donors at the TCR b J segment level. Relative clonal frequency was determined for each unique TCR clone at the specified segment level and plotted according to frequency-rank. Slope of the resulting linear regression lines from log-log plots of rank-frequency was used to determine self-similarity and fractal dimension. These plots were comparable among donors with resulting slopes of 1.6 ± 0.01 and 1.8 ± 0.1, respectively for TCR J and VJ containing clones (Figure 2). The plots also revealed a hierarchy of T cell clones, with few dominant clones occupying the high ranks and a multitude of clones in the later ranks.
For donor-recipient comparisons, we examined the dominant ranking clones which would be most likely to be involved in GVHD and response to infection. The ordered TCR clonal frequency distribution seen in donors was perturbed in recipients following SCT, with recipients demonstrating a lower level of complexity in their TCR repertoire, and a large shift in the frequency distribution of the dominant T cell clones compared to the donor. When dominant clones were compared between donors and recipients, recipients shared only a small proportion of TCR clonotypes with their donors despite full donor T cell chimerism. This difference did not change over time, suggesting an alternate T cell clonal hierarchy and repertoire develops in transplant recipients when compared with their donors.
Using simple mathematical analysis, we demonstrate that the TCR b repertoire has a fractal, self-similar pattern with a hierarchy of dominant and minor clones. We note that the complexity of the TCR repertoire is diminished and TCR b hierarchy is altered following SCT. Restoration of this order may serve as a marker for post-SCT immune reconstitution. Further, by demonstrating shifts in TCR clonal dominance, fractal analysis comparing donor and recipient T cell repertoire may allow for more accurate monitoring of immunotherapy of malignancies in general, beyond allogeneic SCT.
There is an increasingly heated-up nation-wide speculation, reaching far into the unforeseeable future, about potential security threats to the USA with Industrialization of Genomics.
We have seen such dire times before. When nuclear industry emerged from the disruptive science of quantum mechanics to strategically decisive applications pertaining to energy, the US responded by securing nearly all the intellect from the World that could, and did, make the difference.
Some futuristic scenarios aside, global players may wish to address economy as an immediate issue.
As genomics has turned into a globally competitive industry, by a land-rush to acquire know-how can and will, as Juan Enriquez predicted over a decade ago, gain dominance over others by gobbling up intellectual property (along with the purchase of assigned IP, or even easier if such is presently un-assigned). Thus, using the leverage of cash in the buy/build classic dilemma, the luckiest parties could gain an extra profit by collecting royalties over the sweat and blood of creating wealth in the first place.
When a Silicon Valley company is acquired, the patent-portfolio assigned to the company is one of the greatest assets. Un-assigned personal IP is perhaps the singularly best investment for security. - Andras Pellionisz
2012 After a Decade of Uncertainty, Genomics is at Crossroads
As calendar year 2012 is wrapping up in a few days, it seems clear that after a "Decade of Uncertainty" (2002-2012) Genomics reached sevenfold crossroads.
1) Old School versus Informatics Science. The most dramatic turn-around for any disruptive technology happens when (see "energy") the underlying science (see "nuclear physics") reaches a change of axioms compelling for science leadership. Comparably to earlier examples, Industrialization of Genomics started with Genentech (based on the "gene" [and Junk]), interpreted by the oversimplified Central Dogma of a DNA>RNA>Protein "arrow model"). However, by September 2012 the ENCODE program concluded for the second time after 2007 that "the scientific community must re-think long held beliefs", calling for a holistic approach to Genomics/Epigenomics/Informatics, see the perspective given by the .pdf compilation The FractoGene Decade. Estimating from the fact that genomics took half a decade (2007-2012) to consolidate a conceptual paradigm-shift from "Junk" to "Function", it is expected that The Principle of Recursive Genome Function (2008) that makes mathematical sense why the fractal folding of DNA structure must provide "functional proximity" in a massively parallel recursion (thus unites the fractal folding structure with the fractal recursive iteration function) might also take half a decade to be fully appreciated by 2013.
2) Sequencing versus Analytics. In 2012 it became evident that pure sequencing supply-business goes bankrupt without the market-pull through analytics. The glut is not confined to any single company - it is characteristical to the entire industrial genomics.
3) Analytics by Hand versus by Computers. Haussler (UCSC) says "In the minute it took me to walk on stage, another person in the U.S. died of cancer. We’ve reduced the death rate from heart disease by about 70 percent over the last 50 years; cancer, only about 10 percent. In the next two years, cancer will replace heart disease as a number one killer in the United States. The white coats have done their best, but we actually need the computer geeks to get involved at this point. We need you, we need your creativity, we need your drive. Why is it the geeks’ turn? Cancer is a digital disease, and it will have a digital cure. Cancer is caused by mutations created by a few cells in your body, causing them to grow in uncontrolled fashion. There are thousands of different types of mutations that occur in a huge number of different combinations. It’s not comprehensible by an unaided human mind, but it would be with the power of a computer-aided analysis". The call for automated analytics by arguably the most qualified creator of "genome browser" is contrasted by Tina Hambuch (Illumina), who says "Illumina is now offering the interpretation of the 344 genes as an option for its Individual Genome Sequencing service, and is able to customize that gene set somewhat on a case-by-case basis...although there are many software tools to aid with steps in the interpretation process, the final assessment of weighing the evidence will be difficult to automate. "There really just isn't a software that can do that at this point in time," Hambuch said. "And in fact, that's not even easy for people to do. We actually did have this team of people, and we would really argue about these things. Some of these are not easy to decide...The analysis required about 19 hours of manual review per genome". To a professional informatics expert, in full agreement with Dave Haussler, scaling of manual analysis of 344 genes (out of about 20,000) to potentially the entire human genome (6.2 million thousands of A,C,T,G-s, massively changing e.g. in the course of cancer/chemo) appears eminently untenable.
4) Analytics must be able to parse parametric (human diversity) "structural variants" from syntax-errors (fractal defects, directly linked to pathology). Ample evidence of an enigmatic wealth of "structural variants" (both very large, such as Copy Number Variations/Alterations (CNV/CNA), or ultimately small such as a Single Nucleotide Polymorphism (SNP) - and every size of repeats in between - renders either manual or computer-aided analytics an exercise of futility unless and until a theoretical (mathematicsl) understanding how to parse them apart is available. Presently, FractoGene is a leading candidate of an algorithmic parsing of myriads of structural variants, see particularly the outline of its global scalable clinical applications (2012).
5) Academia versus Industry. Traditionally, disruptive research & technology inventions have been incubated in Academia (see Internet language of UNIX by Bill Joy, browser by Marc Andressen, search engine by Sergey Brin and Larry Page, etc, etc) - entreprises that quickly outgrew their University home (Sun, Netscape, Google, etc). A major new challenge is that the traditional powerhouses of academic research (NIH in the USA, ENCODE support in UK/EU) experience constraints. Fortunately, just as the Internet transitioned from Academia/Government to Private Domain (1994), the entire DNA Sequencing industry in already in Private Domain, plus SAMSUNG for Analytics marked by Sept. 1st, 2011 a similar transition, though major Academic Centers (Stanford, Cold Spring Harbor, etc) continue to pave way to major advances.
6) National versus Global. USA versus China, with India and Russia "sleeping giants", Korea and Brazil poised to jump.
7) Free versus IP, as early Internet, or Intellectual Property as in Google/Microsoft/Apple/Samsung
Genome Data Analysis, San Francisco 27-29, 2012
Have you cracked the data bottleneck? [See my 2-cents worth at it in paper and google-tech-talk, 2008, 2002 utility secured Oct. 2nd, 2012, write-up Nov. 1st, 2012 and 2013 - AJP]
You’ll know the cost of analysis can outweigh cost of sequencing by 10 to 1, which is a huge headache for anyone trying to actually understand genome information. Answering the fundamental challenges of how to effectively store, transfer and interpret sequencing data will be key to fully unlock the meaningful information contained inside the genome.
These challenges are universally recognized but as yet there are no clear cut answers as to how to overcome this expensive hurdle. Bringing together the world’s brightest minds in data analysis, the World Genome Data Analysis Summit will, for the first time ever, create a truly unique forum where these issues can be addressed.
Featuring cutting-edge case studies from the likes of Roche, BMS, Pfizer, GSK, Sanger Institute, Washington University and many others, you’ll gain access to the latest strategies to control, analyze and interpret your genomic data. Discover new software tools to help efficient transfer of large data volumes, uncover cloud storage solutions to manage data warehousing and hear the industry leaders explain how they’re managing to achieve effective high throughput analysis of their genome sequencing.
[Waiting (busily) for the Next Meeting (Software-enabling approaches to Recursive Genome Function) one wonders if it is an accident that exactly at the time of the SF meeting a third, as yet un-named, "Party J" approached Complete Genomics, hopefully not just to sequence, store DNA data, but also with algorithmic understanding to put the analytics into clinical applications, most notably against cancer? - AJP]
Architecture Reveals Genome’s Secrets
Three-dimensional genome maps are leading to a deeper understanding of how the genome’s form influences its function.
By Sabrina Richards | November 25, 2012
Genome sequencing projects have provided rich troves of information about stretches of DNA that regulate gene expression, as well as how different genetic sequences contribute to health and disease. But these studies misses a key element of the genome—its spatial organization—which has long been recognized as an important regulator of gene expression. Regulatory elements often lie thousands of base pairs away from their target genes, and recent technological advances are allowing scientists to begin examining how distant chromosome locations interact inside a nucleus. The creation and function of 3-D genome organization, some say, is the next frontier of genetics.
Genome spatial organization is critical for gene regulation, explained Job Dekker, a molecular geneticist at the University of Massachusetts Medical School, and “everything else chromosomes do involves three dimensions,” as well. Chromosomes have to replicate, separate properly during division, and change shape during the cell cycle—all without tangling. The genome is “rebuilt entirely after cell division,” Dekker said.
The mechanisms for such delicate orchestration have remained unclear, however. About 10 years ago—just as the human genome project was completing its first draft sequence—Dekker pioneered a new technique, called chromosome conformation capture (C3) that allowed researchers to get a glimpse of how chromosomes are arranged relative to each other in the nucleus. The technique relies on the physical cross-linking of chromosomal regions that lie in close proximity to one another. The regions are then sequenced to identify which regions have been cross-linked. In 2009, using a high throughput version of this basic method, called HiC, Dekker and his collaborators discovered that the human genome appears to adopt a “fractal globule” conformation—a manner of crumpling without knotting.
In the last 3 years, Dekker and others have advanced technology even further, allowing them to paint a more refined picture of how the genome folds—and how this influences gene expression and disease states.
Conversing chromosomes
Dekker’s 2009 findings were a breakthrough in modeling genome folding, but the resolution—about 1 million base pairs—was too crude to allow scientists to really understand how genes interacted with specific regulatory elements. More detail was needed to understand how cells know which areas of the genome “should be talking [to each other], and which shouldn’t,” said Dekker. After all, “you don’t want everybody talking to each other; you want [your genome] to have a decent conversation.”
Recent advances in deep sequencing are now providing researchers with a way to glean that detail. Dekker and his colleagues discovered, for example, that chromosomes can be divided into folding domains—megabase-long segments within which genes and regulatory elements associate more often with one another than with other chromosome sections. The DNA forms loops within the domains that bring a gene into close proximity with a specific regulatory element at a distant location along the chromosome. Another group, that of molecular biologist Bing Ren at the University of California, San Diego, published a similar finding in the same issue of Nature.
Between the two groups, the researchers identified these domains in mouse and human embryonic stem cells and human fibroblasts, suggesting that they are “a fundamental property of the genome,” Ren said. Additionally, both groups found that deleting boundary sections of domains threw gene regulation into disarray, causing previously silent genes to be transcribed and vice versa. These results demonstrate that “domain structure is essential to keep the gene program tightly regulated,” said Ren.
“I think the discovery of [folding] domains will be one of the most fundamental [genetics] discoveries of the last 10 years,” Dekker said. The big questions now are how these domains are formed, and what determines which elements are looped into proximity.
Chromosomes and cancer
In addition to its effect on gene regulation, chromosome folding may also play a role in cancer development. Somatic copy number alterations (SCNAs), or the deletion or amplification of genes, are a hallmark of cancer’s genomic instability. Leonid Mirny’s lab at Massachusetts Institute of Technology, who collaborated with Dekker on the 2009 discovery of “fractal globules,” found that the genome’s loops contribute to the formation of particular SCNAs. Comparing SCNA maps to the 3-D architectures of human cancer genomes, Mirny and colleagues found that genomic regions that formed the ends of the loop—and were therefore in close physical proximity—are likely to be boundaries where the intervening section is deleted or amplified, creating SCNAs.
Translocations, or the abnormal arrangements of chromosome sections, are another hallmark of certain cancers, and also seem to be facilitated by spatial organization. Mirny and his group found that the break points for two well-known translocations—Bcr-Abl in chronic myelogenous leukemia and between Myc and immunoglobulin genes in Burkitt’s lymphoma—are frequently found near each other in normal cells, and especially in cells of the lineages prone to these tumor types.
Tissue-specific differences in translocations may point to subtle differences in genome organization based on cell type. “Presumably in different tissue types there are specifics of the genome organization that we have yet to discover,” said Mirny. How differences in chromosome organization are achieved is another ripe area for study, said Dekker. For example, chromosome folding domains could be determined by some sort of marker in the cell, depending on type and environmental factors. “If boundaries to domains are flexible—turning on or off by cell type—suddenly genes have access to a whole new set of regulatory elements,” Dekker speculated.
In addition to better understanding cancer, chromosome folding may help predict it. As normal cells transition into tumor cells, genes can change their spatial organization in characteristic ways, said Tom Misteli, a cell biologist at the National Cancer Institute at the National Institutes of Health who is using the genome’s 3-D architecture to develop diagnostic tools. His team has shown that in breast cancer, certain genes “change position dramatically as their cells transform into cancer cells,” allowing Misteli and his colleagues to “look at an unknown tissue biopsy, localize genes, and with high accuracy determine whether its cancer or normal,” he said.
[To paraphrase a classic wisdom of biology, it appears that "Nothing makes as much sense about the genome if not interpreted from the viewpoint of principle of recursive genome function (2008)". Given a (Hilbert-fractal) structure of the genome, that enables "functional proximity" of physically distant sections it would be difficult to explain how "they are talking to one-another", if functional recursion (stated as "fractal iterative recursion") could not be formulated into a software-enabling approach. The fractal stages of development of a neuron (Pellionisz, 1989) assumed that the genome is revisited (a double heresy since proteins were not supposed to recurse to DNA information, moreover such recursion would make no sense if the revisited sequences were void of information, would be Junk DNA). Similarly, at an early stage, Grosberg (1993) promulgated the fractal folding of DNA-strand. From the former approach FractoGene emerged (2002-2012), from the latter the Hilbert-fractal was invoked (2009). An overview of Fractal Defects Clogging Recursive Genome Function (linked to cancerous mis-regulation) was provided recently (see .pdf above in full, Pellionisz, 2012), concept illustrated below]
Fractal defect of CNV clogs genome function recursion in Hilbert-curve [.pdf here]
Thanksgiving - having counted our blessings it is time to address the "horror vacui"
Having counted all our ample blessings, also with genomics, it may be time to face a historical challenge. After a "Decade of Uncertainty" in Genomics (2002-2012) with the first conclusion of ENCODE-2007, finally confirmed by a hesitant bulk of ~350 top scientists in ENCODE-2012, a "vacuum exists in general acceptance of software-enabling systemic understanding of genome function and (mis)regulation". Since science abhors any conceptual vacuum, the "horror vacui" have urged pioneers to fill the void. By now efforts towards a deeper understanding are widening into to a mainstream of hard-core science & technology branch. In an avalanche, "tomorrow" it might even be taken for granted (though formerly granting was outright denied).
Perhaps the most succint summation to characterize the "Decade of Uncertainty" is by Venter (2010) "We're at a frighteningly unsophisticated level of genome interpretation"
Such summary statement is probably meant for the rapidly dwindling (yet still not zero) minority of old-schooler workers who somehow omitted to perceive the collapse of the intellectual prison of "gene and junk, further constrained by the prohibition of feedback by some central dogmatic misnomers".
Dozens of independent experimental Proof of Concept papers have been reviewed recently (proceedings is available upon request).
Those wishing to be listed as proponents of hologenomics, both in the sense that the whole DNA might require analysis of some function (beyond "genes" that do not even have now a universally accepted single definition...) and that the whole genome/epigenome requires a system-approach, email Pellionisz at the address of holgentech_at_gmail_dot_com. Names and affiliation will be listed on HoloGenomics.com site.
Formula Unlocks Secrets of Cauliflower's Geometry
ScienceDaily (Oct. 23, 2012) — The laws that govern how intricate surface patterns, such as those found in the cauliflower, develop over time have been described, for the first time, by a group of European researchers.
In a study published October 24, in the Institute of Physics and German Physical Society's New Journal of Physics, researchers have provided a mathematical formula to describe the processes that dictate how cauliflower-like patterns -- a type of fractal pattern -- form and develop.
The term fractal defines a pattern that, when you take a small part of it, looks similar, although perhaps not identical, to its full structure. For example, the leaf of a fern tree resembles the full plant and a river's tributary resembles the shape of the river itself.
Nature is full of fractal patterns; they can be seen in clouds, lightning bolts, crystals, snowflakes, mountains, and blood vessels. The fractal pattern of the cauliflower plant is ubiquitous and can be spotted in numerous living and non-living systems.
The properties of fractals, such as their shapes, sizes and relative positions, have been studied extensively; however, little is known about the processes involved in their formation.
To identify this, the researchers, from Comillas Pontifical University, Universidad Carlos III de Madrid, Instituto de Ciencia de Materiales-CSIC, École Polytechnique and Katholieke Universiteit Leuven, firstly grew thin films using a technique known as chemical vapour deposition (CVD).
CVD is a technique used to grow a solid, in which a substrate is exposed to a number of precursors that react and/or decompose on its surface to create a specific thin film. The researchers tailored the CVD process so the film would grow into shapes similar to those seen on a cauliflower, but limited to the submicron scales.
From this the researchers were able to derive the formula which described how the cauliflower-like patterns develop over time. They proved that the formula was able to successfully predict the final cauliflower-like patterns by comparing them to actual cauliflower plants and combustion fronts, both of which occur at much larger scales.
Co-author of the paper, Mario Castro, said: "In spite of the widespread success of fractal geometry to describe natural and artificial fractal shapes, purely geometrical descriptions do not provide insight into the laws that govern the emergence of the shapes in time.
"We believe that by knowing the general laws that dictate how these patterns form and grow, it will help to identify the biological and physical mechanisms that are at play."
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| Cauliflower. The laws that govern how intricate surface patterns, such as those found in the cauliflower, develop over time have been described, for the first time. (Credit: © Africa Studio / Fotolia) |
Cauliflower Romanesca, with the intricate structural pattern, described as governed by the fractal (recursive iteration) of genome function (Pellionisz, 2008) | |
[Mario Castro and colleagues properly describe the physics of the surface of cauliflower - "a cabbage with college education", said Mark Twain. Their "first time" article, however, does not even mention "genome" (or "DNA"). The challenge as a result of their great work, therefore is very straightforward - once the DNA of the cauliflower romanesco is available, preferably along with the full DNA of cabbage. With the FractoGene utility, in 2007 CIP specifically illustrating "cauliflower romanesco" it will be revealed how its fractal genome governs the fractal growth. Since fractal theory is, in full generality, is now out in the open (Alexei Kurakin) and he also claims that e.g. the "C-value enigma" is just an epiphenomenon, time seems not too far to understand "college education" of cauliflower over less repetitively schooled cabbage in genomic terms - Pellionisz]
Szentagothai Centenary Tribute in New York City
Janos Szentagothai, who carried the legacy of structure of CNS neural networks from Ramon y Cajal to the function of modern neural net theories (eventually leading to a geometric unification of neuroscience and genomics), was celebrated in the Consulate of Hungary in New York City, November 12, 2012.
Software developers analyzing patterns to boost odds against cancer
Software engineers are moving to the fore in the war on cancer, designing programmes that sift genetic sequencing data at lightning speed and minimal cost to identify patterns in tumors that could lead to the next medical breakthrough.
Their analysis aims to pinpoint the mutations in our genetic code that drive cancers as diverse as breast, ovarian and bowel. The more precise their work is, the better the chance of developing an effective new drug.
Ever since James Watson and Francis Crick discovered the structure of DNA in 1953, scientists have been puzzling over how genes make us who we are. The confluence of computing and medicine is accelerating the pace of genetic research.
But making sense of the swathes of data has become a logjam.
That, in turn has created an opportunity for computer geeks and tech firms such as Microsoft, SAP and Amazon.
Oncology is the largest area of therapy in the global drugs market with market researcher IMS predicting it will increase to $83-$88 billion by 2016 from $62 billion in 2011. Computational genomics - using computers to decipher a person's genetic instructions and the mutations in cancerous cells - is emerging as the driver of this growth.
Life Technologies Corp and Illumina Inc are among firms developing equipment that can extract a person's entire genetic code - their genome - from a cell sample.
The newest machines are about the size of an office printer and can sequence a genome in a day, compared with six to eight weeks a few years ago. They can read the 3.2 billion chemical "bases" that make up the human genetic code for $1,000, compared with $100,000 dollars in 2008.
Growing numbers of software engineers are needed to help make sense of all this data.
"Many labs can now generate the data but fewer people or labs have the expertise and infrastructure to analyze it - this is becoming the bottleneck," said Gad Getz, who heads the Cancer Genome Analysis group at the Broad Institute in Boston, jointly run by MIT and Harvard.
Getz is one of a new generation of computational biologists who develop algorithms to parse data from tens of thousands of cell samples, shared with research institutes around the globe.
He and his team of 30 are trying to establish recurring patterns in the mutations and how they are linked to tumor growth. They are using some 1,200 processing units, each with 4-8 gigabytes of random access memory - about the computing power that comes with most desktop PCs.
Harvesting knowledge
Eli Lilly CEO John Lechleiter sees potential for progress.
"We are starting to harvest the knowledge that we gained through the sequencing of the human genome, our understanding of human genetics, disease pathways. We've got new tools that we can use in the laboratory to help us get to an answer much, much faster," said Lechleiter, whose firm is co-owner of the rights to bowel cancer drug Erbitux.
Approved drugs that take genetic information into account include Amgen's Vectibix and AstraZeneca's Iressa. But both these drugs derive from a single mutation. Sequencing has laid bare many more mutant genes - often hundreds in any given tumor - and highlighted the need for a subtler approach to cancer treatment.
Roche, the world's largest maker of cancer medicines, has spent several million euros on information technology for a pilot scheme examining how cancer cells in petri dishes react to new drugs. The scheme involves crunching hundreds of terabytes of gene sequences.
"It's the first large-scale in-house sequencing project for Roche and we expect more to follow in the near future," said Bryn Roberts, Roche's head of informatics in drug research and early development.
Roberts said the project, which uses processing power equivalent to hundreds of high-end desktop PCs, was self contained but there were plans to draw in external data. This would require advances in cloud computing - using software and computing power from remote data centers - but Roberts said the technology would soon be available.
"The scale of the problem means the solution will be on an international collaborative scale," he said.
Opportunities in clouds
The trend of using cloud computing networks to allow commercial and public researchers to share cancer data is promising for the likes of IBM and Google which according to GBI Research are already established providers of cloud computing to drug makers' research efforts.
Amazon, with its cloud computing unit AWS, said it is benefiting as life science researchers rethink how data is stored, analyzed and shared. "We are happy with the growth we are seeing," a spokesman said, declining to provide figures.
Microsoft said it was dedicating "significant resources" to the expansion of cloud computing in the health and life sciences markets.
"Pharma R&D will be working with other technology companies, like Microsoft, in developing new algorithms, methodologies and indeed even therapies themselves," said Les Jordan, chief technology strategist at Microsoft's Life Sciences unit.
The world's largest business software company SAP has teamed up with German genetic testing specialist Qiagen. They are modifying SAP database software so that certain cancer diagnostic tests, which now keep a network of super computers busy for days, can be run on a desktop PC within hours.
Genetic analysis has revealed that types of cancer, now treated as one because they are in the same organ and look the same under the microscope, are driven by different genetics.
Hans Lehrach at the Max Planck Institute for Molecular Genetics in Berlin says every single tumor should be seen as an "orphan disease", using a term for rare illnesses that typically prompt drug regulators to make drug approval easier.
He has designed a software he describes as a virtual patient. It suggests a drug or a mix of drugs based on each tumor's genetic fingerprint. A single case can take several days to be processed.
Lehrach, a geneticist who says he has written software code throughout his scientific career, likens his approach to that of a meteorologist who regards every day's set of readings as unique.
Taking the analogy further, he says the convention of stratifying cancer patients is equivalent to a weather forecast based on simple rules such as 'red sky in the morning, sailor take warning'.
At a unit of Berlin's Charite university hospital, 20 patients left with no other treatment options for their aggressive type of skin cancer are being diagnosed based on Lehrach's computer model.
The trial is exploratory and there are no results yet on the overall treatment success, but the project, like many others, is driven by the hope that cancer can be wrestled down by sheer computing power.
[“With time a Microsoft or Google type IT will acquire – or build – an IT-led pharma.” - said a pharma-guru, cited here. What software-enabling approach to genome (mis)regulation will IT use?]
Genomics Industrialized. Patents Drive Innovation
[Genomeweb lists, for a fee, a number of issued patents in genome informatics. It may be a signal of the new times of genome informatics (very much like formerly the Internet), that from a largely "government-subsidized research and development" this boom is also turning into an industry on its own. Since the Genomeweb-list of patents is "for fee", it can not be reproduced here. Suffice to note that FractoGene, issued to Pellionisz is on the list (Fractal genome governs fractal organisms).
There may be an opinion that the emergence of industrialized genomics, ruled by traditional business interests, might not be a good thing - a topic certainly worth discussing (Twitter @holgentech or Andras Pellionisz on FaceBook).
This columnists makes a point that innovations can be extrordinarily taxing (for a long time now, developing a new drug costs fortunes, and if the innovation can not be protected, the drive to develop new drugs disappears). Likewise, paradigm-shift innovations need to be rewarded - otherwise genomics might be suffocated to remain "data-development R&D" that in itself may not lead to clinical applications, see Thomas Kuhn's classic book "The Structure of Scientific Revolutions" - Pellionisz]
FractoGene Emerges in a Global, Scalable Business Model, with Protected IP and an Avalanche of Independent Experimental Proof of Concept Results
[Cover page, for full Abstract and References of the avalanche of Proof of Concept results, click here]
Fractal Defects in the genome, repeat structural variants with their largest example of Copy Number Variations
clog the functional transparency of the Hilbert-fractal of DNA. Clinical applications 'focus like a laser-beam' on
diagnosis by geometric analysis, exact measurement of the efficacy of therapies, as well as drug-targetable neutralization
of Fractal Defects
FractoGene Patent Licensed to Seven in Southern California in its First Week
FractoGene Patent 8,280,641 was licensed to seven in Southern California in its first week after issue. Dr. Pellionisz, with his cross-domain expertise of Ph.D-s. in computer engineering, biology and physics is committed to the success of efforts. As expressed in the Press Release (below), there is also a great interest from BRIC countries to obtain precious access to USA markets in genome informatics (see e.g. news of the Chinese BGI offer to acquire Complete Genomics in the middle of California Silicon Valley at depressed prices of $117 M after it sustained a $500 M loss in valuation; the BGI transaction is presently litigated). For FractoGene Patent 8,280,641 an expert in international licencing is wanted. The preferred candidate represents an entity proven in the type of opportunity ("jobs").
US Patent Office Issues FractoGene Patent to HolGenTech Founder Pellionisz
After PRWeb - October 2nd, 2012
HolGenTech Inc., a pioneer in genome analytics, announces that the USPTO issued patent No. 8,280,641 for a “Utility of Genomic Fractals Resulting in Fractals of Organisms” to founder Dr. Andras Pellionisz.
SUNNYVALE, CALIFORNIA (PRWEB) October 02, 2012
US Patent Office Issues FractoGene Patent
Recognizes breakthrough research; validates business model
This method and system is critical to the application of industrial genomics in clinical settings, most especially in the fight against cancer. The computation of genomic fractal defects can parse individual diversity from pathology, and thus represents a quantum leap in early diagnosis, personal therapy, and genome-based drug development.
click to enlarge
Pursuant to decades of research in applying mathematics to neuroscience and genomics, Pellionisz, who has three doctoral degrees, submitted his provisional application on August 1st, 2002. Recognition by the US government may well have been delayed due to its cross-disciplinary nature. The greatest hurdle was most likely the required paradigm shift - moving from the “Junk DNA” model to a fractal iterative recursion paradigm, which was initially perceived by many as a “lucid heresy.”
The status quo began to give way a few years ago, beginning with the results from the ENCODE Project, when in 2007 its leader Francis Collins urged the scientific community to “re-think long held fundamentals.” Such progress was made in Pellionisz’s presentation at George Church’s meeting in Cold Spring Harbor, Sept. 16, 2009. Another landmark was the publication by Eric Lander et al. in the Oct. 9, 2009 of Science, featuring the Hilbert-fractal of a genome on the cover.
“A protracted period of examination was costly and occasionally painful, but the issue could not be better timed for deployment,” said Pellionisz. He was delighted that legal recognition arrived via an “Issue Notification,” dated September 12, 2012. It was a heady time. The notice came less than a week after the release of 30+ papers from ENCODE on September 6th, 2012 - again concluding that “Junk DNA” was a myth.
Progress accelerated in 2008, when Pellionisz disseminated his new approach in a peer-reviewed publication on “The Principle of Recursive Genome Function.” He was also the guest speaker that year for a Google Tech Talk entitled “Is IT Ready for the Dreaded DNA Data Deluge?”
As the old school of thought has finally come to an end, a new program based on recursive fractal iteration can proceed at full steam.
Pellionisz was also decorated in Hyderabad, India on the anniversary of the first decade of the “eureka moment of FractoGene” on February 15, 2012. There he announced the first independent experimental proof of concept, showing that fractal defects are implicated in cancer. He also accurately predicted that within six months about half a dozen further confirmations were likely to appear.
“Dr. Andras Pellionisz’s openness to involving the subcontinent in a global transformation of genome informatics for his fair share lends a remarkable opportunity to India,” stated Prof. Erithrean Rajan, Director of the Pentagram Institute, who invited Pellionisz to a Three-State Guest of Honor Keynote Speech and lecture tour. Prof. Rajan writes:
"The Big Picture that Pellionisz is promoting for India lends a special opportunity for Bharat to catch up to China, where the Beijing Genome Institute (BGI) in Shanzhen pursues genomic syndromes in a strategic relationship with Merck Beijing, with sequencing, analytics and clinical trials pursued abroad by the USA to optimize economic feasibility by outsourcing."
Prof. Rajan’s comments are timely, given that BGI tendered an offer to acquire Complete Genomics at a depressed price once it sustained a $520 million loss of valuation due to the predicted “Dreaded DNA Data Deluge.” Pharma expert Dr. Karoly Nikolich quoted Ernst & Young in their 2009 Churchill Club YouTube video [at 1:04:30], “With time a Microsoft or Google type IT will acquire - or build - an IT-led pharma.”
The algorithmic approach to fractal recursive genome function appears to be perfectly timed, given the recently announced $3 billion “moon shot” cancer program of M.D. Anderson. Underscoring the vital importance of his invention, Pellionisz points to the hundreds of millions of people gravely suffering from genome regulation syndromes amidst global economic hardship.
Pellionisz is delighted to be among those who have lived to witness their scientific paradigm-shift come to pass and bear fruit in practical applications.
Dr. Pellionisz can be reached at holgentech [at] gmail.com
[This announcement can be commented on the FaceBook of Andras Pellionisz and @holgentech on Twitter]
(Sep 06-15) ENCODE Gets out from a 40-Year Dead-End; up to a New Era of Global Industrial Genomics
On September 6, 2012 the "official" Nature lead-article (along with 30-40 academic papers and ~500 general media-outlets) marked Ohno's "Junk DNA" (for 98.7% of the human DNA there "for the importance of doing nothing" see verbatim quote from the facsimile) not only a closed chapter, but virtually the entire R&D Establishment (80 Institutes and Companies with about 350 leading authors) finally arrived at the same conclusion as the ENCODE architect over 5 years ago: "the scientific community will need to rethink some long-held views" (Francis Collins on June 13, 2007).
Possible reasons for revisiting with so much PR a 5 year old conclusion?
Plenty. The simplest may be that in spite of the strong voice of conclusion by Francis Collins, for five years many were hoping against hope that perhaps the 35 year young "Junk DNA" misnomer was not quite dead yet (dying from a thousand wounds, RIP at 40). For instance, the leader of "ENCODE post-mortem era" Ewan Birney lost "a case of vintage champagne" with his 2007 bet with John Mattick - well masked by 30+ top papers. In a much starker legal sense, the long list of hundreds of researchers can be seen as an indemnification, rendering harmless the part of community that in a class action could be found negligent, overlooking 98.7% of human DNA in some long list of diseases; see Lee Silver's "Annus Mirabilis" article omitted from Newsweek, perhaps for the same reason, from the USA/Canada edition (the facsimile is from the European Edition), though it went to print in the other 3 editions.
In a scientific sense, Francis Collins' call "to re-think" was immediately answered by Pellionisz' "The Principle of Recursive Genome Function" (widely disseminated in his Google Tech Talk YouTube, both in 2008), since those who never believed either the "Junk DNA" or "Central Dogma" mistaken "axioms" did not have to "re-think" PLUS have been ready for years with the replacement-paradigm (see FractoGene since 2002).
In the sense of many $Bn losses, major sequencing companies lost the lion-share of their valuation due to the predicted "Dreaded DNA Data Deluge" - investors & industries perhaps should have listened (in addition to the 15,000+ viewers who viewed, but perhaps with some disbelief).
Much more sobering may be that the global balance of the emerging Industrial Genomics may have changed as a result of 4-5 years of delays & mis-steps. R&D in Europe (symbolized by Ewan Birney) and in the US (symbolized by the looming 10%+ cut of the budget of Francis Collins' NIH) appear to stumble, while (symbolized by BGI bid to acquire Complete Genomics for a mere $117 M, a loss of about half a billion dollars) China may catapult based on Genomics Industrialized, perhaps upsetting even the Asian balance (unless India, Korea or Japan launches a competitive initiative).
What Went Wrong and When? Was the Misfiring of a Mega-Billion Dollar Business Inevitable?
Forbes Analyst: The Better Desktop DNA Sequencer May Be Losing The Marketing War
[Matthew Herper, Expert Analyst of the Forbes staff puts his light touches on a delicate subject, picturing what an anonymous commenter bluntly terms as "the market is ... going to crash down" from the angle of a "marketing war". True that the two (not pure-play) sequencer companies of Illumina versus Life Technologies. Fig. 1 (Estimated quarterly units shipped) could, indeed, be seen as some "marketing war" (with one firm with more muscle and shoe leather slightly outdoing the other) - but the numbers (columns, rather) reveal a more serious condition of the (predictable and predicted) unsustainability of mega-billion dollar sequencing business. While in just one quarter one firm outdid the other (with marketing hype reminescent of Happy Meal commercials...) the columns show that both companies actually sell less units than a mere few quarters ago - though full-range companies that do not focus on "pure play sequencing" can afford to mask the crisis better than "pure-play" Pacific Biosciences (trading today at $1.85) or Complete Genomics (today at $2.51) - while 18 months ago both were above $15 per share. - AJP]
Anonymous commenter [paints a starker picture than some "marketing war"]:
elmatos 1 day ago
First of all, complete genomics is totally out of the picture,almost everyone has left the raft… they just constantly have to lower their price against the likes of BGI who feed massive sums in ILMN. Sorry but the market is only going to crash down.
Second, Jim Creamer, ILMN does not own 15% of Oxford, it has taken equity in the company and has absolutely NO IP on nanopore sequencing. ONT will burn also.
Pac Bio, we don’t even care about, no plan B, will just burn the fuel (one system sold in the last quarter, enough said).
Now about PGM numbers, no one here, sadly because you are stock price mongrels have even looked at revenue volume per install. It’s pretty simple in that case, ILMN just totally destroys Life tech in NGS, why, because PGM has been sold as a toy, often bundled with other needless toys for large labs. PGMs arrive in boxes, stay in boxes for months, then finally they need to start using it, screw up their library prep, get tired and service out. That’s the PGM reality, same crappy old chemistry as 454. ILMN on the hand, because of the front is very easy to utilise, in a prognostic setting, there is no comparison. And yes, people turn on their miseqs as soon as they get them and run them 24-7, sending thousands of POs on material with really high margins…
Also, Life tech did not choose to de-emphasize sales of their SOLID, they had just singned a massive agreement with Hitachi, to which they promised a certain build volume. That went crashing down badly, why ? Because SOLID was an utter piece of crap, a unit that never even made profit. To this day, the same can be said of IT. And while Life is completely defocusing all volume related business such as PCR QPCR and cell sciences, they are also focusing on making less money.
You see, this market is in an endless fight to reduce cost and increase volume, it’s a scientists dream to have the fastest corvette, very typical of the community who still fails to see the big picture: The excessive data dump is worthless…
And while these companies constantly ramp up, to bring the cost down they can only see one fine silverlining on the horizon: Less money at every new versions. Why ? Because the market is not interested, the billion dollar market becomes the million dollar market, the floors are saturated with these boxes and the next new ones cost even less.
During this time, weather you want to believe it or not, NGS is simply not gaining acceptance in clinical diagnostic, it’s pretty simple and if you think that the T21 test will help the market, you have no clue. The reality is that the true applied markets for these machines is eons away, there are still many generations to go and in the meantime you can look at all these companies go at each other, with the resulting discount battles and placement…
The car is too fast and the highway is completely jammed. And next time you hear a NGS lab rat tell you that NGS is used in hospitals, go to a pathology unit, even a molecular one and ask them: “How much routine work are you doing here ” and listen closely at the “ we are setting up, we want to be ready, we are doing a pilote… gibberish”.
People don’t give a crap about their genome, they want a quick fix and right now small mutation panels are just not NGS ready nor will they be and companies like ONT better have a much pricier machine to sell if they want to survive… The fact is, a single instrument company simply does not thrive in life sciences anymore…
And yes, ILMN would have done well if Roche had planned to invest properly in them, why ? Because Roche has the diag and pharma might no one else has… Shares back in the 30s when this nightmare is over. And if you talk to anyone in the NGS world, you will know it is not thriving, reps fired, downsizing, increased quota, increased territory. I know a lot of people losing sleep over this balloon.
[Since I published "The Principle of Recursive Genome Function" in 2008 (manuscript submitted for peer review a few months after ENCODE concluded earlier than planned, in part due to the World Conference of PostGenetics Society in October, 2006 officially abandoning obsolete axioms of Central Dogma and Junk DNA), moreover I widely publicized the messages in Google Tech Talk YouTube "Is IT Ready for the Dreaded DNA Data Deluge", it would be easy to dismiss any "I told you so". Were it not the case that not only the danger costing us billions of dollars - and even more precious years - was predicted (and now a starking reality) - but the actual science agenda was also laid out. No, scientists are not to be blamed (actually, some key leaders listened carefully) but Industrialization of Genomics were considered, and is still considered, as a "pure technology-run", building on its own momentum. Not unlike "the fastest Corvette" cited by the anonymous commenter, as if Detroit would build cars regardless of the availability of roads, gas-stations - or exert extra marketing effort when exports to Japan dipped - instead of putting the steering wheel to the side the Japanese actually use. Remember the Manhattan Project. Nuclear technology would have been not only a failure, but a very dangerous one, if the scientific-technological advancement weren't guided by the World's best scientific minds; first of all creating Quantum Mechanics in a truly global effort. Likewise, in 2012, a Decade after FractoGene-Discovery one respectfully disagrees with the anonymous commenter that clinical applications are "eons away". The road is already laid out (see Springer Textbook, 2012 and Proceedings of the Hyderabad Conference, 2012 in submission) - Pellionisz, comments at Facebook "Andras Pellionisz, or Twitter "@Holgentech"]
A Quest for Clarity [or Understanding?]
Genomeweb, July/August, 2012
by Tracy Vence
Projects supported by the US National Institutes of Health will have produced 68,000 total human genomes — around 18,000 of those whole human genomes — through the end of this year, National Human Genome Research Institute estimates indicate. And in his book, The Creative Destruction of Medicine, the Scripps Research Institute's Eric Topol projects that 1 million human genomes will have been sequenced by 2013 and 5 million by 2014.
"There's a lot of inventory out there, and these things are being generated at a fiendish rate," says Daniel MacArthur, a group leader in Massachusetts General Hospital's Analytic and Translational Genetics Unit. "From a capacity perspective ... millions of genomes are not that far off. If you look at the rate that we're scaling, we can certainly achieve that."
The prospect of so many genomes has brought clinical interpretation into focus — and for good reason. Save for regulatory hurdles, it seems to be the single greatest barrier to the broad implementation of genomic medicine. [As belabored in the 2008 Google Tech Talk YouTube, the single greatest barrier to implementation of genomic medicine is NOT the "Dreaded DNA Data Deluge" - now a predicted reality of an industry show-stopper "glut" - but the lack of Information Theory. If your computer is ailing, the question to the repairman is NOT "how many hundreds of malfunctioning computers are around?" - but the crucial question if the repairman knows what he/she is doing; understands the functioning or just pretends to?]
But there is an important distinction to be made between the interpretation of an apparently healthy person's genome and that of an individual who is already affected by a disease, whether known or unknown.
In an April Science Translational Medicine paper, Johns Hopkins University School of Medicine's Nicholas Roberts and his colleagues reported that personal genome sequences for healthy monozygotic twin pairs are not predictive of significant risk for 24 different diseases in those individuals. The researchers then concluded that whole-genome sequencing was not likely to be clinically useful for that purpose. (See sidebar, story end.) [The Genome is only one part of the Genome/Epigenome ying-yang; the HoloGenome. If one monozygotic twin has worked a lifetime in an asbestos mine and the other did not, it is not their genome that will determine which one will get lung cancer]
"The Roberts paper was really about the value of omniscient interpretation of whole-genome sequences in asymptomatic individuals and what were the likely theoretical limits," says Isaac Kohane, chair of the informatics program at Children's Hospital Boston. "That was certainly an important study, and it was important to establish what those limits of knowledge are in asymptomatic populations. But, in fact, the major and most important use cases [for whole-genome sequencing] may be in cases of disease."
Still, targeted clinical interpretations are not cut and dried. "Even in cases of disease, it's not clear that we know now how to look across multiple genes and figure out which are relevant, which are not," Kohane adds. [In an orchestra which musical instrument is "relevant" and which one isn't ? In Bartok, percussion instruments are more relevant compared to Wagner's horns]
While substantial progress has been made — in particular, for genetic diseases, including certain cancers — ambiguities have clouded even the most targeted interpretation efforts to date. Technological challenges, meager sample sizes, and a need for increased, fail-safe automation all have hampered researchers' attempts to reliably interpret the clinical significance of genomic variation. But perhaps the greatest problem, experts say, is a lack of community-wide standards for the task.
Genes to genomes
When scientists analyzed James Watson's genome — his was the first personal sequence, completed in 2007 and published in Nature in 2008 — they were surprised to find that he harbored two putative homozygous SNPs matching Human Gene Mutation Database entries that, were they truly homozygous, would have produced severe clinical pheno-types.
But Watson was not sick.
As researchers search more and more genomes, such inconsistencies are increasingly common.
"My take on what has happened is that the people who were doing the interpretation of the raw sequence largely were coming from a SNPs world, where they were thinking about sequence variants that have been observed before, or that have an appreciable frequency, and weren't thinking very much about the single-ton sequence variants," says Sean Tavtigian, associate professor of oncology at the University of Utah.
"There is a qualitative difference between looking at whole-genome sequences and looking at single genes or, even more typically, small numbers of variants that have been previously implicated in a disease," Boston's Kohane adds.
"Previously, because of the cost and time limitations around sequencing and genotyping, we only looked at variants in genes for which we had a clinical indication. Now, since we can essentially see that in the near future we will be able to do a full genome sequence for essentially the same cost as just a focused set-of-variants test, all of the sudden we have to ask ourselves: What is the meaning of variants that fall outside where we would have ordinarily looked for a given disease or, in fact, if there is no disease at all?"
Mass General's MacArthur says it has been difficult to pinpoint causal variants because they are enriched for both sequencing and annotation errors. "In the genome era, we can generate those false positives at an amazing rate, and we need to work hard to filter them back out," he says.
"Clinical geneticists have been working on rare diseases for a long time, and have identified many genes, and are used to working in a world where there is sequence data available only from, say, one gene with a strong biological hypothesis. Suddenly, they're in this world where they have data from patients on all 20,000 genes," MacArthur adds. "There's a fundamental mind-shift there, in shifting from one gene through to every gene. My impression is that the community as a whole hasn't really internalized that shift; people still have a sense in their head that if you see a strongly damaging variant that segregates with the disease, and maybe there's some sort of biological plausibility around it as well, that that's probably the causal variant." [Amen]
It's no crystal ball, but whole-genome sequencing has shown to be clinically useful, particularly for patients with genetic diseases. Still, scientists working toward targeted clinical interpretation of human genomes face several challenges, not the least of which is a lack of community standards.
Studies have shown that that's not necessarily so. Because of this, "I do worry that in the next year or so we'll see increasing numbers of mutations published that later prove to just be benign polymorphisms," MacArthur adds.
"The meaning of whole-genome -sequence I think is very much front-and-center of where genomics is going to go. What is the true, clinical meaning? What is the interpretation? And, there's really a double-edged sword," Kohane says. On one hand, "if you only focus on the genes that you believe are relevant to the condition you're studying, then you might miss some important findings," he says. Conversely, "if you look at every-thing, the likelihood of a false positive becomes very, very high. Because, if you look at enough things, invariably you will find something abnormal," he adds.
False positives are but one of the several challenges scientists working to analyze genomes in a clinical context face.
Technical difficulties
That advances in sequencing technologies are far outstripping researchers' abilities to analyze the data they produce has become a truism of the field. [Truism, yes, funding no]. But current sequencing platforms are still far from perfect, making most analyses complicated and nuanced. Among other things, improvements in both read length and quality are needed to enable accurate and reproducible interpretations.
"The most promising thing is the rate at which the cost-per-base-pair of massively parallel sequencing has dropped," Utah's Tavtigian says. Still, the cost of clinical sequencing is not inconsequential. "The $1,000, $2,000, $3,000 whole-genome sequences that you can do right now do not come anywhere close to 99 percent probability to identify a singleton sequence variant, especially a biologically severe singleton sequence variant," he says. "Right now, the real price of just the laboratory sequencing to reach that quality is at least $5,000, if not $10,000."
However, Tavtigian adds, "techniques for multiplexing many samples into a channel for sequencing have come along. They're not perfect yet, but they're going to improve over the next year or so."
Using next-generation sequencing platforms, researchers have uncovered a variety of SNPs, copy-number variants, and small indels. But to MacArthur's mind, current read lengths are not up to par when it comes to clinical-grade sequencing, and they have made supernumerary quality-control measures necessary.
"There's no question that we're already seeing huge improvements. ... And as we add in to that changes in technology — for instance much, much longer sequencing reads, more accurate reads, possibly combining different platforms — I think these sorts of [quality-control] issues will begin to go away over the next couple of years," MacArthur says. "But at this stage, there is still a substantial quality-control component in any sort of interpretation process. We don't have perfect genomes."
In a 2011 Nature Biotechnology paper, Stanford University's Michael Snyder and his colleagues sought to examine the accuracy and completeness of single-nucleotide variant and indel calls from both the Illumina and Complete Genomics platforms by sequencing the genome of one individual using both technologies. Though the researchers found that more than 88 percent of the unique single-nucleotide variants they detected were concordant between the two platforms, only around one-quarter of the indel calls they generated matched up. Overall, the authors reported having found tens of thousands of platform-specific variant calls, around 60 percent of which they later validated by genotyping array.
For clinical sequencing to ever become widespread, "we're going to have to be able to show the same reproducibility and test characteristic modification as we have for, let's say, an LDL cholesterol level," Boston's Kohane says. "And if you measure it in one place, it should not be too different from another place. ... Even before we can get to the clinical meaning of the genomes, we're going to have to get some industry-wide standards around quality of sequencing."
Scripps' Topol adds that when it comes to detecting rare variants, "there still needs to be a big upgrade in accuracy."
Analytical issues
Beyond sequencing, technological advances must also be made on the analysis end. "The next thing, of course, is once you have better -accuracy ... being able to do all of the analytical work," Topol says. "We're getting better at the exome, but every-thing outside of protein-coding -elements, there's still a tremendous challenge."
Indeed, that challenge has inspired another — a friendly competition among bioinformaticians working to analyze pediatric genomes in a pedigree study.
With enrollment closed and all sequencing completed, participants in the Children's Hospital Boston-sponsored CLARITY Challenge have rolled up their shirtsleeves and begun to dig into the data — de-identified clinical summaries and exome or whole-genome sequences generated by Complete Genomics and Life Technologies for three children affected by rare diseases of unknown genetic basis, and their parents. According to its organizers, the competition aims to help set standards for genomic analysis and interpretation in a clinical setting, and for returning actionable results to clinicians and patients.
"A bunch of teams have signed up to provide clinical-grade reports that will be checked by a blue-ribbon panel of judges later this year to compare and contrast the different forms of clinical reporting at the genome-wide level," Kohane says. The winning team will be announced this fall and will receive a $25,000 prize, he adds.
While the competition covers all aspects of clinical sequencing — from readout to reporting — it is important to recognize that, more generally, there may not be one right answer and that the challenges are far-reaching, affecting even the most basic aspects of analysis.
"There is a lot of algorithm investment still to be made in order to get very good at identifying the very rare or singleton sequence variants from the massively parallel sequencing reads efficiently, accurately, [and with] sensitivity," Utah's Tavtigian says.
Picking up a variant that has been seen before is one thing, but detecting a potentially causal, though as-yet-unclassified variant is a beast of another nature.
"Novel mutations usually need extensive knowledge but also validation. That's one of the challenges," says Zhongming Zhao, associate professor of biomedical informatics at Vanderbilt University. "Validation in terms of a disease study is most challenging right now, because it is very time-consuming, and usually you need to find a good number of samples with similar disease to show this is not by chance."
Search for significance
Much like sequencing a human genome in the early- to mid-2000s was more laborious than it is now, genome interpretation has also become increasingly automated.
Beyond standard quality-control checks, the process of moving from raw data to calling variants is now semiautomatic. "There's essentially no manual intervention required there, apart from running our eyes over [the calls], making sure nothing has gone horribly wrong," says Mass General's MacArthur. "The step that requires manual intervention now is all about taking that list of variants that comes out of that and looking at all the available biological data that exists on the Web, [coming] up with a short-list of genes, and then all of us basically have a look at all sorts of online resources to see if any of them have some kind of intuitive biological profile that fits with the disease we're thinking about."
Of course, intuitive leads are not foolproof, nor are current mutation data-bases. (See sidebar, story end.) And so, MacArthur says, "we need to start replacing the sort of intuitive biological approach with a much more data-informed approach."
Developing such an approach hinges in part on having more genomes. "If we get thousands — tens of thousands — of people sequenced with various different phenotypes that have been crisply identified, that's going to be so important because it's the coupling of the processing of the data with having rare variants, structural variants, all the other genomic variations to understand the relationship of whole-genome sequence of any particular phenotype and a sequence variant," Scripps' Topol says.
Vanderbilt's Zhao says that sample size is still an issue. "Right now, the number of samples in each whole-genome sequencing-based publication is still very limited," he says. At the same time, he adds, "when I read peers' grant applications, they are proposing more and more whole-genome sequencing."
When it comes to disease studies, sequencing a whole swath of apparently healthy people is not likely to ever be worthwhile. According to Utah's Tavtigian, "the place where it is cost-effective is when you test cases and then, if something is found in the case, go on and test all of the first-degree relatives of the case — reflex testing for the first-degree relatives," he says. "If there is something that's pathogenic for heart disease or colon cancer or whatever is found in an index case, then there is a roughly 50 percent chance that the first-degree relatives are going to carry the same thing, whereas if you go and apply that same test to someone in the general population, the probability that they carry something of interest is a lot lower."
But more genomes, even familial ones, are not the only missing elements. To fill in the functional blanks, researchers require multiple data types.
"We've been pretty much sequence-centric in our thinking for many years now because that was where are the attention [was]," Topol says. "But that leaves the other 'omes out there." [Amen]
From the transcriptome to the proteome, the metabolome, the microbiome, and beyond — Topol says that because all the 'omes contribute to human health, they all merit review.
"The ability to integrate information about the other 'omics will probably be a critical direction to understand [here is the word; if the Genome is Life's Code, it must be mathematically understood] the underpinnings of disease," he says. "I call it the 'panoromic' view — that is really going to become a critical future direction once we can do those other 'omics readily. We're quite a ways off from that right now." [Recursive Genome Function, tracking fractal iterations that can be derailed by fractal defects, does HoloGenomics already - the business of HolGenTech]
Mass General's MacArthur envisages "rolling in data from protein-protein interaction networks and tissue expression data — pulling all of these together into a model that predicts, given the phenotype, given the systems that appear to be disrupted by this variant, what are the most likely set of genes to be involved," he says. From there, whittling that set down to putative causal variants would be simpler.
"And at the end of that, I think we'll end up with a relatively small number of variants, each of which has a probability score associated with it, along with a whole host of additional information that a clinician can just drill down into in an intuitive way in making a diagnosis in that individual," he adds.
According to MacArthur, "we're already moving in this direction — in five years I think we will have made substantial progress toward that." He adds, "I certainly think within five years we will be diagnosing the majority of severe genetic disease patients; the vast majority of those we'll be able to assign a likely causal variant using this type of approach."
Tavtigian, however, highlights a potential pitfall. While he says that "integration of those [multivariate] data helps a lot with assessing unclassified variants," it is not enough to help clinicians ascertain causality. Functional assays, which can be both inconclusive and costly, will be needed for some unclassified variant hits, particularly those that are thought to be clinically meaningful.
"I don't see how you're going to do a functional assay for less than like $1,000," he says. "That means that unless the cost of the sequencing test also includes a whole bunch of money for assessing the unclassified variants, a sequencing test is going to create more of a mess than it cleans up."
Rare, common
Despite the challenges, there have been plenty of clinical sequencing success stories. Already, Scripps' Topol says there have been "two big fronts in 2012: One is the unknown diseases [and] the other one, of course, is cancer." But scientists say that despite the challenges, whole--genome sequencing might also become clinically useful for asymptomatic individuals in the future.
Down the line, scientists have their sights set on sequencing asymptomatic individuals to predict disease risk. "The long-term goal is to have any person walk off the street, be able to take a look at their genome and, without even looking at them clinically, say: 'This is a person who will almost certainly have phenotype X,'" MacArthur says. "That is a long way away. And, of course, there are many phenotypes that can't be predicted from genetic data alone."
Nearer term, Boston's Kohane imagines that newborns might have their genomes screened for a number of neonatal or pediatric conditions.
Overall, he says, it's tough to say exactly where all of the chips might fall. "It's going to be an interesting few years where the sequencing companies will be aligning themselves with laboratory testing companies and with genome interpretation companies," Kohane says. [This is a key, and presently BGI/Merck is the scalable global business model to follow. In China, outside of US regulatory jurisdiction, the World's largest sequencing power is already integrated with an IT arm of 5,000 software developers (average age is 27) - and strategic partner Merck Beijing can do clinical trials outsourced. HolGenTech leverages a model to catch up with the leading example of China with another BRICs country; India]
Even if clinical sequencing does not show utility for cases other than genetic diseases, it could still become common practice.
"Worldwide, there are certainly millions of people with severe diseases that would benefit from whole--genome sequencing, so the demand is certainly there," MacArthur says. "It's just a question of whether we can develop the infrastructure that is required to turn the research-grade genomes that we're generating at the moment into clinical-grade genomes. Given the demand and the practical benefit of having this information ... I don't think there is any question that we will continue to drive, pretty aggressively, towards large-scale -genome sequencing."
Kohane adds that "although rare diseases are rare, in aggregate they're actually not — 5 percent of the population, or 1 in 20, is beginning to look common."
Despite conflicting reports as to its clinical value, given the rapid declines in cost, Kohane says it's possible that a whole-genome sequence could be less expensive than a CT scan in the next five years. Confident that many of the interpretation issues will be worked out by then, he adds, "this soon-to-be-very-inexpensive test will actually have a lot of clinical value in a variety of situations. I think it will become part the decision procedure of most doctors."
[Sidebar] 'Predictive Capacity' Challenged
In Science Translational Medicine in April, Johns Hopkins University School of Medicine's Nicholas Roberts and his colleagues showed that personal genome sequences for healthy monozygotic twin pairs are not predictive of significant risk for 24 different diseases in those individuals and concluded that whole-genome sequencing was unlikely to be useful for that purpose.
As the Scripps Research Institute's Eric Topol says, that Roberts and his colleagues examined the predictive capacity of personal genome sequencing "without any genome sequences" was but one flaw of their interpretation.
In a comment appearing in the same journal in May, Topol elaborated on this criticism, and noted that the Roberts et al. study essentially showed nothing new. "We cannot know the predictive capacity of whole-genome sequencing until we have sequenced a large number of individuals with like conditions," Topol wrote.
Elsewhere in the journal, Tel Aviv University's David Golan and Saharon Rosset noted that slightly tweaking the gene-environment parameters of the mathematical model used by Roberts et al. showed that the "predictive capacity of genomes may be higher than their maximal estimates."
Colin Begg and Malcolm Pike from Memorial Sloan-Kettering Cancer Center also commented on the study in Science Translational Medicine, reporting their -alternative calculation of the predictive capacity of personal sequencing and their analysis of cancer occurrence in the second breast of breast cancer patients, both of which, they wrote, "offer a more optimistic view of the predictive value of genetic data."
In response to those comments, Bert Vogelstein — who co-authored the Roberts et al. study — and his colleagues wrote in Science Translational Medicine that their "group was the first to show that unbiased genome-wide sequencing could illuminate the basis for a hereditary disease," adding that they are "acutely aware of its immense power to elucidate disease pathogenesis." However, Vogelstein and his colleagues also said that recognizing the potential limitations of personal genome sequencing is important to "minimize false expectations and foster the most fruitful investigations."
[Sidebar] 'The Single Biggest Problem'
That there is currently no comprehensive, accurate, and openly accessible database of human disease-causing mutations "is the single greatest failure of modern human genetics," Massachusetts General Hospital's Daniel MacArthur says.
"We've invested so much effort and so much money in researching these Mendelian diseases, and yet we have never managed as a community to centralize all of those mutations in a single resource that's actually useful," MacArthur says. While he notes that several groups have produced enormously helpful resources and that others are developing more, currently "none covers anywhere close to the whole of the literature with the degree of detail that is required to make an accurate interpretation."
Because of this, he adds, researchers are pouring time and resources into rehashing one another's efforts and chasing down false leads.
"As anyone at the moment who is sequencing genomes can tell you, when you look at a person's genome and you compare it to any of these databases, you find things that just shouldn't be there — homozygous mutations that are predicted to be severe, recessive, disease-causing variants and dominant mutations all over the place, maybe a dozen or more, that they've seen in every genome," MacArthur says. "Those things are clearly not what they claim to be, in the sense that a person isn't sick." Most often, he adds, the researchers who reported that variant as disease-causing were mistaken. Less commonly, the database moderators are at fault.
"The single biggest problem is that the literature contains a lot of noise. There are things that have been reported to be mutations that just aren't. And, of course, a lot of the databases are missing a lot of mutations as well," MacArthur adds. "Until we have a complete database of severe disease mutations that we can trust, genome interpretation will always be far more complicated than it should be."
[Consider a library of Russian books that is littered with lots of trashy books from the Soviet Union. Is the main problem if the library is not open to everyone? Do we need, perhaps, a ten- or thosand times bigger library?? Should we spend uncounted tax-dollars to weed out Soviet propaganda-books??? All the above might make sense - once that the far the most important requirement will have been satisfied: "The Reader Understands the Russian Language". As for the HoloGenome, HolGenTech.com focuses in a single-minded manner on the absolutely crucial breakthrough: the mathematical understanding of Fractal Recursive Genome Function; healthy regulation and cancerous misregulation; immediately translated into clinical applications. We have some summer weeks to think about the challenge and best solutions; see Facebook page of Andras Pellionisz and Twitter: @Holgentech]
Summer Solstice in Industrialization of Genomics - Celestial Recursion
As documented below, in the past 6 months 4 top papers (3 Nature, 1 PNAS, a combined 137 authors, Worldwide) published independent experimental Proof of Concept, all cited on hologenomics.com (“News”) that fractal defects, particularly CNV-s, are implicated in cancer. Accordintly, attention is catapulting astronomically.
Maybe the Summer Solstice on the Northern Hemisphere prompted the following answer yesterday, apparently from Australia, to the YouTube Comment “Is IT Ready for the Dreaded DNA Data Deluge?”
The Comment (earlier) was: “This presentation is eye-opening for those who no longer wish to hide their heads in the sand. The aim of his talk is not to intimidate, but to enlighten and instill hope. Pellionisz single-handedly drags the audience into the future where the merger of genomics and IT will save and improve lives. The revolutionary ideas he presents are often attacked by cynics, but as Albert Einstein once said: "Great spirits have always encountered violent opposition from mediocre minds"
The Anwser yesterday reads: “So true: ‘Great spirits have always encountered violent opposition from mediocre minds’. Look how Galileo was treated by the Roman Inquisition or the Catholic Church's response when Copernicus tried to show us that the Earth moved in an orbit around the sun.”
The Answer is sensible enough to leave unmentioned the burning image of Giordano Bruno, though he had to pay an even heavier price for the paradigm-shift than Galileo Galilei or Nicolaus Copernicus. It is telling that 400 years after the execution of Giordano Bruno was apparently not enough to rectify a serious error in judgement against him by the establishment, though nobody (else) died because of the fact that the Earth moved in an elliptical orbit around the Sun (and not vice-versa).
Also unmentioned is a more contemporary and thus much milder case, when it took “only” 40 years to rectify ridiculing Dr. Barbara McClintock as a “Kook” for her paradigm-shift. The tenfold acceleration in the speed of backpedaling on an oversight occurred perhaps because hundreds of millions died every year caused by the disregard/ridicule of genomic paradigm-shifts.
Additional tenfold acceleration is happily observed that in less than 4 years ample independent experimental Proof of Concept was published for the fractal approach in the interpretation of Recursive Genome Function (published in peer reviewed publication and generally disseminated in 2008, with unification of Neuroscience and Genomics in 2012). “Recursive Genome Function”, “of course”, yields today several orders of magnitude less “secret” Google hits and – reverse IP weblog-monitored downloads – compared to its scholarly citations. (Still, can not complain; Watson & Crick paper was not cited by anyone in the first seven years… and the Unification has been amply cited though at the moment it is still In Press with Springer).
How many billions must die before Industrialization of Genomics realize that the bottleneck is not the supply-side of “sequencing” (even if it is spectacularly affordable but too low in demand to sustain “sequencing only”)? Algorithmic, thus software enabling understanding of Genome Function, most particularly of Genome Regulation, e.g. the current wave of identification of Fractal Defects derailing Fractal Recursive Iteration, is the bottleneck to be neatly uncorked for Genome Analytics and thus resulting in a sustainable Industrialization of Genomics.
The Lost Decade; Too Many Genomic Melt-downs (We Could Do Better)
By far the most painful "genome-meltdown" has been experienced by the uncounted hundreds of millions of cancer victims. Massive re-arrangement of DNA with structural variants as long as hundreds of thousands of A,C,T,G bases ("Copy Number Alterations, CNA-s", upsetting normal "Copy Number Variations, CNV-s") are now reported (see below) in independent experimental investigations of world-leader scientists (an overlapping 137 authors from all academic corners of the Globe), within 6 months appearing in 3 leading Nature papers (Boston, Cambridge UK, Ann Arbor MI) plus a PNAS paper (NY, led by an Institute endowed by a $10 M gift from a Prince of Saudi-Ariabia, the paper edited by Eric Lander, a Science Advisor to the US President). CNA-s are implicated in cancers by CNA-s constituting some of the biggest "fractal defects" (CNV-alterations). The Hilbert-fractal of DNA folded into the cell nucleus in a knot-free manner (for glitch-free transcription), furthermore ultra-dense (for the 2m long DNA-strand to fit into a 6 micron diameter cell-nucleus) was put on the Science Cover article on Oct. 9, 2009 to the effect of "Mr. President, the Genome is Fractal!" - yet the decade-old 2002 FractoGene concept 1,see in 2 Fig. 3. , 3 (Principle of Recursive Genome Function), YouTube 2008 32:10, 4 (Cold Spring Harbor, Sept. 16, 2009), 5 (2012 Springer; Geometrical Unification of Neuroscience and Genomics, In Press) of fractal genome governing growth of fractal organisms still (now, evidently fractal defects of DNA causing visibly and obviously misregulated fractal growths, otherwise known as cancers) can not be openly referenced for IP considerations. Although Leroy Hood also in the same year declared that "Genomics became Informatics" (Kyoto Prize to Leroy Hood, 2002), only the General Systems Theory of Ludwig von Bertalanffy (1936) could be put to use, the mathematical identification of the "system" (as fractal iterative recursion) constituted a "lucid heresy" double-fold, as it "violated" the two prevailing (mistaken) axioms (of "Central Dogma" by Crick, pontificating that recursion of information from proteins to DNA "never happens", held since his misconception in 1956 till his passing away in 2004, and of "Junk DNA" by Ohno, who used his veritable credentials of genome-scientist in 1972 to daresay that "too much Junk" is in our DNA "for the importance of doing nothing" - implying [till his passing away in 2000] that even if there was a recursion, from the "junk DNA" no information would be gained). Thus - unfortunately perhaps - against advice of those seasoned in informatics the US and allied governments spent time and money from 2003-2007 on "big science" of ENCODE, to arrive at the same conclusion of ENCODE in 2007 what math of informatics dictated prior to the start of exercise; "Junk DNA is anything but". In 2012, to the day a Decade after its inception, the fractal approach was decorated to the Guest of Honor and Keynote Speaker in a lecture-tour in India; a math- and informatics-savvy science community that did not have to make an about-face, as they were never chained to fundamentally misled establishments.
Science and clinical applications (most notably, cancer) aside, an "Industrial Melt-down" also happened in Genomics, illustrated by the above collated stock-meltdown of four leading public DNA sequencing companies. Predictably (and predicted specifically by 2008 YouTube), "Industrialization of Genomics" went out on the single limb of "sequencing" (dashing for an erroneously "magic" $1,000 Human Genome Sequence), not heeding advice that the imbalance of strong supply and (without matching analytics) a weak demand for sequences will render the path towards industrialization economically untenable. As seen above, even though the two leading sequencing companies (especially since they never were "pure play full DNA sequencing firms") sustained significant loss of market-valuation of their shares, Illumina already was subjected to repeated hostile "take-over" bids by Roche, but to this they prevailed on its own (refusing an elevated $6.6 Bn take-over offer of Roche). As seen above, hardest hit are the two "pure-play sequencing firms" PacBio (PACB) and Complete Genomics (GNOM) - to the extent that on June 6th GNOM issued a SEC filing, "seeking strategic re-structuring, including the possibility of sale of the company":
Complete Genomics axes 55 staffers
Fierce Biotech IT
June 6, 2012 | By Ryan McBride
Complete Genomics ($GNOM) is slumping. The provider of whole genome sequencing and informatics support has decided to cut about 20% of its roster to conserve cash, and the company has hired a financial adviser to aid in its hunt for "strategic alternatives."
With problems in the current whole-genome sequencing market, Complete Genomics plans to boost its focus on clinical sequencing applications in anticipation of growth in demand from hospitals and healthcare providers wishing to decode the DNA of their patients. Meanwhile, the company plans to keep servicing its existing research customers in academia and biopharma. The cutbacks will claim the jobs of 55 employees at the Complete Genomics' base in Mountain View, CA, and those of field workers in the U.S., the company said on Tuesday. Most of the layoffs will take place before the end of this month, with restructuring charges expected to hit $1.5 million.
Complete Genomics hired Jeffries & Company to serve as its financial adviser, and, though no decisions have been made about specific strategic alternatives, the options on the table include a sale of the company, a merger, business combination and equity investment.
A number of factors appear to be hammering Complete Genomics, which has commercialized its proprietary sequencing platform and its informatics and software for managing genomic data in an outsourcing service model. As Cowen & Company analyst Doug Schenkel wrote in a note to investors this week, "[Complete Genomics] deserves credit for attempting to drive growth in a market that has traditionally been quite elastic, via a services model. Unfortunately, demand for whole genome sequencing has not been as robust as other sequencing applications."
Meantime, companies such as Life Technologies ($LIFE), with the Ion Torrent system, and others are developing desktop sequencers that make the technology accessible to individual labs. Plus, the cost of sequencing an individual genome has fallen faster than Moore's Law, pushing sequencing toward a commoditized service and forcing players in the game to rethink their business models.
For example, China-based BGI, the world's largest DNA sequencing provider, has put a lot of energy and investment into cloud computing and bioinformatics that improve the utility of whole genome data and help researchers analyze the huge amount of information. Complete Genomics has made similar investments in IT, for instance, enabling its customers to access their sequencing data on Amazon's cloud, Nature News reported in July.
[One of the main conclusions of Bio-IT World conference in Singapore in these days is, that China-based BGI is the current World-leader with its aggressively pursued, centralized government assisted integration of Sequencing (250 Illumina and Solid platforms, the World's most potent capacity surpassing the entire USA) with Analytics (5,000 software-developers, with the average age of 27, also putting to use the World's fastest supercomputer, surpassing by 40% the speed of fastest US supercomputer). In addition, BGI is aligned with Merck-Beijing subsidiary. This enables Merck with not only outsourced sequencing and analytics, but also affordable clinical trials not directly under FDA. For Big Pharma not only Merck and Roche are now on record to see their future based on Genomics, but e.g. Glaxo is also pursuing M/A penetrating genomics. So is the fervor from major IT companies, from the Korean SAMSUNG entering genome informatics last September to German SIEMENS, with their announcement this Spring. The Global horse-race is on, leaving too many guesses to publicly list for the potentially most successful M/A, best suited for global and scalable business-model of Industrialization of Genomics - Comment by Andras Pellionisz]
Chromosome structure fractal defects implicated in cancer
In about half a year we have seen three independent experimental Proof of Concept studies that fractal defects, the largest of which are Copy Number Variations, are implicated in cancer.
The first Nature study appeared by a HARVARD/MIT/BROAD/DANA-FARBER group in Boston, USA (4 authors)
The second Nature study carried the concept abroad to SANGER INSTITUTE in Cambridge, UK (63 authors, linked to 10 countries; UK, Belgium, Norway, China, Singapore, The Netherlands, Australia, Canada, USA, France)
The third Nature study was completed in USA MIDWEST, MICHIGAN, ANN ARBOR (27 authors, affiliation linked also to to India)
The above three PoC Nature papers are the easiest to understand by visualization of the rotating 3D Hilbert-curve of DNA-fractal, that is transparent in its pristine form, but Copy Number Alterations clog the transparency, thus the functional proximity of physically distant parts of the DNA, to be read in a parallel manner, is disrupted by fractal defects.
--
Now a fourth, PNAS study led by a NEW YORK CITY Institute, endowed by HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud of SAUDI ARABIA, also linked to the Mount Sinai Hospital and an affiliation in Italy (14 authors), the paper edited by Eric Lander (The Broad Institute) generalized the above findings to show that chromosome structure fractal defects are implicated in cancer. This study is not so easy to visualize as it pertains to the genome regulation aberrations at the DNA chromatin level (for early 2008 visualizations, see YouTube [at 30:26], cf. Fig. 6. of The Principle of Recursive Genome Function). The PNAS paper uses the analysis of functional proximities (by the properly cited experimental Hi-C method of Lieberman-Aiden et al, 2009, co-authored by Eric Lander, the paper hallmarked by the Science Magazine displaying on its cover the Hilbert-curve). It is interesting that the edited paper states in its Supplement info that "Interaction Data Support the Fractal Globule Model of Nuclear Organization".
[The above can be discussed in FaceBook page of Andras Pellionisz, and Tweeted @HolGenTech]
In half a year, third independent experimental Proof of Concept that fractal defect of Copy Number Variation is implicated in cancer
The mutational landscape of lethal castration-resistant prostate cancer
Nature (2012) doi:10.1038/nature11125
Characterization of the prostate cancer transcriptome and genome has identified chromosomal rearrangements and copy number gains and losses, including ETS gene family fusions, PTEN loss and androgen receptor (AR) amplification, which drive prostate cancer development and progression to lethal, metastatic castration-resistant prostate cancer (CRPC)1. However, less is known about the role of mutations2, 3, 4. Here we sequenced the exomes of 50 lethal, heavily pre-treated metastatic CRPCs obtained at rapid autopsy (including three different foci from the same patient) and 11 treatment-naive, high-grade localized prostate cancers. We identified low overall mutation rates even in heavily treated CRPCs (2.00 per megabase) and confirmed the monoclonal origin of lethal CRPC. Integrating exome copy number analysis identified disruptions of CHD1 that define a subtype of ETS gene family fusion-negative prostate cancer. Similarly, we demonstrate that ETS2, which is deleted in approximately one-third of CRPCs (commonly through TMPRSS2:ERG fusions), is also deregulated through mutation. Furthermore, we identified recurrent mutations in multiple chromatin- and histone-modifying genes, including MLL2 (mutated in 8.6% of prostate cancers), and demonstrate interaction of the MLL complex with the AR, which is required for AR-mediated signalling. We also identified novel recurrent mutations in the AR collaborating factor FOXA1, which is mutated in 5 of 147 (3.4%) prostate cancers (both untreated localized prostate cancer and CRPC), and showed that mutated FOXA1 represses androgen signalling and increases tumour growth. Proteins that physically interact with the AR, such as the ERG gene fusion product, FOXA1, MLL2, UTX (also known as KDM6A) and ASXL1 were found to be mutated in CRPC. In summary, we describe the mutational landscape of a heavily treated metastatic cancer, identify novel mechanisms of AR signalling deregulated in prostate cancer, and prioritize candidates for future study.
[Copy Number Variations - the largest pieces of fractal defects - clog the transparency of the delicate fractal Hilbert-curve of DNA. In half a year, a Boston (MIT-Harvard-Broad-Dana Farber), a Cambridge UK (Sanger) and now a Michigan-group provides, what appears to be an undisputable evidence that massive fractal defects disrupt genome regulation in cancer. Now the question is no longer "if a Proof of Concept" is clinched, but only a matter of ramping up the fractal interpretation of the analysis of derailed genome regulation - Pellionisz]
A Decade after Genomics was Declared to be Informatics; Vistas by Andras Pellionisz (Part II)
An Avalanche of Independent Experimental Proofs of Concept that Intact or Defective Fractal Genome Governs Physiological or Pathological Growth of Organs, Organelles and Organisms; FractoGene (2002)
Pellionisz’ “FractoGene” approach receives massive independent experimental “Proof of Concept”, published in top peer-reviewed journals (25-29, 31 in 2011 and 2012). POC-s stream within a single decade since inception and in spite of a need to shred both prevailing mistaken axioms of genomics defended with (ad hominem) “tools and nails” by ossified detractors of the old establishment.
While upon arrival of the first full human DNA (2000) it became evident that the old hypotheses of “Genes and Junk” were debased by the obvious lack of expected genes, moreover a fractal hypothesis was already seeded (1-2), Eric Lander declared a conceptual vacuum of “hypothesis free search for genes” (3). Nobody dared issuing any attacks against him, especially not “ad hominem” slur for this, since it at least permitted a “no brainer” activity to supposedly last forever. As a mathematician he knew that, without scientific hypotheses proving or disproving by rigorous check their validity, research degenerated into a serendipituous mode - in English, to the horrendously expensive “brute force” – much like as a ship that randomly navigates without a compass; see the famous bestseller of Thomas Kuhn entitled “The Structure of Scientific Revolutions”.
It took less than a decade, when Dr. Pellionisz already published his Principle of Recursive Genome Function (12) and he was invited by George Church to present it in Cold Spring Harbor (17) that the Science Advisor to the President, Eric Lander put the fractal concept of genome on the cover of Science magazine (18).
The ensuing tumultuous the three years brought endorsements based on due diligence: “Of most interest to me are Andras Pellionisz’ ideas around the fractal nature of genomes and how disruptions in such structure may drive phenotypic variation ... The detection of these structure and subsequent determination of their association with disease are computationally intense problems. If something like this were shown to be true (disruptions in the fractal structures lead to phenotypic change), it would be truly revolutionary” (Schadt E, written communication to prominent IT industry).
Now the long-requested Proofs of Concept are in, the trickle of first findings rapidly becoming an avalanche of massive evidence (instead of “hypothesis-free search for genes”). In (18) and (25) with personal co-authorship of the Science Advisor to the President, it is shown that the unique fractal property of ultra-dense and knotless folding of DNA also has a “see through” feature (bringing to functional proximity all parts of the DNA, in a parallel manner) – but this “transparency” is lost e.g. by the fractal defect of a copy numuber alteration (26).
Along with fervent experimental-theoretical research (18-31) it is now recognized that Dr. Pellionisz “... revolutionized the biological sciences with geometrization of neuroscience and genetics. This has been achieved by application of recursion Algorithms based on the principle that each state of the system is deduced from its previous states just like in algebraic series” (30). Germany embraced his geometrization by the Senior Distinguished American Scientist Humboldt Prize, was invited as Keynote Speaker on FractoGene (2003 Florida), to Hungary (2006), to Cold Spring Harbor (2009), India embraced his fractal concept by Guest of Honor and Keynote Lectures and Decoration of his Lifetime Achievement (2012, Hyderabad and Trivandrum), an is presently invited to Italy (Bologna, June 2012). Geometrization of neuroscience drew its consequences on philosophy in Neuroscience by Patricia Churchland (Neurophilosophy, MIT) and on Unified Neuroscience and Genomics in 2012 by Niraj Kumar in India (30). Further, the roadplan outlined e.g. in personalized medicine, most notably cancer, (32) became a linchpin in fierce global competition of scalable full business models of emerging countries.
Most concpicuously, cancer may or may not become the first disease solved by computers, but at this time it might take a veritable computer illiterate to dream that the “genome disease” (cancer) can ever be solved without the leadership of those with software enabling algorithmic hypotheses, now within a single decade way beyond the POC stage.
Timeline
(1) 1989 Pellionisz AJ, Neural geometry: towards a fractal model of neurons. In: Cotterill RMJ (ed) Models of brain function. Cambridge University Press, Cambridge, pp 453–464. http://www.junkdna.com/pellionisz_fractal_purkinje_neuron_1989.pdf
(2) 1993 Grosberg A, Rabin Y, Havlin S, Neer A (1993) Crumpled globule model of the three-dimensional structure of DNA. Europhys Lett 23:373–378. http://havlin.biu.ac.il/PS/scan189.pdf
(3) 2000 Lander E. (2000) A Hypothesis-free Search for Genes. Keystone Millenium Meeting: A Trends Guide (Page 48 in Neurosciences at the Postgenomic Era, by Jacques Mallet, Yves Christen, Springer, 2003)
(4) 2002 Pellionisz A; FractoGene Provisional submission to USPTO
http://www.fractogene.com
(5) 2002 Plotkin H. (2002) Junk DNA Revisited. Silicon Valley startup claims to have unlocked a key to its hidden language. San Francisco Chronicle, http://www.junkdna.com/plotkin.htm
(6) 2003 Pellionisz A; FractoGene Regular submission to USPTO
http://www.fractogene.com
(7) 2003 Pellionisz A. (2003) FractoGene Design-Tool for Protein-Based Self-Assembling Nanostructures, Materials and Applications Keynote Lecture of the 204th Electrochemical Society, pp. 1195 http://www.fractogene.com/1195.pdf
(8) 2006 Pellionisz, A. (2006) PostGenetics: Genetics beyond Genes. The journey of discovery of the function of "junk" DNA. Peer-invited and peer-reviewed Keynote lecture at "European Inaugural of the International PostGenetics Society", 12. October, 2006, Budapest, Hungary, a Satellite to the International Congress of Immunogenomics and Immunomics, pp. 219., BCII2006
(9) 2006 Simons M, Pellionisz A (2006a) Genomics, morphogenesis and biophysics: triangulation of purkinje cell development. The Cerebellum 5(1):27–35. http://www.junkdna.com/fractogene/05_simons_pellionisz.pdf
(10) 2006 Simons M, Pellionisz A (2006b) Implications of fractal organization of DNA on disease risk genomic mapping and immune function analysis. Australasian and Southeast Asian Tissue Typing Association. In: 30th scientific meeting 22–24 Nov 2006, Chiangmai. http://www.junkdna.com/fractogene/06_simons_pellionisz.html
(11) 2007 Pellionisz A; FractoGene CIP submission to USPTO
http://www.fractogene.com
(12) 2008 The Principle of Recursive Genome Function, Springer, Cerebellum. 2008;7(3):348-59.
Full .pdf http://ww.junkdna.com/pellionisz_principle_of_recursive_genome_function.pdf
Supplementary material http://www.junkdna.com/pellionisz_principle
(13) 2008 October 30, Is IT Ready for the Dreaded DNA Data Deluge? YouTube
http://www.youtube.com/watch?v=WJMFuc75V_w approaching 14 thousand views
(14) 2008 Shapshak P, Chiappelli, F, Commins D, Singer E, Levine AJ, Somboonwit C, Minagar A, Pellionisz, A (2008) Molecular epigenetics, chromatin, and NeuroAIDS/HIV: translational implications. Bioinformation 3(1):53–57. PMCID: PMC2586134
(15) 2008 Chiappelli F, Shapshak P, Commins D, Singer E, Minagar E, Oluwadara O et al (2008) Molecular epigenetics, chromatin, and NeuroAIDS/HIV: immunopathological implications. Bioinformation 3(1):47–52.
(16) 2009 Cartieri FJ (2009) Darwinism and Lamarckism before and after Weisman: a historical, philosophical, and methodological analysis. University of Pittsburg, pp 1–54.
(17) 2009 Pellionisz A. (2009 September 16) From the Principle of Recursive Genome Function of HoloGenome Regulation by Personal Genome Computers (Cold Spring Harbor Labs “Personal Genomes II.” invited by George Church)
(18) 2009 Erez Lieberman-Aiden, Nynke L. van Berkum, Louise Williams, Maxim Imakaev, Tobias Ragoczy, Agnes Telling, Ido Amit, Bryan R. Lajoie, Peter J. Sabo, Michael O. Dorschner, Richard Sandstrom, Bradley Bernstein, M. A. Bender, Mark Groudine, Andreas Gnirke, John Stamatoyannopoulos, Leonid A. Mirny, Eric S. Lander, Job Dekker (2009 October 9) The comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326. doi: 10.1126/science.1181369
Full .pdf at http://www.cbcb.umd.edu/~hcorrada/CMSC858B/readings/Hi-C_2009.pdf
(19) 2010 Perez JC (2010) Codon population in single-stranded whole human genome DNA are fractal and fine-tuned by the Golden Ration 1.618. Interdiscip Sci: Comput Life Sci 2(3):228–340. doi:10.1007/s12539-010-0022-0.
(20) 2010 Arneth BM (2010) Sequence variability and sequence evolution: An explanation of molecular polymorphisms and why many molecular structures can be preserved although they are not predominant. DNA Cell Biol 29(10):571–576. doi:10.1089/dna.2009.0942
(21) 2010 Oller JW (2010) The antithesis of entropy: Biosemiotic communication from genetics to human language with special emphasis on the Iimmune systems. Entropy 12:631–705. doi:10.3390/e12040631. http://www.mdpi.com/1099-4300/12/4/631/pdf
(22) 2011 Stagnaro S (2011) Glycocalix quantum-biophysical-semeiotic evaluation plays a central role in demonstration of water memory-information. http://www.sisbq.org/uploads/5/6/8/7/5687930/wmi_glycocalyx.pdf
(23) 2011 Stagnaro S, Caramel S (2011) A new way of therapy based on water memory-information: the Quantum biophysical approach. http://www.sisbq.org/uploads/5/6/8/7/5687930/qbtherapy.pdf
(24) 2011 Elnitski L, Piontkivska H, Welch JR (2011) In: Fedorov A, Fedorava L (eds) Advances in genomic sequence analysis and pattern discovery, Chapter 3. Science, engineering and biology informatics, vol 7. World Scientific, Singapore, pp 65–93
(25) 2011 The genomic complexity of primary human prostate cancer (2011) Nature. 470, 214-220, doi:10.1038/nature09744 Michael F. Berger, Michael S. Lawrence, Francesca Demichelis, Yotam Drier, Kristian Cibulskis, Andrey Y. Sivachenko, Andrea Sboner, Raquel Esgueva, Dorothee Pflueger, Carrie Sougnez, Robert Onofrio, Scott L. Carter, Kyung Park, Lukas Habegger, Lauren Ambrogio, Timothy Fennell, Melissa Parkin, Gordon Saksena, Douglas Voet, Alex H. Ramos, Trevor J. Pugh, Jane Wilkinson, Sheila Fisher, Wendy Winckler, Scott Mahan, Kristin Ardlie, Jennifer Baldwin, Jonathan W. Simons, Naoki Kitabayashi, Theresa Y. MacDonald, Philip W. Kantoff, Lynda Chin, Stacey B. Gabriel, Mark B. Gerstein, Todd R. Golub, Matthew Meyerson, Ashutosh Tewari, Eric S. Lander, Gad Getz, Mark A. Rubin, Levi A. Garraway
Full .pdf at http://www.nature.com/nature/journal/v470/n7333/full/nature09744.html
(26) 2011 High order chromatin architecture shapes the landscape of chromosomal alterations in cancer (2011) Fundenberg G, Getz G, Meyerson M, Mirny L.A. http://www.nature.com/nbt/journal/v29/n12/full/nbt.2049.html
(27) 2011 The genetic code, 8-dimensional hypercomplex numbers and dyadic shifts. (2011) Petoukhov, Sergey V. International Conference on Bioinformatics & Computational Biology (Las Vegas, USA, July 18-21, 2011) http://arxiv.org/ftp/arxiv/papers/1102/1102.3596.pdf
(28) 2012 February 17 Human mitotic chromosomes consist predominantly of irregularly folded nucleosome fibres without a 30-nm chromatin structure (2012) Yoshinori Nishino, Mikhail Eltsov, Yasumasa Joti, Kazuki Ito, Hideaki Takata, Yukio Takahashi, Saera Hihara, Achilleas S Frangakis, Naoko Imamoto, Tetsuya Ishikawa and Kazuhiro Maeshima
http://www.ncbi.nlm.nih.gov/pubmed/22343941
(29) 2012 February 17 Toward Convergence of Experimental Studies and Theoretical Modeling of the Chromatin Fiber (2012) Tamar Schlick, Jeff Hayes and Sergei Grigoryev, doi: 10.1074/jbc.R111.305763 February 17, 2012 The Journal of Biological Chemistry, 287, 5183-5191.
http://www.jbc.org/content/287/8/5183.abstract
(30) 2012 March 11, Niraj Kumar (2012) Sryantra – Mathematical Properties. Geophilosophy of India and Sriyantra.
http://ariseasia.blogspot.com/2012/03/sriyantra-mathematical-properties.html
(31) 2012 March 13 Human mitotic chromosome structure: what happened to the 30-nm fibre? Jeffrey C Hansen The EMBO Journal (2012) 31, 1621–1623. doi:10.1038/emboj.2012.66; Published online 13 March 2012
http://www.nature.com/emboj/journal/v31/n7/full/emboj201266a.html
(32) 2012 Andras J. Pellionisz, Roy Graham, Peter A. Pellionisz and Jean-Claude Perez (In Press) Recursive Genome Function of the Cerebellum: Geometric Unification of Neuroscience and Genomics. In: Springer Handbook; "The Cerebellum and Cerebellar Disorders", Ed. by M. Manto. Submitted October 20, Accepted November 1, 2011, (In Press)
http://www.junkdna.com/pellionisz_unification/
Full .pdf http://www.fractal.org/Geometric-unification.pdf
Legacy
It is a twist of life that Ohno’s scientifically erroneous notion “So much ‘Junk’ in Our Genome” (1972), where his original, meant-to-be “scientific” statement that almost all of our DNA was there for “the importance of doing nothing”, see page 367 was misinterpreted, like other half-baked assumptions such as “Central Dogma”, that together set back Genomics by half a Century. As a further and ultimate irony, John Mattick and Malcolm Simons arrived at their respective final conclusions about the same time when the superseeding fractal paradigm prevailed. Dear Malcolm Simons passed away January 25th, 2012 in his 73rd year unrecognized outside the 66 Founders of International Hologenomics Society (Founders overlaping with the list below), where he was the Honorary Chairman. Very soon after, Dr. Mattick received a lifetime-award from HUGO on March 12, 2012.
Finally, it bespeaks volumes of ignorance caused by lack of domain expertise in abstract sciences coupled with remarkable arrogance shrouded by thick denial, that a detractor calls the awarded Dr. Mattick “stupid and irrational”: “I am criticizing Mattick's ideas, which I find to be quite silly and irrational. Saying that Mattick is stupid and irrational because of what he writes and draws is not an ad hominem fallacy”. Dr. Mattick, who heroically struggles with a mathematical interpretation of the RNA system, may be comforted by the well-known history that Dr. Barbara McClintock was also called a “kook” by similar detractors – and awarded by a Nobel Prize some 40 years after her paradigm-shift.
Partial List of Personal Commitments in the First Decade
Andras J. Pellionisz, Roy Graham, Peter A. Pellionisz, Jean-Claude Perez, Sergey V. Petoukhov, Michael F. Berger, Michael S. Lawrence, Francesca Demichelis, Yotam Drier, Kristian Cibulskis, Andrey Y. Sivachenko, Andrea Sboner, Raquel Esgueva, Dorothee Pflueger, Carrie Sougnez, Robert Onofrio, Scott L. Carter, Kyung Park, Lukas Habegger, Lauren Ambrogio, Timothy Fennell, Melissa Parkin, Gordon Saksena, Douglas Voet, Alex H. Ramos, Trevor J. Pugh, Jane Wilkinson, Sheila Fisher, Wendy Winckler, Scott Mahan, Kristin Ardlie, Jennifer Baldwin, Jonathan W. Simons, Naoki Kitabayashi, Theresa Y. MacDonald, Philip W. Kantoff, Lynda Chin, Stacey B. Gabriel, Mark B. Gerstein, Todd R. Golub, Matthew Meyerson, Ashutosh Tewari, Eric S. Lander, Gad Getz, Mark A. Rubin, Levi A. Garraway, Grosberg A, Rabin Y, Havlin S, Neer A, Plotkin Hal, Simons J. Malcolm, Shapshak P, Chiappelli, F, Commins D, Singer E, Levine AJ, Somboonwit C, Minagar A, Oluwadara O, Cartieri FJ, Erez Lieberman-Aiden, Nynke L. van Berkum, Louise Williams, Maxim Imakaev, Tobias Ragoczy, Agnes Telling, Ido Amit, Bryan R. Lajoie, Peter J. Sabo, Michael O. Dorschner, Richard Sandstrom, Bradley Bernstein, M. A. Bender, Mark Groudine, Andreas Gnirke, John Stamatoyannopoulos, Leonid A. Mirny, Eric S. Lander, Job Dekker, Arneth BM, Oller JW, Stagnaro S, Caramel S, Elnitski L, Piontkivska H, Welch JR, Fundenberg G, Getz G, Meyerson M, Mirny L.A. Yoshinori Nishino, Mikhail Eltsov, Yasumasa Joti, Kazuki Ito, Hideaki Takata, Yukio Takahashi, Saera Hihara, Achilleas S Frangakis, Naoko Imamoto, Tetsuya Ishikawa and Kazuhiro Maeshima, Jeffrey C Hansen, Tamar Schlick, Jeff Hayes, Sergei Grigoryev, Church GM, Schadt E, Ruis J, Flanagan B, Audrey S M Teo, Pramila N Ariyaratne, Naoto Takahashi, Kenichi Sawada, Yao Fei, Sheila Soh, Wah Heng Lee, John W J Huang, John C Allen Jr, Xing Yi Woo, Niranjan Nagarajan, Vikrant Kumar, Anbupalam Thalamuthu, Wan Ting Poh, Ai Leen Ang, Hae Tha Mya, Gee Fung How, Li Yi Yang, Liang Piu Koh, Balram Chowbay, Chia-Tien Chang, Veera S Nadarajan, Wee Joo Chng, Hein Than, Lay Cheng Lim, Yeow Tee Goh, Shenli Zhang, Dianne Poh, Patrick Tan, Ju-Ee Seet, Mei-Kim Ang, Noan-Minh Chau, Quan-Sing Ng, Daniel S W Tan, Manabu Soda, Kazutoshi Isobe, Markus M Nöthen, Tien Y Wong, Atif Shahab, Xiaoan Ruan, Valère Cacheux-Rataboul, Wing-Kin Sung, Eng Huat Tan, Yasushi Yatabe, Hiroyuki Mano, Ross A Soo, Tan Min Chin, Wan-Teck Lim, Yijun Ruan, S Tiong Ong, Niraj Kumar
[This entry can be discussed on the FaceBook page of Andras Pellionisz - full .pdf copies may be requested - AJP]
Genomeweb
April 18, 2012
The Walrus magazine's Mark Czarnecki says that "to comprehend genomes is to begin to unlock the mysteries of life." But that, of course, has been tricky. In the meantime, he says, society has found itself "lost on the gene map" — while [some] researchers have not yet nailed down their interpretations of the human genome, direct-to-consumer firms are hawking genetic tests and that offer to "detail your risks for a menu of diseases." All the while, researchers have struggled to understand the ethical, legal, and social implications of genomics.
Now, Czarnecki says, "with whole-genome sequencing providing so much data that is so little understood, making the best ethical choice is more difficult than ever." He goes on to add that "as the demand for whole-genome sequencing grows, so will profits, but the big money in personalized medicine will come from the development of treatments. … A given mutation on a person's genome may not necessarily express as a malignant disease, so identifying the probability of a multigenic disease is extremely challenging."
Overall, Czarnecki says that at present, "the human genome is still a vast catalogue of the unknown and scarcely known," and that "as medical science struggles to apply these new discoveries to society's benefit, human genome research, now unstoppable, continues to evolve."
[Indeed, it is kind of "awkward". We know ever since the mouse was fully sequenced a decade ago (2002) that our (human) "genes" are 98% identical to those of the mice; thus "upon arrival" the half-a-century assumption that the genome rotates around the "genes", was dead. Some know that in the same year, a decade ago, Leroy Hood declared in 2002 that "Genomics=Informatics" and thus must be approached from some "Systems Biology" viewpoint (invoking Ludwig von Berthalanffy, 1934). Of course, but WHAT SYSTEM? Without system identification systems biology would be rendered serendipitous (in plain English; horrendously expensive). Thomas Kuhn recalled in his famous bestseller "The Structure of Scientific Revolutions" that through the history of science, amassing data alone was never enough - it always underwent the transformative revelation of what the data actually mean. Some recall that in 2002 Pellionisz' "FractoGene" (at that time a "double lucid heresy" flying in the face of the two prevailing dogmas) identified that "the genome is a fractal systems, governing the fractal growth of organelles, organs and organisms" (like normal lungs, brain cells, etc., and cancerous tumors). Has anyone ever seen a fractal object generating another fractal object? Here is some little help: "Clouds are not spheres... nor does lightning travel in a straight line" said the now late giant, Mandelbrot. To remind you having seen "fractals generating fractals" here is my rendering:
FractoGene: "Fractal Genome governs growth of fractal organelles, organs and organisms" (Pellionisz, 2002 see also Geometric Unification of Neuroscience and Genomics, in Press 2012)
Moore's Law versus Javon's Law: Drawning in the Dreaded DNA Data Deluge is mandated by law, unless ...
[As usual, there are always workers who believe that the role of science is to provide more data (not so, claimed Thomas Kuhn in his classic bestseller, "The Structure of Scientific Revolutions"). The present cop out is to claim that the science of genomics might be "hypothesis free". Eric Topol points out the self-contradiction of the latest genomic misnomer in his present bestseller. Just because some do not have any hypothesis of e.g. what the new definition of "gene" might be (replacing the old one made obsolete by ENCODE), does not mean that the "FractoGene" algorithmic hypothesis has to move over because of the pushy brute force of (ultra expensive) "big science" schemes. This issue can be debated on the FaceBook page of Andras Pellionisz]
A Decade after Genomics was Declared to be Informatics; Vistas by Andras Pellionisz (Part I)
In 2002, a pair of seminal notions were introduced, just months after the much-heralded "solves it all" misnomer of "cracking the code" (that was at best a $3Bn Big Science coup, resulting in a crude first draft of revealing - not decyphering - a composite human genome), by Leroy Hood and Andras Pellionisz (separately). Lee Hood, in his Kyoto Prize lecture (2002) declared that "Genomics became Informatics" and thus proposed that it should be approached by System Theory (of Ludwig von Bertalanffy, 1934). I went in the same year of 2002 on written record of USPTO, since no peer-reviewed forum would have accepted his "double lucid heresy", by System Identification; that is, "The genome is a fractal system, that governs fractal growth of organelles, organs and organisms; FractoGene" (Pellionisz, 2002).
After a tumultuous Decade, in a series of notes here Dr. Pellionisz provides the "vistas" how the initial axioms led to conquering the half-a-Century old interlocking (false) Dogmas (the frighteningly unsophisticated "Central Dogma" and comparably simplistic falsehood of "Junk DNA"), towards a breakthrough of today, when both an advanced mathematical treatise of Unification of Neuroscience and Genomics, In Press, (featuring the RNA system as a "genomic cerebellum"), plus at least "the magic trio" of independent experimental Proof of Concept is in high-ranking peer-reviewed journals to validate that "fractal defects cause hereditary diseases" like cancers.
While in the USA "the priesthood of the establishment" (to use the jargon of Eric Topol from his book of The Creative Disruption of Medicine) has fought this revolution all the way (and beyond), Drs. George Church and Eric Schadt have grown to acknowledge my effort by my name, and implicitly Drs. Eric Lander and his numerous co-authors recognize the cardinal notion of fractal genome - as e.g. sophisticated outsiders llike Ray Kurzweil have immediately did so.
Lately, and entire Subcontinent (of India) provided support to a global and scalable business model based on fractal genome and scientifically targeted search for fractal defects as root causes of hereditary diseases. The illustration below alludes the immediate realization that even if not all DNA is fractal, "the DNA of Indian architecture" is based on self-similar repetition, it is fractal.
While NONE of the methods of science can ever be "hypothesis-free" (as Eric Topol alludes in his bestseller to the inherent hypocrisy), the software-enabling mathematical (fractal) theory is particularly well above the level of usually ultra-expensive "brute force approaches" - that would further enhance "unsustainable trends" of the PostModern Industrialization of Genomics.
[To be continued; the issue can be debated on the FaceBook page of Andras Pellionisz]
TKI Cancer Treatment Resistance Linked to Common Germline Variant in East Asian Populations
By Andrea Anderson
NEW YORK (GenomeWeb News) – A Singapore-led team has identified a germline polymorphism that appears to account for reduced drug response in some East Asian cancer patients treated with tyrosine kinase inhibitors.
As they reported online yesterday in Nature Medicine, the researchers used massively parallel paired-end digital tag sequencing to look for germline variationsassociated with reduced response to TKI drugs, which are used to combat certain cancers marked by excess kinase activity, including some forms of chronic myelogenous leukemia and non-small cell lung cancer.
Using blood samples from five CML patients whose cancers did or did not respond to TKI treatment, the team found a resistance-related deletion polymorphism in BIM, a gene that normally helps spur on apoptosis following TKI treatment.
While this deletion appears to be a fairly common germline variant in East Asian populations — turning up in just over 12 percent of healthy individuals tested in that population — it has not been found with any regularity in populations from Europe or Africa.
The investigators have already started looking for alternative treatments for individuals with the variant, focusing on ways to restore the apoptotic activity that's lost when deletion-containing BIM isoforms are expressed. Indeed, their preliminary cell line experiments hint that it is possible to combat TKI resistance in those carrying the BIM deletion by augmenting TKI treatment with drugs known as BH3 mimetics.
"Ideally we would like to conduct clinical trials using a combination of TKIs and BH3 mimetics in patients with the polymorphism who have failed or become resistant to standard TKI therapy," co-corresponding author S. Tiong Ong, a medical oncology researcher at Duke-National University of Singapore, told GenomeWeb Daily News in an e-mail message.
TKI drugs such as imatinib (marketed as Gleevec by Novartis) or gefitinib (marketed by AstraZeneca under the brand name Iressa) have been successfully used to treat many kinase-driven cancers that did not respond to cancer drugs used in the past, study authors explained. But a subset of cases — around 20 percent — remain resistant to TKI drugs.
To look into the genetic basis of this resistance, the team did paired-end digital tag sequencing, or DNA-PET, with Life Technologies' SOLiD platform to look for germline structural variants that might help explain treatment response heterogeneity, using blood samples from two imatinib-sensitive and three imatinib-resistant CML patients.
"We correlated the structural variations detected by DNA-PET with clinical resistance to Gleevec, and picked out SVs which were found only in samples from resistant patients," Ong said in his e-mail.
A 2,903 base pair deletion polymorphism in an intron of the BIM gene coincided with TKI treatment resistance, they found.
The germline polymorphism appears to be fairly common in East Asian populations, turning up with around 12 percent carrier frequency in their screening experiments on thousands of healthy individuals. But the BIM deletion was not found in any individuals from African or European populations.
In addition, retrospective analysis of more than 200 CML patients enrolled in two cohorts from Singapore and Malaysia or Japan indicated that individuals with the BIM deletion were almost three times as likely to show imatinib resistance than those without the polymorphism.
Similarly, in East Asian non-small-cell lung cancer patients with EGFR mutations, the researchers found evidence for shorter progression-free survival in individuals with the BIM polymorphism who received TKI treatment compared to those without the deletion.
The team's cell line and other experiments indicated that BIM deletion affects treatment outcome by influencing BIM splicing patterns in a way that alters the apoptosis-related interactions of the resulting protein.
Whereas cells without the deletion typically expressed exon 4 in the presence of TKI compounds, cells with the polymorphism predominantly expressed exon 3 of the gene, which does not code for a domain necessary for apoptosis.
"The BIM deletion polymorphism results in the splicing (and expression) of BIM isoforms lacking a critical BH3 domain that is required for BIM apoptotic function," Ong said.
"Without this domain, the BIM protein isoforms which are produced in response to the TKI are no longer capable of killing the cancer cells," he added.
Moreover, the researchers found that even a single copy of the polymorphism was enough to prompt TKI resistance in CML and EGFR-mutation containing NSCLC cell lines.
Given their mechanistic findings, the team speculated that it might be possible to curb this intrinsic TKI treatment resistance by combining TKI treatment and therapy with so-called BH3 mimetics, compounds that restore the apoptosis promoting activity missing from cells expressing exon 3 rather than exon 4 of the BIM gene.
Preliminary experiments in CML cell lines supported that prediction, since treatment with both imatinib and the BH3 mimetic ABT-737 seemed to spur apoptosis even in the presence of the BIM deletion.
The team has not yet looked at how the TKI treatment resistance differences associated with the BIM polymorphism relate to overall survival patterns in patients, if at all. Ong noted that such studies are complicated by the fact that individuals who initially show TKI resistance often receive other treatments later on, making direct comparisons difficult.
Still, he and his colleagues hope to do prospective controlled clinical trials to look more closely at such questions.
They are also interested in doing clinical trials to explore the effectiveness of combined BH3 mimetic and TKI therapy for individuals with inherent resistance related to the BIM polymorphism or for patients who have become resistant to the inhibitors through another mechanism.
BH3 mimetics have not yet secured approval from the US Food and Drug Administration, Ong said, but are being tested in clinical trials for other cancer types.
In addition, working with collaborators from the Genome Institute of Singapore and A*STAR's commercialization arm, Exploit Technologies Private Limited, Ong and his colleagues are developing a kit that would make it feasible to routinely test East Asian patients for germline mutations in BIM.
Authors:• et al.
Cancer & Stem Cell Biology Signature Research Programme, Duke–National University of Singapore (NUS) Graduate Medical School, Singapore.
King Pan Ng, Charles T H Chuah, Wen Chun Juan, Tun Kiat Ko, Sheila Soh, John W J Huang, Chia-Tien Chang, Shenli Zhang, Dianne Poh, Patrick Tan & S Tiong Ong
Genome Institute of Singapore, Singapore.
Axel M Hillmer, Audrey S M Teo, Pramila N Ariyaratne, Yao Fei, Wah Heng Lee, Xing Yi Woo, Niranjan Nagarajan, Vikrant Kumar, Anbupalam Thalamuthu, Wan Ting Poh, Patrick Tan, Atif Shahab, Xiaoan Ruan, Valère Cacheux-Rataboul, Wing-Kin Sung & Yijun Ruan
Department of Haematology, Singapore General Hospital, Singapore.
Charles T H Chuah, Ai Leen Ang, Hae Tha Mya, Gee Fung How, Li Yi Yang, Hein Than, Lay Cheng Lim, Yeow Tee Goh & S Tiong Ong
Department of Hematology, Nephrology and Rheumatology, Akita University Graduate School of Medicine, Akita, Japan.
Naoto Takahashi & Kenichi Sawada
Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Yao Fei
Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore.
John C Allen Jr
Department of Hematology-Oncology, National University Cancer Institute of Singapore, National University Health System, Singapore.
Liang Piu Koh, Wee Joo Chng, Ross A Soo & Tan Min Chin
Clinical Pharmacology Laboratory, National Cancer Centre, Singapore.
Balram Chowbay
University of Malaya, Kuala Lumpur, Malaysia.
Veera S Nadarajan
Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Wee Joo Chng
Cancer Science Institute of Singapore, National University of Singapore, Singapore.
Wee Joo Chng, Patrick Tan & Ross A Soo
Department of Pathology, National University Health System, Singapore.
Ju-Ee Seet
Department of Medical Oncology, National Cancer Centre, Singapore.
Mei-Kim Ang, Noan-Minh Chau, Quan-Sing Ng, Daniel S W Tan, Eng Huat Tan, Wan-Teck Lim & S Tiong Ong
Division of Functional Genomics, Jichi Medical University, Tochigi, Japan.
Manabu Soda & Hiroyuki Mano
Department of Respiratory Medicine, Toho University Omori Medical Center, Tokyo, Japan.
Kazutoshi Isobe
Institute of Human Genetics, University of Bonn, Bonn, Germany.
Markus M Nöthen
Singapore Eye Research Institute, Singapore National Eye Centre and National University Health System, Singapore.
Tien Y Wong
Department of Pathology and Molecular Diagnostics, Aichi Cancer Center, Aichi, Japan.
Yasushi Yatabe
Department of Medical Genomics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Hiroyuki Mano
Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore.
Wan-Teck Lim
Department of Biochemistry, National University of Singapore, Singapore.
Yijun Ruan
Department of Medicine, Division of Medical Oncology, Duke University Medical Center, Durham, North Carolina, USA.
S Tiong Ong
Tyrosine kinase inhibitors (TKIs) elicit high response rates among individuals with kinase-driven malignancies, including chronic myeloid leukemia (CML) and epidermal growth factor receptor–mutated non–small-cell lung cancer (EGFR NSCLC). However, the extent and duration of these responses are heterogeneous, suggesting the existence of genetic modifiers affecting an individual's response to TKIs. Using paired-end DNA sequencing, we discovered a common intronic deletion polymorphism in the gene encoding BCL2-like 11 (BIM). BIM is a pro-apoptotic member of the B-cell CLL/lymphoma 2 (BCL2) family of proteins, and its upregulation is required for TKIs to induce apoptosis in kinase-driven cancers. The polymorphism switched BIM splicing from exon 4 to exon 3, which resulted in expression ofBIM isoforms lacking the pro-apoptotic BCL2-homology domain 3 (BH3). The polymorphism was sufficient to confer intrinsic TKI resistance in CML and EGFR NSCLC cell lines, but this resistance could be overcome with BH3-mimetic drugs. Notably, individuals with CML and EGFR NSCLC harboring the polymorphism experienced significantly inferior responses to TKIs than did individuals without the polymorphism (P = 0.02 for CML and P = 0.027 for EGFR NSCLC). Our results offer an explanation for the heterogeneity of TKI responses across individuals and suggest the possibility of personalizing therapy with BH3 mimetics to overcome BIM-polymorphism–associated TKI resistance.
MSKCC, IBM Developing Tool for Personalized Cancer Diagnosis and Treatment
March 22, 2012
GenomeWeb staff reporter
NEW YORK (GenomeWeb News) – Memorial Sloan-Kettering Cancer Center in New York and IBM today announced they will work together to develop a tool to help doctors create individualized cancer diagnostic and treatment recommendations based on current evidence.
The tool will use the computational power and natural language processing ability of IBM's Watson system and be combined with MSKCC's clinical knowledge, existing molecular and genomic data, and repository of cancer case histories. The goal, the partners said, is to give physicians access to detailed diagnostic and treatment options based on updated research to make better decisions about patient care.
MSKCC's oncologists will assist in developing IBM Watson to use patient medical information "and synthesize a vast array of continuously updated and vetted treatment guidelines, published research, and insights gleamed from the deep experience of MSKCC clinicians to provide an individualized recommendation to physicians," it and IBM said in a statement.
Development work has begun for the first application, which includes lung, breast, and prostate cancers, with a goal of providing solutions to a select group of doctors in late 2012 and a wider distribution planned for late 2013.
"The combination of transformational technologies found in Watson with our cancer analytics and decision-making process has the potential to revolutionize the accessibility of information for the treatment of cancer in communities across the country and around the world," MSKCC President and CEO Craig Thompson said in a statement. He added that in addition to improving the personalized care of individual patients, the center expects new research opportunities to emerge from the collaboration.
Financial and other terms of the deal were not disclosed.
Recent CDx, NGS Deals Signal Siemens' Increasing Interest in Genomics Arena
Genomeweb
March 14, 2012
By Tony Fong
NEW YORK (GenomeWeb News) – As companion diagnostics gain acceptance within the pharmaceutical sector and next-generation sequencing progresses toward clinical applications, Siemens is seeking a bigger role in the genomics space.
In the past few months, the German-based electronics company has announced three deals that signal its renewed interest in genomics after largely being a spectator in recent years. Last month, it entered into an agreement with UK-based ViiV Healthcare to develop a companion diagnostic for evaluating patients who may benefit the most from an HIV treatment.
At the same time, it obtained the rights from Tocagen to commercialize a companion diagnostic for the San Diego firm's gene therapy for primary brain cancer. And in November, Siemens said it would make its molecular HIV tests compatible with Illumina's MiSeq platform under a partnership between the firms.
Until about three years ago, Siemens had been active in the 'omics space, forging several partnerships, including ones with Laboratory Corporation of America; Beckman Coulter, which is now part of Danaher; and Celera, now part of Quest Diagnostics. It also established a molecular diagnostics laboratory in Germany.
Since then, however, while Siemens is repeatedly mentioned as a possible buyer whenever a life science tools firm is up for sale, the company has kept a relatively low profile in the 'omics space. But as the results of the Human Genome Project begin to have some impact on the practice of medicine, Siemens is joining a handful of other large, diversified firms that are poised to increase their presence in the genomics space.
With the ViiV, Tocagen, and Illumina deals, Siemens is telling the genomics market that it is "ready to play," Harry Glorikian, founder and managing partner of Scientia Advisors, said.
According to Trevor Hawkins, head of next-generation diagnostics at Siemens Healthcare Diagnostics, the deals represent starting points for the company, catalyzed by what it sees as new phases in genomics technology development and a growing acceptance of the technology by both pharma/biotech and the clinical world.
Hawkins declined to disclose the level of Siemens' investment in its diagnostics operations. Overall, R&D in Siemens Healthcare reached €1.2 billion ($1.59 billion) in fiscal 2011 ended Sept. 30, 2011, or 31 percent of the €3.93 billion spent on R&D companywide.
The Healthcare business generated €12.52 billion in revenues in 2011, or 17 percent of total revenues of €73.52 billion. Within Healthcare, Diagnostics generated €3.67 billion in revenues during the year, unchanged from fiscal 2010.
While Siemens has an established medical imaging and in vitro diagnostics business, the ViiV and Tocagen deals are Siemens' first forays into companion diagnostics, fueled by growing adoption of such tools by drug developers, Hawkins said.
"We feel that pharmaceutical companies are now receptive to the use of diagnostics as an empowering tool for improved therapeutics," he told GenomeWeb Daily News recently, "and we felt that Siemens is rather unique in that we can offer the entire spectrum of capabilities of companion diagnostics," from assay design to clinical trials to assay commercialization.
Siemens has experience working with various types of assays, including immunoassays and molecular-based assays, he added, as well as different technologies. Its collaboration with Tocagen, for example, includes both molecular and immunoassays, and will encompass mass spectrometry and PCR technology, Hawkins said.
Also, as imaging increasingly plays a larger role in companion diagnostics, Siemens is positioned to leverage its expertise in that area, "and we look forward to bringing together the in vivo and in vitro components as a single solution in the companion diagnostics arena," he said.
In addition to ViiV and Tocagen, Siemens is working with other pharma firms in the companion diagnostics space, though Hawkins did not elaborate.
According to Glorikian, Siemens recent activity is a clear indication that it is "willing to make deals to gain access to the technologies and content that are going to be driving diagnostics growth for the next decade or so," he said in an e-mail to GWDN. "Every diagnostics company needs a strategy here and these deals are signs that Siemens is executing on theirs."
He added that while the company may have been less conspicuous in the genomics market in recent years, it may have been due not to a lack of interest but rather because of internal work directed at integrating other healthcare-related acquisitions, managing a challenging economic environment, especially in imaging, and addressing opportunities in healthcare informatics.
Stepping into Next-generation Sequencing
In the same way that Siemens views the ViiV and Tocagen transactions as the seeds of a companion diagnostics business, Siemens views its partnership with Illumina as an initial step toward adopting next-generation sequencing for clinical assay development.
The deal focuses on making Siemens' Trugene HIV-1 molecular tests compatible with Illumina's MiSeq benchtop sequencer. The test, which has been on the market for about a decade, was the first DNA sequencing-based HIV test approved by the US Food and Drug Administration.
Trugene is based on a technology called slab gel sequencing, and while the test has been "a really good product," Siemens believes that next-gen sequencing will be a transformational technology, Hawkins said, and the company "really needed to be part of the conversation around how next-gen [sequencing] was going to impact clinical diagnostics."
Trugene, he said, offered Siemens a "great example" of what it would take to move an existing assay onto a new technology platform, and in the process shed light on how next-gen sequencing could have clinical use.
While next-gen sequencing has generated a tremendous amount of buzz in the research community, it has had limited use in the clinic, and it may still be several more years before clinical adoption occurs in any meaningful way. Recently, Quintin Lai, an analyst at RW Baird, said in a research note that broad use of the technology will take time as hurdles such as regulatory and payor support, and as well as cost, still need to be addressed.
Attendees at the Future of Genomic Medicine conference earlier this month expressed doubt that the clinical $1000 genome was obtainable in the near term because although sequencing costs are going down, the "all-in" costs including training and interpretation remain high enough to keep clinical sequencing prohibitively expensive, Lai said.
Additionally, Hawkins said that other issues, such as how regulators will view the technology in the clinic, the quality of the data, the laboratory workflow, and how the data should be interpreted and analyzed, still need to be addressed.
Siemens has programs seeking to tackle many of these questions, and its deal with Illumina is expected to provide some answers, at least in relation to the Trugene assay, Hawkins said.
Illumina is fighting to prevent a $5.7 billion hostile takeover by Roche, which launched its bid for the company in January. A spokesperson for Siemens told GWDN that a potential deal has no effect on its partnership with the sequencing technology firm, which it regards as "as a key element toward providing our customers with a broad, high-quality menu of solutions that address their laboratory diagnostic testing needs."
Siemens is in discussions with the FDA about its plans to move Trugene onto the MiSeq system, and it is comparing patients samples run side by side on Trugene and on a next-gen sequencing-based test. Within the next 12 months, Siemens hopes to move into formal clinical trials for the test and then to submit a regulatory filing, Hawkins said.
He added that in addition to the Trugene test, "we wholly anticipate future assays to be moved over onto next-generation sequencing as well as completely new assays that next-gen sequencing empowers," such as oncology assays.
Maturing Ecosystem
Siemens' renewed push into genomics comes at a time when other large, diversified firms that play in the space are also strategizing to increase their presence. An official with Novartis, for example, told GWDN in December that companion diagnostics is a key strategic focus for that company. A year ago, a Johnson & Johnson executive also outlined that firm's molecular diagnostics strategy.
Scientia's Glorikian said that companies such as Siemens want to see "a level of maturity in an ecosystem," so that they can understand the rules of the game.
"What we’ve seen recently is that the genomics ecosystem, particularly the companion diagnostics area, is slowly becoming better understood," he told GWDN. "Pharmaceutical firms are more actively seeking diagnostics partners … [and] regulatory requirements are becoming better understood.
"We believe that any organization that is serious about having a molecular diagnostics business has to be thinking about their companion diagnostics programs. But it is easier said than done," he added. "There is a level of complexity in the companion arena that most organizations may not appreciate yet."
Hawkins said that for Siemens it also is a matter of clinical value. More than a decade after the first draft of the Human Genome Project was released, "we've gone through the early research phases, and now we're starting to really see the clinical relevance of individual genes, and gene clusters and signatures, and how they are linked to disease," Hawkins said.
"We see molecular as an important part of diagnostics, and we see more and more molecular is a piece of the [clinical] equation," he added. "This is a jigsaw puzzle that you are building here when you're trying to give informed information to the physician, and … at Siemens we see molecular as a very important piece of the diagnostic equation going forward."
[The entry of SIEMENS into Genomics is far more important than the last September 1st entry by SAMSUNG. Siemens is not "just another global IT giant", but perhaps the most potent company of the certainly most potent country of Europe (Germany). The "low profile" for too long, is probably largely due to the "genomically ultraconservative" Germany - but "the growing acceptance of pharma and clinical relevance" has finally broken through and Germany is on a spectacular rise. The entire global equation is altered by this "game changer" - This entry can be discussed on the FaceBook page of Andras Pellionisz]
The Creative Destruction of Medicine
New York Times; BOOKS
Genomics as a Final Frontier, or Just a Way Station
By ABIGAIL ZUGER, M.D.
Published: February 27, 2012
The medical world is holding its breath, waiting for the revolution. It will be here any minute. Definitely by the end of the decade. Or perhaps it will take a little longer than that, but seriously, it’s right around the corner. More or less.
That’s the genomics revolution, with its promise of treatment focused on the individual rather than the group. At last, patients will be more than the product of their age, sex, ethnicity, illnesses and bad habits; treatments will be aimed like a laser at their personal genetic particulars, and if those genes are not quite what they should be, then those genes will be fixed.
Over the last few years, various breathless visions of this therapeutic future have been written out for public admiration. A particularly readable and comprehensive version can be found in Dr. Eric J. Topol’s new book, “The Creative Destruction of Medicine.”
Dr. Topol, a cardiologist and researcher at the Scripps Research Institute with the energy of 10 (if his prose style and his honor-laden biography are any indication), dispenses in short order with our current population-based medical strategies. They are wasteful and inexact, he points out, often marginally beneficial to the group and downright harmful to the individual.
He presents an array of far better ideas, a few now actually being practiced in rudimentary form. These include pharmacogenomics, in which specific genes that govern responses to medications are routinely assayed, and cancer treatments that probe tumors for specific genetic targets rather than relying on standard chemotherapy.
But that’s not all: Dr. Topol also points out that soon a person’s precise genetic data will be augmented by an extraordinary wealth of other digital data (provided by, say, the continuous monitoring of blood pressure, pulse and mood, and a variety of ultra-precise scans). The outcome will be nothing short of a new “science of individuality,” one that defines individuals “at a more granular and molecular level than ever imaginable.”
Praise:
The Creative Destruction of Medicine by Eric Topol
“A must-read that lays out a road map for how new technologies in genomics, information technology, and mobile medicine may completely change the way we treat and prevent illness. It’s highly recommended, because Topol has a unique vantage point: he’s one of the few researchers to have played an important role in the old, mass-market medicine world and the newer, genetically focused one.”
Forbes
“Topol demonstrates how the digital revolution can be used to change individual care and prevention, and even the economics of American healthcare. From cell phones that automatically collect medical data, to biosensors, advanced imaging, individualized prescriptions and gene-specific drugs, Topol’s book leads readers through science-fiction-sounding scenarios that may soon be a reality.”
Salon
“The Creative Destruction of Medicine - an allusion to economist Joseph Schumpeter’s description of ‘creative destruction’ as an engine of business innovation is a venture capitalist’s delight, describing dozens of medical technologies that show great promise. The book also provides colorful anecdotes about Dr. Topol’s own sampling of these products, as both a doctor and stand-in patient…. [The book’s] most important contributions are in portraying how medical innovation will coalesce to change clinical practice and what the coming changes mean for today’s policy debates…. In Dr. Topol’s vision, innovation that enables real-time diagnosis and personalized treatments is a certainty, though not because reluctant or ‘sclerotic’ doctors accept it or because Washington wills it into being. A seductive technology that works like a dream and improves lives will set off a consumer clamor, whether the new tool is an iPhone 4S or an implantable blood-sugar meter.”
Wall Street Journal
“Topol does an excellent job of explaining all, and his enthusiasm for the possibilities of what the future holds is infectious. It can only be hoped, as the convergence he so convincingly predicts materializes, that the barriers erected by the gatekeepers of yesterday’s paradigms will be easily dismantled so as not to impede the benefits it promises.”
Boston Globe
“An eye opening account of why conventional medicine is doomed…. [C]ompelling stuff…. [T]he book provides an excellent summary of the current state of medical genetics and how fast it is progressing, with examples that may surprise even those working in medicine.”
New Scientist
“Topol makes the case that the masses of macro-data at our fingertips (literally), will unleash micro-level diagnostic and curative solutions never before imagined or hypothesized. It’s a remarkably bold vision that many experienced physicians will call naïve since it defies conventional wisdom - which is precisely why I think he’s on to something big.”
Longwoods eLetter
“[A] prescient view of the near future of medicine…. Every patient should read this book in order to understand the rapidly evolving role in they play in their own care…. The Creative Destruction of Medicine is a call to action for doctors and patients alike. We must see our world and our job as doctor and patient very differently. In a profession so uncertain of its future, we need precisely the vision and critical dialog offered here…. I suspect that 150 years from now when historians are looking back at the most dramatic flexion point in medicine’s history they’ll reference this book as one of the first to identify the start of medicine’s creative destruction.”
Bryan Vartabedian, MD, 33 Charts.com
“Modern medicine needs a makeover. Topol, of the Scripps Research Institute, believes the process begins with embracing the digital world. His plan involves genomics, wireless biosensors, advanced imaging of the body, and highly developed health information technology. Smartphones will tie these elements together to make health care individualized,efficient, and accessible. Topol foresees a future medical landscape characterized by virtual house calls, remote monitoring, and a lessened need for hospitals.”
Sharon Begley, Kirkus Reviews
“The book makes a compelling case for the role of digital technology in bringing nimbleness to ossified healthcare systems worldwide.”
Calestous Juma, Harvard Kennedy School
“Our sequencing of the human genome eleven years ago was the beginning of the individualized medicine revolution, a revolution that cannot happen without digitized personal phenotype information. Eric Topol provides a path forward using your digitized genome, remote sensing devices and social networking to place the educated at the center of medicine.”
J. Craig Venter,Chairman and President, J. Craig Venter Institute
“Eric Topol gives us an eye-opening look at what’s possible in healthcare if people can mobilize to charge the status quo. The Creative Destruction of Medicine is simply remarkable.”
Clayton M.Christensen, Robert and Jane Cizik Professor of Business Administration, Harvard Business School,and author of The Innovator’s Dilemma
“Eric Topol has been a longtime innovator in healthcare. In The Creative Destruction of Medicine, he cites the big waves of innovation that will save healthcare for the future. Real healthcare reform has not yet begun, but it will. The Creative Destruction of Medicine lays out the path.”
Jeffrey Immelt,Chairman and CEO of General Electric
“This is the one book to read for a complete and clear view of our medical future, as enabled by the convergence of digital, mobile,genomic, and life science breakthroughs. Dr. Topol explains how iPhones, cloud computing, gene sequencing, wireless sensors, modernized clinical trials,internet connectivity, advanced diagnostics, targeted therapies and other science will enable the individualization of medicine - and force overdue radical change in how medicine is delivered, regulated, and reimbursed. This book should be read by patients, doctors, scientists, entrepreneurs, insurers,regulators, digital engineers - anyone who wants better health, lower costs, and participation in this revolution.”
Brook Byers,Partner, Kleiner Perkins Caufield & Byers
“Eric Topol is that rare physician willing to challenge the orthodoxies of his guild. He recognizes that in the U.S., health care business-as-usual is unsustainable. But he does not despair. He bears witness to the rise of Homodigitus and the promise it holds to upend the inefficiencies and dysfunction so entrenched in clinical medicine. The Creative Destruction of Medicine is a timely tour de force. It is a necessary heresy.”
Misha Angrist, Assistant Professor, Duke Institute for Genome Sciences & Policy, and author of Here is a Human Being
“Much of the wealth created over the last decades arose out of a brutal transition from ABC’s to digital code. While creating some of the world’s most valuable companies, this process also upended whole industries and even countries. Now medicine, health care, and life sciences are undergoing the same transition. And, again, enormous wealth will be created and destroyed.This book is a road map of what is about to happen.”
Juan Enriquez,Managing Director, Excel Venture Management, and author As the FutureCatches You
“Health care is poised to be revolutionized by two forces - technology and consumerism - and Dr. Eric Topol explains why. One-size-fits-all medicine will soon be overtaken by highly personalized,customized solutions that are enabled by breakthroughs in genomics and mobile devices and propelled by empowered consumers looking to live longer, healthier lives. Fasten your seat belts and get ready for the ride—and learn what steps you can take to begin to take control of your health.
Steve Case,co-founder, AOL, and founder of Revolution LLC
“If we keep practicing medicine as we know it today, healthcare will become an unbearable burden. We are in a real race between healthcare innovation and the resistance to change of the medical system. In a comprehensive and well researched tour de force, Eric Topol, always a clear and uncompromising thought leader of his generation, challenges us to imagine the revolutionary potential of a world where medical information no longer belongs to a few and can be automatically collected from the many to greatly improve healthcare for all. This is a must read!”
Elias Zerhouni, MD, President, Global R&D, Sanofi and former director, National Institutes of Health
“Dr. Topol believes that medicine, catalyzed by extraordinary innovation that exploits digital information, is about to go through its biggest shakeup in history. His newest book calls for a ‘jailbreak’ from the ideas of the past. In the next phase of medicine, powerful digital tools including mobile sensors and advanced processors will transform our understanding of the individual, enabling creative ‘mash-ups’ of data that will spark entirely new discoveries and spawn ultra-personalized health and fitness solutions. And with over 5.7 billion mobile connections worldwide, the mobile technology platform will have a major impact on that vision - leading to what Dr.Topol describes as nothing less than a ‘reboot’ of the health care system. Qualcomm, and its partners all around the world, are working to bring wireless innovations to market that will contribute to the solution. And we share Dr.Topol’s view that individual consumers have the opportunity, and the power, to increase the pace of the titanic change that’s coming.”
Paul E. Jacobs,PhD, Chairman and CEO, Qualcomm Incorporated
“What happens when you combine cellular phone technology with the cellular aberrations in disease? Or create a bridge between the digital revolution with the medical revolution? How will minute biological sensors alter the way we treat lethal illnesses, such as heart attacks or cancer? This marvelous book by Eric Topol, a leading cardiologist, gene hunter and medical thinker, answers not just these questions, but many many more.Topol’s analysis draws us to the very front lines of medicine, and leaves us with a view of a landscape that is both foreign and daunting. He manages to recount this story in simple, lucid language—resulting in an enthralling and important book.”
Siddhartha Mukherjee, author of The Emperor of All Maladies: A Biography of Cancer
“What happens when the super-convergence of smart phones further combines with million-fold lower-cost genomics and diverse wearable sensors? The riveting answer leads to a compelling call to activism—not only for medical care providers, but all patients and everyone looking for the next ‘disruptive’ economic revolution. This future is closer than most of us would have imagined before seeing it laid out so clearly. A must-read.”
George Church,Professor of Genetics, Harvard Medical School
“Dr. Topol is the top thought leader in medicine today, with exceptional vision for how its future can be rebooted. This book will create and catalyze a movement for the individualization and democratization of medicine - and undoubtedly promote better health care.”
Greg Lucier, CEO, Life Technologies
“Eric Topol is the perfect author for this book.He has a unique understanding of both genomics and wireless medicine and has a remarkable track record as a charismatic pioneer, visionary, and change agent in medicine. I’m sure this book will reach a very large number of people with information that can both empower and help transform their lives for the better.”
Dean Ornish, M.D., Founder and President, Preventive Medicine Research Institute, and author of The Spectrum
“Dr. Eric Topol is an extraordinary doctor. He’s started a leading medical school, identified the first genes to underlie development of heart disease, led major medical centers, and been a pioneer of wireless medicine. But he is also a remarkable communicator - one of the few top-flight scientists in medicine to be able to genuinely connect with the public. He was, for example, the first physician researcher to question the safety of Vioxx - and unlike most who raise safety questions, actually succeed in bringing the concerns to public attention. I have known and admired Dr. Topol for a long time. I recommend him highly.”
AtulGawande, M.D., author of The Checklist Manifesto
“Eric Topol is uniquely positioned to write such a timely and important book. He leads two institutions - one in genomics and one in wireless health - that will each play a huge role in transforming medicine in the twenty-first century. From this vantage point, he can see unifying themes that will underlie the coming revolution in population and personal health, and he communicates his vision with vibrant energy. Everyone will want to read this book.”
James Fowler,Professor of Medical Genetics and Political Science, UC San Diego, and author of Connected
[Dr. Eric Topol says in his book that "the train has already left the station". Indeed, this column cited on Oct. 15, 2007 "The Year of Miracles" Newsweek article by Prof. Lee Silver . As the column's remarks observed, however, in five years ago (2007), precisely since the article foreshadowed a "Creative Destruction of Medicine", the artilce "Annus Mirabilis" by Prof. Lee Silver was published in all Editions of Newsweek - EXCEPT in the US-Canadian Edition (where there wasn't a single word about it). This colum cited the paper from the European Edition...
The Year of Miracles (Annus Mirabilis) by Lee Silver (full paper)
Likewise, the establishment did not take it very well that Genomics became Informatics (in 2002 Leroy Hood framed Genome Informatics in terms of Systems Biology, while Andras Pellionisz defined it as a Fractal System; FractoGene 2002). Particularly crass detractors came to the fore when Pellionisz published the peer-reviewed science paper "The Principle of Recursive Genome Function" (popularized by the Google Tech Talk YouTube in the same year of 2008), and subsequent YouTube-s on Personal Genome Computing and Personalized Genome Assistant mobiles (for an age when e.g. in India many more people carry smart phones than the number with access to a sewage system):
A scalable global business model and a (sub)continent offers support of Pellionisz' Fractal Approach: Ten slides winning over a (sub)continent for funding Pellionisz' Fractal Approach
Dr. Andras Pellionisz was a Guest of Honor as well as Keynote Speaker at the ICSCI-2012 "International Conference on Systemics, Cybernetics and Informatics" in Hyderabad, Andhra-Pradesh (February 15-18, 2012, organized by Prof. E.G. Rajan), and subsequently at the ICETT-2012 "International Conference on Emerging Technology Trends on Advanced Engineering Research" in Kollam, Kerala (February 20-21), organized by Prof. B. Kumar. His biophysics-approach, using tensor- and fractal geometry to unify genomics and neuroscience was awarded at both international congresses. In the third state of Karnataka, in Bangalore, he conducted discussions in addition to Academia with Private sector, and Government officials. As also reflected by a visit a few weeks earlier by Dr. Francis Collins NIH Director (see below in this column), India flexes muscles to answer the challenge that the genome informatics initiatives of China (BGI) and Korea (Samsung) represent.
Roche’s Illumina Bid May Spur Buying in DNA Test Land Grab
January 31, 2012, 12:32 PM EST
Bloomberg
By Robert Langreth, Meg Tirrell and Ryan Flinn
(Updates with closing share price in the fifth paragraph.)
Jan. 26 (Bloomberg) -- Less than 10 years after the first human genome was decoded, Roche Holding AG’s hostile $5.7 billion bid for Illumina Inc. may spark additional deals as companies race to bring DNA scanning into routine medical use.
Illumina competes with Life Technologies Corp., Affymetrix Inc. and other companies to sell gene-decoding machines that are just starting to be used to tailor therapies for patients with cancer and inherited diseases. While scientific excitement around genome sequencing is high, the companies’ shares have plummeted over the last year because their target customers are mostly scientists dependent on grants in a tough economy.
Getting the technology out of the lab and into doctors’ offices and hospitals could vastly expand the existing $1.5 billion market for gene sequencing machines, industry officials and analysts said.
“This is going to be an enormous opportunity, and now you see it unfolding,” said Greg Lucier, chief executive officer of Life Technologies, based in Carlsbad, California, in a telephone interview. The bid Roche is an acknowledgment that DNA mapping is key to the future of diagnostics, particularly involving its use in cancer treatment, he said.
Illumina today adopted a so-called poison-pill takeover defense in which shareholders will receive one preferred stock purchase right as a dividend for each common share held as of the close of business on Feb. 6.
The San Diego-based company fell 4.5 percent to $52.65 in New York trading. Roche, based in Basel, Switzerland, rose less than 1 percent to 160.20 Swiss francs in Zurich.
‘Unwilling to Participate’
Roche made its hostile offer directly to Illumina shareholders after saying the testing company was “unwilling to participate in substantive discussions,” according to a statement yesterday.
The rights agreement adopted today by Illumina can block a hostile bid by making it prohibitively expensive. Should Roche or another bidder own 15 percent or more of Illumina’s stock, other shareholders will be able to exercise the rights to buy new common stock, diluting the stake of the prospective bidder.
Before initial talk of a takeover attempt surfaced in December, the shares of Illumina -- which draws a third of its revenue from researchers funded by the National Institutes of Health -- had dropped 58 percent over 12 months. Life Technologies and Affymetrix also tumbled, leaving them as vulnerable as Illumina to buyout bids, said Bill Bonello, an analyst with RBC Capital Markets in Minneapolis.
‘Intensity M&A Angle’
“This will intensify the M&A angle people will look at with these stocks,” Ross Muken, an analyst with Deutsche Bank Securities Inc. in New York, said in a telephone interview.
The human genome was first sequenced in 2003. The market for machines that map DNA has been “fast-growing” over the last five years, said Daniel O’Day, chief operating officer of Roche’s diagnostics division, in a conference call yesterday.
“We expect that to continue into the future,” O’Day said. “Today, it is over a $1 billion marketplace, and we expect that to be over a $2 billion marketplace in 2015.”
The devices sold by Illumina, Life Technologies and Affymetrix search through DNA coding that contains the instructions for making all human cells. Scientists use the technology to build an understanding of how variations or mutations found by the machines contribute to disease.
Cancer Variations
This is particularly true in cancer, where variations can contribute to uncontrolled cell growth. Doctors want to use genetic data to aim cancer treatments precisely at these variations, and stop only diseased cells from growing. Genome sequencing is also helping doctors understand, diagnose and, in some cases, treat mysterious childhood diseases that had previously taken years to identify.
The National Human Genome Research Institute has allocated funds to determine how to integrate individuals’ genetic information into day-to-day clinical care issues, such as the appropriate dosing of drugs.
Already, U.S. regulators are working with drugmakers in approving cancer drugs tied to companion genetic tests. Pfizer Inc.’s crizotinib, a treatment for a form of lung cancer caused by a genetic defect, was approved in August along with a companion diagnostic made by a unit of Abbott Laboratories that determines whether a patient has the abnormal gene.
Roche, the world’s biggest developer of cancer medicines, has particular experience with gene-targeted therapies.
Herceptin, Zelboraf
The company sells the breast-cancer drug Herceptin, one of the first cancer medicines aimed only at a subset of patients whose tumors have a particular genetic abnormality. In August, it garnered U.S. regulatory approval for Zelboraf, a melanoma drug that works on patients whose tumors have a certain gene mutation. Roche also sells a companion test with Zelboraf.
While Roche may be a pioneer in its bid for Illumina, it isn’t clear that other drugmakers will seek to acquire similar companies unless they already have a toe in the business, said Les Funtleyder, a portfolio manager with Miller Tabak & Co. in New York, whose fund owns Illumina shares.
“It seems like a bit of a leap for a pharmaceutical company to get into a whole new line of business,” Funtleyder said in a telephone interview. “You don’t need a sequencer to develop a companion diagnostic; you just need the sequence. And you can outsource that.”
Funtleyder cited General Electric Co. and Abbott as companies with existing businesses that may consider a similar acquisition.
‘Best in Class’
With Illumina, Roche is “buying best in class,” said Peter Lawson, an analyst with Mizuho Securities in New York, by telephone. “Illumina’s one of the most interesting companies in this space. They’ve been serial innovators, they’ve been great acquirers of technology and great executors.”
Illumina has been in a race to develop the first machine to be able to parse the building blocks of life in a day, rather than weeks or months. It announced Jan. 10 that it would market such a machine in the second half of this year. Life Technologies said on the same day it had reached the same goal. The current Illumina machines can sequence five human genomes in 10 days, according to the company.
Erik Gordon, a professor at the Ross School of Business at the University of Michigan in Ann Arbor, sees Roche and Illumina as a perfect fit.
“On its own, Illumina will have trouble reaching the broader clinical markets for its devices and will remain dependent on the shaky government-funded markets,” Gordon said in an e-mail. ‘As part of Roche, it quickly gets through the door at clinics worldwide.”
At the same time, he said, “Roche gets another product to run through its sales channel.”
Harvard Geneticist
George Church, a Harvard Medical School geneticist who has founded and advised numerous companies in the industry, said Roche “probably had their eye on Illumina for a long time and were waiting for the price to come down. They knew it was a valuable company; why not buy it at its lowest point?”
The sequencing technology is moving so fast the Illumina technology may become quickly outmoded, said Craig Venter, who led a private team that sequenced one of the first two human genomes a decade ago and runs the J. Craig Venter Institute in Rockville, Maryland.
“I don’t understand why Roche would do this deal when the technology is changing so rapidly,” he said. “I am puzzled.”
When Venter was racing a government team to scan the first human genome, he needed 300 expensive sequencing machines in 100,000 square feet of lab space, he said. Now researchers can build a world-class facility with just 10 smaller desktop machines, he said.
Four Years Difference
“One of these new machines replaces 100 of our old machines four years ago,” he said.
Michael Pellini, chief executive officer of Foundation Medicine in Cambridge, Massachusetts, a company that sells a test looking at 200 cancer genes, said the research market for the machines is saturated while the far bigger market of potential routine medical use is just emerging.
“This technology has not crossed over into the clinical world in earnest,” Pellini said in a telephone interview. “That is the big disconnect.” Roche can help bridge the divide with its expertise in diagnostic tests, he said.
Roche’s pursuit of Illumina reflects the growing focus of health-care companies on personalized medicine, said Susan Clark, professor of medicine at the University of New South Wales, whose lab at the Garvan Institute in Sydney uses Illumina equipment to study cancer gene expression.
The challenge is to better match their cancer therapies with the specific patient populations who will be benefit most, she said. Faster DNA scanning technologies could help, she said.
“A lot of money has been spent by pharmaceutical companies to try to find designer drugs,” Clark said in an interview. “But with designer drugs, you need to know the population that they will target because they are so expensive.”
--With assistance from Jason Gale in Singapore, Naomi Kresge in Berlin and John Lauerman in Boston. Editors: Reg Gale, Andrew Pollack
To contact the reporters on this story: Robert Langreth in New York at rlangreth@bloomberg.net; Meg Tirrell in New York at mtirrell@bloomberg.net; Ryan Flinn in San Francisco at rflinn@bloomberg.net
To contact the editor responsible for this story: Reg Gale at rgale5@bloomberg.net
[I predicted a "Dreaded DNA Data Deluge" in my Google Tech Talk YouTube in 2008 since Sequencing, with billions of dollars of investment became one half of "Industrialization of Genomics" - while the other half, Analytics was not attended, and therefore an oversupply of sequences produced an unsustainability. As a result, all four major "Sequencing Companies" lost much of their valuation (illustrated by stock-market graphs added to the New York Times article "DNA Sequencing Caught in a Deluge of Data", November 30th below). There should be no question that a major wave of merger/acquisition of the type of Roche/Illumina will occur - but in itself will not solve the problem. Without the two types of IT (Information Technology and Information Theory), requiring the SAMSUNG-type of genome analytics service, Industrialization of Genomics will remain unbalanced. If/when "Roche" will acquire "Illumina", look for their next step, to globalize their solution with IT, preferably in Asia]
Genome Informatics, Globalized; - USA - China - Korea - India
Francis Collins (in Bangalore)
How much does the US spend on medical research?
Typically, the NIH invests $30 billion every year. But I should say the budget's been about there for almost eight years. We're having difficulties in fiscal deficit, so medical research may not grow much in the near future. Thoughtful decisions have to be taken on what we want to do.
What strengths do you see in Indian institutions? How are they compared to China?
India's great strength now is its IT and computational capacity. Biology is now more a computational science. To understand diseases like diabetes and cancer, we need computational strategies to sift through vast datasets. India can provide that asset.
India is on right track for biomedical research, says NIH director
Date:2011-12-05houhaizhen
Bangalore, Dec 05, 2011: Currently on a tour of India to improve collaborations between research institutes in India and the NIH, Dr Francis Collins, director, NIH, USA, spoke about the impact NIH funding has had on research in India and how it intends expand its presence in India.
National Centre for Biological Sciences (Bangalore)
Francis Collins in Bangalore
--
Andras Pellionisz, Board of Advisors to ELOGIC Technologies in Bangalore, tours Bangalore, Hyderabad, Trivandrum
China excels in Genome Informatics by running in BGI (Shenzhen) the World's largest capacity of Genome Sequencing Machines (all USA-made), and employing up to 4,000 computer scientists, with an every age of 27, with access to the World's fastest supercomputers (a hybrid of Intel serial chips and NVIDIA graphics parallel chips, all USA-made). Korea announced by Sept. 1, 2011 that SAMSUNG started to provide a Global Genome Analytics Service.
India's strengths, as Francis Collins pointed out in Bangalore, to deploy her massive IT and mathematics expertise towards personalized therapy against cancer, combined with clinical trials.
Andras Pellionisz, his science presentation featured above in Hyderabad, builds collaborations of Silicon Valley of California with "Silicon Valley of India"; Bangalore.
UNESCO’s Memorial Year Honours János Szentágothai
[János Szentágothai - AJP]
As decided by the general assembly of UNESCO, the year 2012 is to be dedicated to honour the 100th birthday of János Szentágothai, a formal president of the Hungarian Academy of Sciences and a groundbreaker in his field. According to Ambassador Katalin Bogyay, the memorial year provides a unique opportunity to showcase the achievements of Hungarian science and Hungarian culture in general to a wider audience through the legacy of the great Hungarian scientist.
UNESCO has long been taking part in commemorating the anniversaries of historical events and outstanding personalities. The national committees of the organisation may make such proposals each year. Headed by member of HAS József Hámori, the Hungarian National Committee initiated a Szentágothai Memorial Year, a proposal supported by the Jury of Experts and the Executional Council and finally was accepted by UNESCO's General Assembly.
"According to my plans, an exhibition and conference are going to be organised commemorating the greatness of János Szentágothai in the centre of UNESCO in Paris. Through introducing his life to an international audience, we are to bring the achievements of Hungarian science into the forefront, while also drawing attention to the responsibility of science and to the connection between science and art", Katalin Bogyay said. "As a Christian thinker and music teacher Franz Liszt had been an ambassador of Hungarian culture in his age, so was János Szentágothai committed to both science and art, a true renaissance man of Hungarian intellectual life."
"János Szentágothai was a real founder of a school of thought. We, today's Hungarian brain researchers, are all standing on his shoulders", former student József Hámori said. Member of HAS and President of the Hungarian UNESCO Committee, professor Hámori started his career at the Department of Anatomy of Pécs University in 1955 where János Szentágothai had established a research community whose members not only improved in expertise but also had the chance to enrich the spiritual-cultural aspects of their personalities. "We worked from 9 am. until late evening every day, but were not doing science exclusively", József Hámori recalls. "The art of painting, baroque music was just as often the topic of our discussions as was poetry." However it was his findings in brain research that had earned him world fame. Among these were his results on the functions of the spinal cord, the cerebellum, and the structure of the neocortex, making him a Nobel-prize nominee several times.
Besides being an avid researcher, János Szentágothai had great affection for teaching and always encouraged his students to pass on their knowledge. He considered education as a crucial aspect of science, not only for the sake of the next generation, but also because he believed it's the perfect way for teachers to keep their knowledge up-to-date. Professor Szentágothai authored Functional Anatomy and the Atlas of Human Anatomy with Miklós Réthelyi and Ferenc Kiss respectively.
János Szentágothai was the President of the Hungarian Academy of Sciences between 1977 and 1985. Besides holding several national awards, he was also a member of many international organisations, such as the Royal Society, and the Papal Academy of the Vatican. He was an honorary doctor at several universities, among them the University of Oxford. Professor Szentágothai also played a significant role in the public life of Hungary. He was a representative of science in the Hungarian Parliament until his death in 1994.
[Why was Prof. Szentágothai passed over for Nobel Prize? - AJP]
The simple answer is that the Nobel Committee did not vote him in. At what rate and why not, is only guesswork, since the Minutes of the Committee are classified for 50 years.
As a pupil of the “Szentágothai school” since 1967, my belief is that the reason might be that the “Grand Old Man” thought and acted in terms of “Schools of Science”, not limited to that of his own. For the Nobel Committee, however, it is much easier to award the Prize to focused efforts, especially since the Science Prizes are to be awarded to living persons, divided at a maximum to three people.
Prof. Szentágothai built his school of science to a large degree on that of the schools of “vestibulo-ocular cerebellar systems” of Drs. Bárány, Hőgyes and Lenhossék – wherein Dr. Bárány was already some time ago awarded by the Nobel Prize. In itself, this could not be a major problem, as both Drs. Kornberg and his son were awarded. In Szentágothai’s school, like in all schools of excellence, there was more than a single eminent course.
There seems to be no doubt that the legacy of Hungarian schools of science, and the international schools of science that Prof. Szentágothai built a solid alliance with, made the cerebellar sensory-motor system a pre-eminent focus of his ouvre – with the cerebellum making up to 1/3 of the brain with birds top make them capable of coordinated flight.
By 1967 János Szentágothai was about ready with the proof of their Springer book, co-authored with the leading British-Australian-American Sir John (Eccles) Nobel Laureate and Japanese Masao Ito. The title was “The Cerebellum as a Neural Machine”. This splendid accomplishment could have been the reason for János Szentágothai NOT having been awarded by a Nobel. Why? The bestseller book, according to Google Scholar under “Eccles” was cited 2,117 times – that is far more than any citation of Szentágothai’s own publications (highest is 472, often in different fields). Thus, it could be difficult to award a Nobel for the authors of the book, divided to the maximal three allotted recipients who might share the Prize. For one issue, this would have been the second Nobel to Sir John (Eccles). Not impossible, for the precedents of double Laureates. However, the electrophysiology of the cerebellum, comprised in the book, was the result of cardinal collaborative effort with Rodolfo Llinás and other colleagues, and similarly the electronmicroscopy laid out in the book was performed largely by József Hámori. Third, the contribution to the book by the Japanese Masao Ito was his flabbergasting discovery that the sole output of the cerebellum, from the Purkinje neurons, are inhibitory (as opposed to the expected excitatory action). The book on the “Neural Machine” simply could not interpret this experimental result. As a conclusion, on the very last page of the book (pp. 177.) the main three authors, altogether at least five individuals, over the limit of maximal three for any Nobel, confessed in effect that “we know everything about the neural networks of the cerebellum, except how it works as a Neural Machine”. The Nobel Committee could have wondered about the best stance towards a book that verbatim predicted by the last sentence of the book that “It is essential to be guided by the insights that can be achieved by communication theorists and cyberneticists who have devoted themselves to a detailed study of cerebellar structure and function. We are confident that the enlightened discourse between such theorists on the one hand and neurobiologists on the other will lead to the development of revolutionary hypotheses of the way in which the cerebellum functions as a neuronal machine and it can be predicted that these hypotheses will lead to revolutionary developments in experimental investigation”. Pondering over such a mesmerising conclusion, perhaps they thought that “there are too many authors now and the conclusion still awaits some time” and decided to reconsider any award for the function of the cerebellum later.
It is noteworthy that Szentágothai was ahead of his time far further than he could have ever envisioned. Springer is just proofing their handbook “The Cerebellum and Cerebellar Diseases”, in which a Chapter on Recursive Genome Function of Cerebellum: Geometric Unification of Neuroscience and Genomics” reveals that the mathematical “trick” of the cerebellum (a topic of neuroscience) is also found, in an even more profound manner, in genomics. The function of the cerebellum as a neuronal machine is to turn the independent, sensory-type motor intentions (that are mathematically covariant) into physically properly assembled motor-execution components (that are mathematically contravariant). This “invention” by mother Nature for the cerebellum is fairly recent in evolution – it only goes back to about 400 million years, when the sharks, equipped with the emerging cerebellum could outswim less coordinated competitors.
Szentágothai, who passed away in 1994, could not have known about “RNA interference” (discovered by Fire and Mello 4 years later, in 1998, to be awarded by Nobel in 2006). “The RNA system acting as a genomic cerebellum” (by both contravariant, cRNA and covariant ncRNA, by their interference comprising the metric of coordinated genome function) is a far more ancient invention about 530 million years ago by mother Nature; it resulted in the so-called “Cambrian explosion” in evolution, by enabling coordinated genome function of multi-cellular organisms.
Our beloved “Grand Old Man” (Prof. Szentágothai) would get truly excited, since he was the epitomy of scientific curiosity and its ultimate reward (understanding) - regardless of the destination of Prizes of any kind, if at all.
János Szentágothai, prof. of Semmelweis University Medical School, Budapest [at right], and András Pellionisz, prof. of New York University Medical School, New York [at left]
Power in Numbers [math must grab genomics, beneath neuroscience – AJP]
[Eric Lander is right, again. In the video above, essentially he says: For genomics, the homework is to explain cerebellar brain function by its intrinsic math. This is exactly the strategy Tensor Network Theory (applied to Recursive Function of Cerebellar Neural Nets) and its Unification with the Fractal Approach to Genome Function followed. Lander skipped, for a number of reasons, the few decades I invested in “geometrization of neuroscience”– and Lander zoomed right into mathematization of genome (as it is “informatics”). While as a Science Adviser to the President, the US might follow his advice (“Mr. President, the Genome is Fractal!”) because of the inertia of the medical establishment countries with smaller legacy but faster growth in their GDP, plus mobilizing their mathematics-physics-engineering formerly honed to create their own nuclear industry, might even overtake the US in certain segments of “Industrialization of Genomics”. Fortunately, Dr. Lander is also schooled in economics – thus along with fellow Havard Professor economist Dr. Juan Enriquez (see his decade-old-bestseller “As the Future Catches You”) the US can maintain leadership in “Genome Based Economy”. – AJP]
“I took random classes in Harvard about the brain… To know about the brain, you have to learn about cell biology… to know about cell biology you have to learn about molecular biology… I have to know about genetics! “
“I began to appreciate that the career of mathematics is rather monastic,” Dr. Lander said. “Even though mathematics was beautiful and I loved it, I wasn’t a very good monk.” He craved a more social environment, more interactions.
“I found an old professor of mine and said, ‘What can I do that makes some use of my talents?’ ” He ended up at Harvard Business School, teaching managerial economics.
He had never studied the subject, he confesses, but taught himself as he went along. “I learned it faster than the students did,” Dr. Lander said.
Yet at 23, he was growing restless, craving something more challenging. Managerial economics, he recalled, “wasn’t deep enough.”
He spoke to his brother, Arthur, a neurobiologist, who sent him mathematical models of how the cerebellum worked."...
Paired Ends, Lee Hood, Andras Pellionisz - 2012 a Year of Turing and Year of Turning
January 1st, 2012
[Genomeweb - AJP]
Genomeweb paired us prompted by our respective press releases, signaling the general trend of new global alliances that bridge the USA to Asia. However, as 2012 begins, the Centennary Year of Alan Turing (born in 1912), some deeper analysis may be warranted. This Centennary year closes a decade of fierce struggle. It starting by the passing of Ohno (“Junk DNA” since 1972) in 2000, and Crick in 2004 (“Central Dogma” since 1956), ENCODE’s belated admission in 2007 that “Junk DNA is anything but”, and the Principle of Recursive Genome Function (paper and YouTube in 2008, Unification of Neuroscience and Genomics in print 2012).
This year may become another turning point in mathematical theory of genome function, with great advances in deciphering cancer, see the potential of Full DNA Sequencing (and Analytics) in cancer diagnosis and personalized therapy e.g. in this YouTube by Matthew Ellis (University of Washington in St. Louis).
A Decade ago (2002) both Dr. Hood and I went on our respective records with the notion that “Biology is an Informational Science”; see Lee Hood and Andras Pellionisz.
LeRoy Hood (2002), in his Commemorative Lecture acceptance speech of the Kyoto Prize in Advanced Technologies announced that he was writing a book on “The Living Code: Biology As An Informational Science” assuming Bertalanffy’s “General Systems Theory” (1956, Dover, New York) as his intellectual foundation.
Andras Pellionisz (2002) , for better or worse, specifically pointed out the mathematics intrinsic to genomes with his FractoGene (2002). The “Fractal Approach to DNA” – after the magical 7 years of silencing – is now boosted by the group of Eric Lander (Science Advisor to the President, featuring fractal folding of DNA on Science magazine cover issue, Oct. 2009 based on decades-old pioneering by Alexander Grosberg). Further, a Boston group of four (MIT/Broad/Harvard/Dana-Farber), with Leonid Mirny in both the 2009 paper and now, found in 2011 that “fractal defect” (in their case, a Copy Number Variation, CNV) blocked the see-through transparency of 3D Hilbert-curve, becoming the root cause of cancer.
My personal FractoGene (interlaced with Tensor Network Theory) is a lineage from relativistic tensor geometry brought into a synthesis of the quantum-theory-inspired-Schroedinger school of thought; Schroedinger, “What is Life?”, 1944). I was intellectually imprinted for life by the Hungarian Edition of John von Neumann “The Computer and the Brain” (1958), where on the last page von Neumann posthumously made a visionary statement. Von Neumann was tragically short-lived 1903-1957, quite possibly exposed to nuclear radiation while participating in the Manhattan-project, died of bone cancer. His statement was that whatever the intrinsic mathematics Nature uses in biological systems (notably in brain function) is certainly not the mathematics we know and that he used to create computers.
Perhaps the most revealing article is by Alan Turing, born a Century ago, who published a seminal article well over half a Century ago (1952), before the Double Helix was ever discovered, in 1952 (in Phil. Trans. R. Soc. Lond. B.)
“The Chemical Basis of Morphogenesis”. Its Chapter 13. was entitled “Non-linear theory. Use of Digital Computers”. Aptly, he wrote: “Most of an organism, most of the time, is developing from one pattern into another, rather than from homogeneity into a pattern. One would like to be able to follow this more general process mathematically also. The difficulties are, however, that one cannot hope to have any very embracing theory of such process, beyond the statement of the equations. It might be possible, however, to treat a few particular cases in detail with the aid of a digital computer”.
Neither Ludwig von Bertalanffy (1956), nor John von Neumann (1958) had time enough to specify the math intrinsic to coordinated genome regulation, in spite of early pioneering by Schroedinger (1944) predicting in his essay “What is Life?” that covalent Hydrogen-bondings over an aperiodical crystal (later to be found as DNA Double Helix in 1953), along with other pioneering by Norbert Wiener’s Cybernetics (1949) and the departure from war-time, having deciphered cryptography, to biology by Alan Turing (1952).
In view of the above, it would be grossly unfair to take the credit of a single decade of (2002-2012), despite all turf-protecting-below-the-belt-strikes by the few mathematically unseasoned, for the now prevailing notion that the sine qua non of progress is the software enabling intrinsic algorithm of genome informatics.
I was lucky enough to be exposed at the level of a personal friend to Edward Teller in his late years. Teller was Heisenberg’s Ph.D. student, with relativistic and quantum physics in his weaponry – and I was also inspired by Benoit Mandelbrot’s fractals - Mandelbrot was a Ph.D. student of John von Neumann.
Is the fact that the lineage of concepts of genome informatics has a clear parallel to war-time efforts of Relativity (published in final form in 1916) and quantum theory that made both peaceful (Leo Szilard) and strategic applications of nuclear industry possible (Heisenberg on one side and Turing, Teller, von Neumann, Wiener on the other)?
War-time efforts drained the best minds to lay down the exact (“bullet-proof”) intellectual infrastructure, as if developing an armor capable of deflating all flak, friendly or not. Mathematicians, physicists and nuclear engineers, having served their respective countries, moved in droves from physics to biology.
In 1971, Nixon declared his “War on Cancer”. Now, those whose experise leaves no question that the disease of the genome (with Genomics=Informatics) can only be won with mathematicians, information theorists and most of all, by computers deployed the bolster the infantry of traditional medicine will make sure that the “New War on Cancer”, forty years after the first, will have the needed weaponry developed and ready to use.
The genetic code, 8-dimensional hypercomplex numbers and dyadic shifts
[Excerpts]
Sergey V. Petoukhov
Head of Laboratory of Biomechanical System, Mechanical Engineering Research
Institute of the Russian Academy of Sciences, Moscow
spetoukhov@gmail.com, petoukhov@imash.ru,
http://symmetry.hu/isabm/petoukhov.html, http://petoukhov.com/
Abstract: Matrix forms of the representation of the multi-level system of molecular-genetic alphabets have revealed algebraic properties of this system. Families of genetic (4*4)- and (8*8)- matrices show an unexpected connections of the genetic system with functions by Rademacher and Walsh and with Hadamard matrices which are well-known in theory of noise-immunity coding and digital communication. Dyadic-shift decompositions of such genetic matrices lead to sets of sparse matrices. Each of these sets is closed in relation to multiplication and defines relevant algebra of hypercomplex numbers. It is shown that genetic Hadamard matrices are identical to matrix representations of Hamilton quaternions and its complexification in the case of unit coordinates. The diversity of known dialects of the genetic code is analyzed from the viewpoint of the genetic algebras. An algebraic analogy with Punnett squares for inherited traits is shown. Our results are discussed taking into account the important role of dyadic shifts, Hadamard matrices, fourth roots of unity, Hamilton quaternions and other hypercomplex numbers in mathematics, informatics, physics, etc. These results testify that living matter possesses a profound algebraic essence. They show new promising ways to develop algebraic biology.
1. Introduction
Science has led to a new understanding of life itself: «Life is a partnership between genes and mathematics» [Stewart, 1999]. But what kind of mathematics is a partner with the genetic code? Trying to find such mathematics, we have turned to study the multi-level system of interrelated molecular-genetic alphabets. On this way we were surprised to find connections of this genetic system with well-known formalisms of the engineering theory of noise-immunity coding: Kronecker products of matrices; orthogonal systems of functions by Rademacher and Walsh; Hadamard matrices; a group of dyadic shifts; hypercomplex number systems, etc. This article is devoted to some of our results of such studing of the phenomenologic system of interrelated genetic alphabets….
Genetic information is transferred by means of discrete elements. General theory of signal processing utilizes the encoding of discrete signals by means of special mathematical matrices and spectral representations of signals to increase reliability and efficiency of information ... A typical example of such matrices is the family of Hadamard matrices. Rows of Hadamard matrices form an orthogonal system of Walsh functions which is used for the spectral representation and transfer of discrete signals… An investigation of structural analogies between digital informatics and genetic informatics is one of the important tasks of modern science in connection with the development of DNA-computers and bioinformatics. The author investigates molecular structures of the system of genetic alphabets by means of matrix methods of discrete signal processing [Petoukhov, 2001, 2005a,b, 2008a-c; Petoukhov, He, 2010, etc.].
The article describes author’s results about relations of matrix forms of representation of the system of genetic alphabets with special systems of 8-dimensional hypercomplex numbers (they differ from the Cayley’s octonions). The discovery of these relationships is significant from some viewpoints. For example, it is interesting because systems of 8-dimensional hypercomplex numbers (first of all, Cayley’s octonions and split-octonions) are one of key objects of mathematical natural sciences today. They relate to a number of exceptional structures in mathematics, among them the exceptional Lie groups; they have applications in many fields such as string theory, special relativity, the supersymmetric quantum mechanics, quantum logic, etc. … The term “octet” is also used frequently in phenomenologic laws of science: the Eightfold way by M.Gell-Mann and Y.Ne’eman (1964) in physics; the octet rule in chemistry… In view of these facts one can think that genetic systems of 8-dimensional numbers will become one of the interesting parts of mathematical natural sciences.
In addition, hypercomplex numbers are widely used in digital signal processing… Formalisms of multi-dimensional vector spaces are one of basic formalisms in digital communication technologies, systems of artificial intelligence, pattern recognition, training of robots, detection of errors in the transmission of information, etc.
Revealed genetic types of hypercomplex numbers can be useful to answer many questions of bioinformatics and to develop new kinds of genetic algorithms. Hadamard matrices and orthogonal systems of Walsh functions are among the most used tools for error-correcting coding information, and for many other applications in digital signal processing …. As noted in the article [Seberry, et al., 2005], many tens of thousands of works are devoted to diverse applications of Hadamard matrices for signal processing. Our discovery of relations of the system of genetic alphabets with the special systems of 4-dimensional and 8-dimensional hypercomplex numbers and with special Hadamard matrices helps to establish the kind of mathematics which is a partner of the molecular-genetic system. Hypercomplex numbers, first of all, Hamilton quaternions and their complexification (biquaternions) are widely applied in theoretical physics. The author shows that matrix genetics reveals a special connection of the system of genetic alphabets with Hamilton quaternions and their complexification. These results give new promising ways to build a bridge between theoretical physics and mathematical biology. They can be considered as a new step to knowledge of a mathematical unity of the nature….
As Hamilton quaternions describe the properties of three-dimensional physical space, the discovery of the connection of the genetic system with Hamilton quaternions attracts our attention to some fundamental questions. It is, for example, the question about innate spatial representations in humans and animals [Russell, 1956; Petoukhov, 1981]. Or the question about a development of physical theories, in which the concept of space is not primary, but derived from more fundamental concepts of special mathematical systems [Kulakov, 2004; Vladimirov, 1998]. The described effectiveness of the algorithm of hidden parameters allows thinking about the systems of hidden parameters as about a possible base for additional development of these theories.
Molecular biology and bioinformatics possess their own problems where Hamilton quaternions and their complexification can be used. For example some approaches are known about algorithmic constructions of fractal patterns in biological structures including fractals in genetic molecules (see [Pellionisz et al, 2011; web]). A development of geometric algorithms for such approaches needs those geometrical operations inside physical 3D-space which are connected with the molecular-genetic system and which can be used as a basis of these geometric algorithms. Hamilton quaternions and their complexifications, which are connected with the system of genetic alphabets and which correspond to geometric properties of our physical space, seem to be promising candidates for this purpose.
Our article shows that now Hamilton quaternions and their complexifications are connected not only with theoretical physics but also with molecular genetics and bioinformatics. The discovery of the relation between the system of molecular-genetic alphabets and Hamilton quaternions together with their complexification provides a bridge between theoretical physics and biology for their mutual enrichment. It can be considered as a next step to discover the mathematical unity of nature.
[Physicists pick up from where Schroedinger left Genome Informatics in 1943 in his essay "What is Life?" - AJP]
Biophysicists Discover Four New [Fractal - AJP] Rules of DNA 'Grammar'
MIT Technology, December 10, 2010
... Today, Michel Yamagishi at the Applied Bioinformatics Laboratory in Brazil and Roberto Herai at Unicamp in Sao Paulo, say they've discovered several new patterns that significantly broaden the grammar of DNA.
Their approach is straightforward. These guys use set theory to show that Chargaff's existing rules imply the existence of other, higher order patterns.
Here's how. One way to think about the patterns in DNA is to divide up a DNA sequence into words of specific length, k. Chargaff's rules apply to words where k=1, in other words, to single nucleotides.
But what of words with k=2 (eg AA, AC, AG, AT and so on) or k=3 (AAA, AAG, AAC, AAT and so on)? Biochemists call these words oligonucleotides. Set theory implies that the entire sets of these k-words must also obey certain fractal-like patterns.
Yamagishi and Herai distil them into four equations.
Of course, it's only possible to see these patterns in huge DNA datasets. Sure enough, Yamagishi and Herai have number-crunched the DNA sequences of 32 species looking for these new fractal patterns. And they've found them.
They say the patterns show up with great precision in 30 of these species, including humans, e coli and the plant arabidopsis. Only human immunodeficiency virus (HIV) and Xylella fastidiosa 9a5c, a bug that attacks peaches, do not conform.
"These new rules show for the first time that oligonucleotide frequencies do have invariant properties across a large set of genomes," they say.
That could turn out to be extremely useful for assessing the performance of new technologies for sequencing entire genomes at high speed.
One problem with these techniques is knowing how accurately they work. Yamagishi and Herai suggest that a simple test would be to check whether the newly sequenced genomes contain these invariant patterns. If not, then that's a sign the technology may be introducing some kind of bias.
This is a bit like a checksum test for spotting accidental errors in blocks of data and a neat piece of science to boot.
Ref: arxiv.org/abs/1112.1528: Chargaff's "Grammar of Biology": New Fractal-like Rules
Chargaff's "Grammar of Biology": New Fractal-like Rules
Michel Eduardo Beleza Yamagishi, Roberto H. Herai
(Submitted on 7 Dec 2011)
Chargaff once said that "I saw before me in dark contours the beginning of a grammar of Biology". In linguistics, "grammar" is the set of natural language rules, but we do not know for sure what Chargaff meant by "grammar" of Biology. Nevertheless, assuming the metaphor, Chargaff himself started a "grammar of Biology" discovering the so called Chargaff's rules. In this work, we further develop his grammar. Using new concepts, we were able to discovery new genomic rules that seem to be invariant across a large set of organisms, and show a fractal-like property, since no matter the scale, the same pattern is observed (self-similarity). We hope that these new invariant genomic rules may be used in different contexts since short read data bias detection to genome assembly quality assessment.
http://arxiv.org/ftp/arxiv/papers/1112/1112.1528.pdf
ABSTRACT
Chargaff once said that “I saw before me in dark contours the beginning of a grammar of
Biology”. In linguistics, “grammar” is the set of natural language rules, but we do not know for
sure what Chargaff meant by "grammar” of Biology. Nevertheless, assuming the metaphor,
Chargaff himself started a “grammar of Biology” discovering the so called Chargaff’s rules. In
this work, we further develop his grammar. Using new concepts, we were able to discovery new
genomic rules that seem to be invariant across a large set of organisms, and show a fractal-like
property, since no matter the scale, the same pattern is observed (self-similarity). We hope that
these new invariant genomic rules may be used in different contexts since short read data bias
detection to genome assembly quality assessment.
[We are soon approaching the phase when everyone will say "Of course, the Genome is Fractal! Why should it be an exception to everything else in Nature?" The invited Springer Handbook Chapter "Recursive Genome Function of the Cerebellum: Unification of Neuroscience and Genomics" (In Press) reviews that looking at the genome as a kind of a "language" (with grammar) goes back to a couple of decades - but could not break through in part because full DNA sequences were not available, and also since the double barrier of the "Central Dogma" and "Junk DNA" misnomer blocked progress for over half a Century. With The Principle of Recursive Genome Function recursive fractal iteration was established as the fundamental algorithm of genome function, and in the "Unification" publication not only the Genomic-Epigenomic (HoloGenomic) system was put in a coherent mathematical framework, but also the "RNA System Serves as the Genomic Cerebellum" concept was put forward. The genome is not only fractal, but the "Coordinated Genome Function" uses the dual (covariant and contravariant) representations of protein production. This entry can be discussed in the FaceBook page of Andras Pellionisz]
Francis Collins [Head of NIH of the USA in Bangalore, India -AJP].
This US scientist is the director of the National Institute of Health, the country's leading health research establishment. He's in Bangalore to meet top Indian scientists on Saturday and Sunday and shares his outlook on life in this exclusive interview.
What strengths do you see in Indian institutions? How are they compared to China?
India's great strength now is its IT and computational capacity. Biology is now more a computational science. To understand diseases like diabetes and cancer, we need computational strategies to sift through vast datasets. India can provide that asset.
China has strengths too - a long history of genomics research and was into the human genome project earlier than India. India is catching up. But we need the collaboration of all countries on health because everyone everywhere in the world has health problems. Like Louis Pasteur said, science belongs to no one country.
Tell us more about your Bangalore visit...
It's been very exciting. I've been to the National Centre for Biological Sciences, St John's Medical Research College and IISc. At St Johns, I visited the hospital and clinical facilities to get a grasp of research there.
At NCBS, I spoke to students and faculty. The experience was wonderful. The students are bright and inquisitive and we discussed issues ranging from neuroscience to genomics. Clearly, Bangalore is a city that has a thoughtful generation of youngsters out to prove themselves in science. I see a spark in the young people here.
What collaborations are you looking at with institutions here?
We have a collaboration on cancer with India. India is concentrating on mouth cancer as that occurs at a higher frequency here. Then, there's diabetes - a worldwide concern. At St John's, we talked about diabetes and cardiovascular research and the implications for heart disease arising out of diabetes. We're also looking at vaccines, HIV, vision and brain research and technologies we need to develop to tackle these and other chronic diseases.
Paired Ends: Lee Hood, Andras Pellionisz
Genomeweb, In Sequence
December 06, 2011
a "leader in the field of genome informatics." Pellionisz holds PhD degrees in computer engineering...
People In The News
GenomeWeb Daily News - Dec 2, 2011
Elogic Technologies has named Andras Pellionisz to serve on its advisory board. An expert in genome informatics who established the International ...
People In The News
GenomeWeb Daily News - 1 day ago
PrimeraDx this week announced that it has appointed Leroy Hood to its scientific advisory board. Hood has helped found several research and commercial ...
[Genomeweb elected to disclose full contents only to subscribers or registered readers. This entry can be discussed on the FaceBook page of Andras Pellionisz]
ELOGIC Technologies (Bangalore) to launch Genome Analytics Service (See Binet - Genome)
ELOGIC Technologies is proud to present, for the very first time in India, IT Infrastructure Services in Next Generation Sequence (NGS) Analysis and Management on the Cloud Environment, through its BINET (Biological Internet) Division.
ET partners with leading IT MNC’s as a preferred alliance partner in India, to cater to the needs of Genome Informatics for a gradually burgeoning global clientele. ET’s partnership with these MNC’s goes back a decade for providing a range of services and products.
ET is foraying into Life Sciences sector as a whole and will pursue its interests at present in Genome Informatics as a focus area.
Being a forerunner in the arena of Genome Informatics and Data Analysis for NGS on the Cloud, ET is building a team comprising Board of Advisors, experienced Genomics experts, Biology Scientists, Mathematicians and IT Professionals, to provide an array of services in this field as a one-stop-shop for its customers. ET is aiming at providing services in the entire Life-Sciences sector per se, which implies developing software products and algorithms for NGS Analysis.
TD Prakash, Managing Director, ET says – “ET understands the Genome Informatics Landscape in all its entirety and interdisciplinary nature of Biological Sciences, which has occupied the foreground now, with the emergence of new areas in Bio research, such as Genomics, Proteomics and System Biology.
We envision value in building Life Science services with cutting edge technologies and will forge collaboration with global players in NGS area, with the objective of serving the global Life Science industry and related Academia.
In order to ensure that we provide services with complete knowledge and understanding of the field, extra proficiency and authority, ET has engaged with Dr. Andras Pellionisz, a global leader in Genome Informatics from USA, to be a part of our Board of Advisors and we welcome him with much delight on board our advisory team”.
Dr. Pellionisz is the President of HolGenTech, Inc. in Silicon Valley, California, USA and is a thought leader in Fractal Genomics and Hologenomics and will be undertaking the role of the guiding force for ET in this field. He has convinced himself of ET’s domain knowledge, capabilities, business model and growth plans prior to being a part of our Board of Advisors. For more information on our collaboration, please read here a Press Release held in California USA on the 30th November, 2011.
Equipped with the very best of high-end IT know-how and technical expertise, Bangalore is emerging as the epicentre of Biotech - a hub for Genomics and Genome Informatics and is therefore in a unique position to deliver to a world-wide audience, quality services and products in the field of Genome Informatics.
ET is poised to deliver its services to the market by the first quarter of 2012 and will, as a first step, start with executing pilot projects with the Academia. ET will offer services in IT infrastructure to begin with, in Compute Power and Storage followed by NGS Data Analysis Services.
Headquartered in Bangalore, ET has its office in Mumbai and operations in Dubai United Arab Emirates and UK too. Its immediate expansion plans include establishing offices in Chennai and Delhi in India, followed by an office in the Silicon Valley of USA.
With its knowledge of Genomics and its strategic location, ET has at its disposal the means and wherewithal to achieving Data Analysis of Next Generation Sequencing on Cloud.
DNA Sequencing Caught in Deluge of Data
New York Times, By ANDREW POLLACK
Published: November 30, 2011
[Added by Pellionisz: In 2008 I warned against this problem in my YouTube. Today, 3 years and 12,744 views later, the NYT describes precisely what was predicted and warned against. The lessons are not analyzed by general media - see my proof of the presently unsustainable path of Genome Sequencing Industry - without the rapid matching of Genome Analytics Industry (stock charts appended). This will emerge (in the USA or elsewhere) once Genome Analytics will be able to tap the vast market of Global Consumers and Global Health Care - this entry can be discussed in the FaceBook of Andras Pellionisz]
BGI, based in China, is the world’s largest genomics research institute, with 167 DNA sequencers producing the equivalent of 2,000 human genomes a day.
BGI churns out so much data that it often cannot transmit its results to clients or collaborators over the Internet or other communications lines because that would take weeks. Instead, it sends computer disks containing the data, via FedEx....
The field of genomics is caught in a data deluge. DNA sequencing is becoming faster and cheaper at a pace far outstripping Moore’s law, which describes the rate at which computing gets faster and cheaper.
The result is that the ability to determine DNA sequences is starting to outrun the ability of researchers to store, transmit and especially to analyze the data.
“Data handling is now the bottleneck,” said David Haussler, director of the center for biomolecular science and engineering at the University of California, Santa Cruz. “It costs more to analyze a genome than to sequence a genome.”
...
That could delay the day when DNA sequencing is routinely used in medicine. In only a year or two, the cost of determining a person’s complete DNA blueprint is expected to fall below $1,000. But that long-awaited threshold excludes the cost of making sense of that data, which is becoming a bigger part of the total cost as sequencing costs themselves decline.
...
But the data challenges are also creating opportunities. There is demand for people trained in bioinformatics, the convergence of biology and computing. Numerous bioinformatics companies, like SoftGenetics, DNAStar, DNAnexus and NextBio, have sprung up to offer software and services to help analyze the data. EMC, a maker of data storage equipment, has found life sciences a fertile market for products that handle large amounts of information. BGI is starting a journal, GigaScience, to publish data-heavy life science papers.
“We believe the field of bioinformatics for genetic analysis will be one of the biggest areas of disruptive innovation in life science tools over the next few years,” Isaac Ro, an analyst at Goldman Sachs, wrote in a recent report.
...
There will probably be 30,000 human genomes sequenced by the end of this year, up from a handful a few years ago, according to the journal Nature. And that number will rise to millions in a few years.
In a few cases, human genomes are being sequenced to help diagnose mysterious rare diseases and treat patients. But most are being sequenced as part of studies. The federally financed Cancer Genome Atlas, for instance, is sequencing the genomes of thousands of tumors and of healthy tissue from the same people, looking for genetic causes of cancer.
One near victim of the data explosion has been a federal online archive of raw sequencing data. The amount stored has more than tripled just since the beginning of the year, reaching 300 trillion DNA bases and taking up nearly 700 trillion bytes of computer memory.
Straining under the load and facing budget constraints, federal officials talked earlier this year about shutting the archive, to the dismay of researchers. It will remain open, but certain big sequencing projects will now have to pay to store their data there.....
“In the life sciences, anyone can produce so much data, and it’s happening in thousands of different labs throughout the world,” he said.
Moreover, DNA is just part of the story. To truly understand biology, researchers are gathering data on the RNA, proteins and chemicals in cells. That data can be even more voluminous than data on genes. And those different types of data have to be integrated. [See an entirely new approach to the "RNA System Acting as a Genomic Cerebellum" In Press - AJP]
...
Researchers are increasingly turning to cloud computing so they do not have to buy so many of their own computers and disk drives.
Google might help as well.
“Google has enough capacity to do all of genomics in a day,” said Dr. Schatz of Cold Spring Harbor, who is trying to apply Google’s techniques to genomics data. Prodded by Senator Charles E. Schumer, Democrat of New York, Google is exploring cooperation with Cold Spring Harbor.
Google’s venture capital arm recently invested in DNAnexus, a bioinformatics company. DNAnexus and Google plan to host their own copy of the federal sequence archive that had once looked as if it might be closed.
...
[Stock charts of the dramatic drop of valuation over the last six monts of the four leading DNA Sequencing companies. (Illumina and Life dropped less, since they are NOT "pure play sequencers"). This evidence of the presently unsustainable path of Genome Sequencing - without the rapid ramp-up in the USA or by SAMSUNG (announced Sept. 1. 2011) and/or other global players - has long been directly communicated, and non-standard solutions are emerging - these additions to the NYT description of the mesmerizing symptoms can be discussed on FaceBook of Andras Pellionisz]
The FDA’s confusing stance on companies that interpret genetic tests is terrible for consumers.
Tell Me What’s in My Genome!
SLATE - By Christopher Mims|Posted Friday, Nov. 25, 2011, at 12:20 AM ET
Right now, for about the same price as a conventional medical test that reveals just a handful of genes, you could learn the entire contents of your genome. Sure, it’s a "research" scan, which means it will contain mistakes, and your insurance won’t cover the $4,000-$5,000 bill. But it won't be more than a few years before a complete and virtually error-free version of your genome will be within financial reach. Wouldn’t you like to unlock your complete instruction set, with all the medical and ancestry data it contains?
Enticing as that may be, it won’t be easy get those keys if the FDA has its way. Last summer, the agency indicated that it wants to classify the work of any company that helps you decipher your genome as a medical test that must be regulated accordingly. But over the last year, the agency’s lack of continued communication has left companies that would interpret genetic information—which are simply offering information—confused as to where they stand. This lack of clarity and direction could ultimately mean ceding leadership in this field to overseas competitors who are not similarly constrained.
The FDA has indicated through its public statements that it will put regulatory barriers in the path of companies that want to help us interpret genomes. In June 2010 the agency sent a series of letters to providers like 23andMe, warning them that they were selling what amounted to medical tests that were not vetted by the FDA, and so were in violation of the law. The FDA’s letter to consumer genetics testing company 23andMe is a good example of the tack the agency is now taking. “23andMe has never submitted information on the analytical or clinical validity of its tests to FDA for clearance or approval. ... Consumers may make medical decisions in reliance on this [genetic] information [provided by 23andMe].”
Since then, the FDA has continued to send out letters of a similar tone—23 in total, all to different companies—but has offered no other guidance to providers of direct-to-consumer genetic tests, leaving these companies, and their investors, in the dark about the ultimate direction of regulation in this area. Frustrated by the delay, in recent months many of these companies have made their responses to the FDA public on their websites, in part to protest the climate of ongoing regulatory uncertainty that the agency’s actions have created. Others have pre-emptively eliminated medically significant interpretations from their tests, even if the genes they return still contain that information.
Rather than protect consumers, the FDA’s move has left the genetic information industry in limbo—and it seems a matter of time before it moves overseas. Can’t get your full genome scan interpreted by software hosted on servers in the United States, owned by a U.S. company? Within a decade, a company in a country not subject to our laws will almost certainly be happy to accommodate you. That’s if you don’t take the do-it-yourself route first, plumbing your genome with free and open-source software linked to Wikipedia-style databases maintained by volunteers (which, because they aren’t sold, aren’t subject to FDA regulation).
It’s difficult, if not impossible, to find legal or medical scholars in the United States who are against patient access to full genome sequences. So where does the FDA’s reticence come from? In part, it’s the long shadow of “genetic exceptionalism”—the idea that “genetic information is inherently unique and should be treated differently in law than other forms of personal or medical information,” as Alan Dow, vice president and legal counsel at Complete Genomics, put it.
Other Western governments, too, have fallen into the genetic exceptionalism trap. In 1997, the European Union’s member states even signed a treaty, the Convention on Human Rights and Biomedicine, which mandates that all signatories apply the precautionary principle when handling biomedical advances like genetic sequencing. This means it’s incumbent upon the advocates of these technologies to prove they won’t do any harm. So, for example, Germany has instituted a law so broad that it basically prevents anyone from getting her own genes sequenced without a doctor’s permission. If the genome-interpreting industry is forced by regulatory limbo to seek shelter outside the United States, we may see developing countries like India compete to fill the market gap.
Based on how we've (mis)used other medical technologies, it’s understandable that governmental bodies are at least a little concerned about the advent of whole-genome sequencing. For example, full-body MRI scans have fed into hypochondria-type fears by flagging benign abnormalities that then have to be further examined. Wouldn't a full-genome scan, with the many disease-contributing genes it turns up, do the same? And won't patients who discover, for example, an elevated chance of an incurable disease have their quality of life adversely affected? We'll get to the details later, but the short answer is no.
Genetic data have to be interpreted in a way that the public might not be accustomed to. But it is elitism of the highest order to imagine that most of us are simpletons who can’t grasp the concept that a gene might contribute to a disease condition, but in no way guarantees it. The fear is that every new study associating a gene with a particular disorder will send patients running to their doctors to ask whether they should be worried. But that seems to be a short-term concern: Most patients will understand the reality after their first (or maybe second) panicked trip to the doctor. The physician will tell them that these studies are always preliminary and that even if they’re borne out by subsequent research, the vast majority of these genes have only a marginal effect on our health.
Studies suggest that even patients who find out they have an elevated risk for a disease with a strong genetic component but no cure—like Alzheimer’s—handle the news quite well. In light of this, it seems like the worst-case scenario for a full genome scan is that a patient might be inspired to actually talk to their doctor about their health. If having genes that suggest an elevated chance of heart disease inspire someone to at least be conscientious about their other risk factors for the disease, great! Preliminary research suggests that results of genetic tests change consumers’ intention to do something about their health, if not their actual behavior. (Consumers’ options about what to do with this information often come down to common lifestyle changes like diet and exercise, which are difficult to get patients to implement under any circumstances.)
The only thing worse than the paternalism keeping genetic data and its implications from consumers is the failure of imagination this represents, in terms of the potential upside of the coming genomic revolution. The more full-genome scans we have, and the cheaper they become, the more useful information patients will have. Widespread genotyping will help us understand our own ancestry, but perhaps more importantly will lead to a new kind of engagement with our health and biology. For this new technology to transform American health—and to cultivate a new, high-tech, high-promise industry within the United States—the FDA needs to provide clarity and guidance. The alternative is that the FDA becomes something like the recording industry at the dawn of the MP3 age: a body trying to lock down immaterial assets that consumers are going to get their hands on, one way or another.
[No Comment - Andras Pellionisz]
ELOGIC Technologies Private Limited Bangalore, India [Pellionisz unites Silicon Valley-s of USA/India - AJP]
PRWeb, November 30, 2011
Bangalore, India - Genome informatics leader Dr. Andras Pellionisz joins the BoA of the global IT Company to break into Industrialization of Genomics
ELOGIC Technologies Private Limited is a Company Certified for ISO 9001:2008 by BVQI and ISO 27001:2005 for Security by BSI for years now and is in the process of obtaining an ISO: 50001 Certification for Energy Management System. In the business of Secure Information Management and IT Infrastructure Services Delivery, the Company aims at maintaining an uncompromising level of integrity and character to serve its customers, partners, employees and community, building a network of trust along the way. The company is now foraying into Genome Informatics and Next Gen Seq Data Analysis on the public and private clouds.
ELOGIC Technologies announces that Dr. Andras Pellionisz has accepted invitation to join the Advisory Board of ELOGIC Technologies. Dr. Pellionisz is an internationally renowned leader in the field of genome informatics specializing in the geometric unification of neuroscience and genomics. Founder and President of Silicon Valley-based HolGenTech, Inc. in California, he exemplifies the model Andy Grove, senior adviser to Intel, by putting innovation within goal-oriented corporation structure. In his major paper in Springer Handbook (in Press, accepted Nov. 1st 2011) he pairs breakthrough algorithmic development with a blueprint for industrial deployment at a crucial time when the Human Genome Project already impacted the economy by $796 Bn.
“We are delighted to welcome Dr. Andras Pellionisz into our Advisory Board. He brings a wealth of leading edge understanding and global contacts in genome informatics that will be invaluable to ELOGIC Technologies as we edge into this major emerging market,” says TD Prakash, Managing Director and Chairman of the Board of ELOGIC Technologies. "This is the formative decision in the long-term co-operation between USA-Silicon valley HolGenTech, Inc. and ELOGIC Technologies from Bangalore, Silicon valley of India, in the emerging field of genome analytics”, both Dr Pellionisz and Mr. TD Prakash concurred.
“Dr. Pellionisz will be our guiding force for foraying into Data Analysis of Next Gen Sequencing and Management, which we are offering to global clientele by early 2012”, added MV Ramanujam, VP of BINET Division, ELOGIC Technologies.
As a domain expert in Genome Informatics, Dr. Pellionisz is a cross-disciplinary scientist and technologist. With Ph.D.-s in Computer Engineering, Biology and Physics, he has 45 years of experience in Informatics of Neural and Genomic Systems spanning Academia, Government and Silicon Valley Industry. He played a leading role in the shift from artificial intelligence to neural nets, including the establishment of the International Neural Network Society. In 2005, he combined interdisciplinary communities of Genomics and Information Technology when he established the International HoloGenomics Society (IHGS).
Based on sound genome informatics, his work sets forth new mathematical principles for proceeding with full exploration of the whole genome. Dr. Pellionisz’ fractal approach to genome analysis is corroborated by recently published findings about fractal folding of DNA structure by Presidential Science Adviser Eric Lander.
“I am very pleased to join the ELOGIC Technologies Advisory Board. I am convinced that they have the foundation essential for edging into genome informatics. As one who served the “Internet Boom” as Chief Software architect of several Silicon Valley companies, I see and publicly expressed already in 2008 two major differences in the coming much larger boom of Industrialization of Genomics. First, while Internet companies could charge ahead without scientific innovation as packet-switching technology is both man-made and is utterly simple, industrialization of genomics (like nuclear industry) would not only be naive but utterly dangerous without science leadership. Second, while the Internet Boom was essentially centered on Silicon Valley of California, the Genome Boom is already global. I not only realize the cardinal importance of an alliance of Silicon Valley of California with Bangalore, the Silicon Valley of India, but will enjoy building a spectacular success based on global alliance,” says Dr. Pellionisz.
In 1973 Dr. Pellionisz was awarded a Stanford Post Doctoral Fellowship, subsequently he served as Research Professor of Biophysics at New York University Medical Center. Later at NASA Ames Research Center, as a Senior Research Associate of the National Academy. From 1994, he served as Chief Software Architect to several Silicon Valley companies.
About ELOGIC Technologies
ELOGIC Technologies is an IT Enabled Services Company serving a clientele of major multi- national and Government organisations seeking collaboration in the areas of Genome Informatics & Life Sciences, E-Security Services, Productivity Enhancement Solutions, Banking Solutions, Engineering Services and Professional Services Consultancy.
Established in the year 2002, they aim at providing high quality, products and services to achieve customer satisfaction and to be an innovating Company in leading edge technologies.
Contact
MV RAMANUJAM
ELOGIC Technologies Pvt Ltd
BANGALORE, INDIA
Phone : 0091 80 41210 892
Email : mvramanujam(at)elogic(dot)co.in
URL : http://www.elogic.co.in
[Dr. Pellionisz, as Founder and President of his HolGenTech, Inc. in Silicon Valley, California, with his new Board of Adviser role to Elogic Technologies of India's Silicon Valley in Bangalore, builds a global alliance. Dr. Pellionisz also serves as Board of Adviser to DRCcomputer - a hybrid serial/parallel processing hardware integrator of California's Silicon Valley. While the terms of an emerging global alliance are not disclosed, in view of the Boston team just having provided "proof of concept" (see full preview and Nature article two items below in this column, and popular coverage here) to the long-held thesis of Dr. Pellionisz that "fractal defects are root causes of a slew of hereditary syndromes, most notably of cancer" (see at min. 30 of his Google Tech Talk YouTube, 2008), Dr. Pellionisz' major peer-reviewed paper In Press provides hints of a global agenda) - This entry can be discussed on the FaceBook page of Andras Pellionisz]
Genomeweb
November 2011
By Christie Rizk
The creation of new drugs is a vital part of the health-care system. Researchers in academia and in industry are always searching for new ways to combat disease — more efficiently, with fewer toxicities, and less chance of rejection. Until recently, most medicines have been limited to the classic formulation of a small molecule targeting a protein to disrupt its function. But there are many targets and formulations that have yet to be fully exploited, particularly those involving RNAs.
Small interfering RNAs and microRNAs can be used both as targets for drugs and as compounds in drug formulations to disrupt the function of certain genes. By taking advantage of RNA interference, miRNAs and siRNAs can bind to specific messenger RNAs and either increase or decrease their expression to affect how much or how little a given target gene functions. "There are really quite a lot of different methods and cellular pathways that are exploited. In some of them the field is quite old, so you might consider antisense technology a form of RNA therapeutics," says the University of Massachusetts Medical School's Phillip Zamore, who co-directs the school's RNA Therapeutics Institute. "There are people who are engineering different kinds of cellular RNAs to alter splicing or to degrade messages. Most of those involve re-engineering longer RNA, and probably can be delivered as drugs."
But the real promise shown by RNA in the therapeutics field comes from small RNAs, he adds. "The new small RNA therapeutics are really the first time that RNA has shown some promise as a drug," Zamore says. "The secret is that they're small, they're generally double-stranded, and they need relatively little — although they need some — chemical modification to make them stable. The real advance has been the discovery of chemical formulations that allow them to be retained in the body instead of filtered out by the kidneys and delivered to cells. And those drugs, which generally take the form of siRNA, are now being tested in early-stage clinical trials." [SiRNA, a tiny start-up to deploy small interfering RNA-s was acquired by Merck 5 years ago for $1.1 Billion - just to be shut down this summer. Why? See comment... AJP]
Most miRNA-based drugs use RNAi pathways to bind argonaute proteins — the protein complexes responsible for RNAi — and, together, the combination of siRNA or miRNA and argonaute protein bind the target complementary mRNA and destroy it. "My lab has for the last 12 years studied the basic mechanisms of siRNAs and microRNAs," Zamore says. "We work in a variety of organisms including flies and mice, and out of our basic research efforts, we've been able to understand new ways in which one can use siRNAs to target genes that differ by as little as a single nucleotide. And in that case it would be a question of targeting a mutant versus a normal gene."
Treating disease
Zamore's primary focus is Huntington's disease, which has been shown to be caused by a mutated gene with an extended CAG repeat. "There has not been much success by any lab targeting the extended repeat," Zamore says. "But as we showed a couple of years ago, there's a polymorphism — a neutral base change — that is commonly associated with the disease gene. And because it creates a single nucleotide difference between the disease gene and the wild-type gene, one can target that and not the normal gene." Zamore's work on Huntington's disease is still in the preclinical stages, but he's hopeful that it will eventually make it to clinical trials.
The list of diseases that are potentially treatable with RNA drugs is long. "There are certainly clinical trials using siRNAs for cancer, and the kinds of targets that people are interested in are molecules, proteins, that normally would be considered non-druggable," Zamore says. "The pharmaceutical industry has a very short list of the types of proteins that they suspect will be amenable to inhibition by classical small-molecule drugs. Any gene that doesn't fall on that list — whose expression or over-expression contributes to a disease — would be a good candidate for RNA interference. So basically, if you can reduce the expression of the protein and do some good, it's a good target for RNAi."
Merck is one of the companies moving into RNA therapeutics. The company acquired San Francisco-based biotechnology company Sirna Therapeutics — which specialized in the development of siRNA drugs — in a 2006 deal worth $1.1 billion, because it believed RNA could "open up a whole new class of medications to treat patients with unmet medical needs," says Jeremy Caldwell, head of Merck's RNA therapeutics division. Caldwell says Merck is working to apply RNA drugs to the treatment of cancer as well as cardiovascular and respiratory diseases. He adds that he's "cautiously optimistic" that the company will be starting clinical trials on some therapeutics in the near future. "RNA is really going to be the modality that takes advantage of all the high-throughput sequencing and genomic information that identify potential drug targets, many of which are not druggable with classic approaches like small molecules," Caldwell says. "Classic small molecule targets are receptors and enzymes because they have a hydrophobic pocket that the small molecule can insert itself into and block the activity of the target. Biologics are similar, but only work on cell surface. What siRNA will be able to do is address both small molecule targets and biologic targets, but also targets such as adaptor proteins that regulate a catalytic intracellular event, for example." He says there are practically no diseases that cannot be targeted by RNA therapeutics.
There are some problems that must be resolved, however. For one thing, most RNA therapeutics are currently limited by their routes of administration — such as intravenous or subcutaneous approaches, Caldwell says. And since there are already plenty of drugs that can be administered orally, it's unlikely they would be replaced by comparable RNA therapeutics unless those, too, could be administered by mouth or were a significant improvement upon the standard treatments.
In addition, researchers are struggling with how to deliver these drugs to their intended targets once they're administered. Many companies developing RNA drugs, including Merck, are taking a route through the liver, which is generally quite efficient, but limits the number of diseases that can be treated with these drugs.
Special delivery
Silence Therapeutics, which specializes in the delivery of targeted RNAi therapeutics, aims to solve this problem with its new delivery system called AtuPlex — a lipid delivery technology that targets the vascular endothelium of different organs. "For the whole field, the biggest hurdle is the delivery. There are different ways to use RNAis or even antisense molecules, but I think in the last three or four years, most companies shifted to formulations — they realized you need delivery technologies for the nucleic acids," says Jörg Kaufmann, Silence's vice president of research. "Our delivery technology, AtuPlex, is a liposomal formulation complexed with nucleic formulations, and the difference is that our formulation targets the vascular endothelium of different organs, including the tumor vasculature." While some companies target the tumor cells themselves, Silence's method allows the therapeutic to directly enter the tumor, and to alter the vasculature of target organs in order to prevent secondary metastasis, Kaufmann adds.
The company's most advanced RNAi therapeutic, a compound called Atu027, has been shown to prevent metastasis to the lungs by modulating the organs' blood vessels, Kaufmann says. "Basically, if the cancer cells are in a solid tumor, at one point they will go into the bloodstream to start growing in different organs. Our system delivers the nucleic acids to the endothelium or the vasculature," he says. Then, the system works to change it enough to prevent cancer cells from taking hold and metastasizing. This approach differs from that of Silence's competitors — companies like Alnylam, which UMass's Zamore co-founded, or Tekmira — which generally target the liver, liver metastases or cancer in the liver, whereas Silence is attempting to target metastases throughout the body, Kaufmann says.
Currently, the company is at the end of Phase I testing on Atu027 and will start Phase II trials by the end of this year or in early 2012. Phase III trials are likely six to 10 years away, Kaufmann says, but he is optimistic about the drug's chances of making it to market. So far, the company is still escalating the dose, and is aiming to show Atu027 can be used as a monotherapy in Phase II testing. "After that, we will combine it, and I personally envision it that it will be used in combination with chemotherapy or maybe as an adjuvant therapy," Kaufmann says. "After the chemotherapy has taken care of the primary tumor, this would prevent metastasis."
RNA in the crosshairs
At the University of Michigan, chemistry and biophysics professor Hashim Al-Hashimi is taking a different approach: instead of using RNAi to create drugs, he's creating ways to target the RNAs themselves. "Antibiotics that bind RNA in the ribosome are really the only example of a bona fide drug that we have on the market that we know functions by binding RNA," says Al-Hashimi, who co-founded a company that specializes in RNA targeting, called Nymirum. "There may be more drugs out there that function by binding RNA that we don't know about, but the drugs that are currently known to bind RNA and have an effect are very few, and the ones that are known are the antibiotics, and those compounds tend to be positively charged. That presents a problem in general, for various reasons — they can be toxic, they can be difficult to take up by cells — but certainly these are compounds that demonstrate the proof of principle that one should be able to target RNA."
However, part of the challenge in finding compounds that bind RNAs is developing assays or technologies that will allow researchers to measure the exact effect of a compound on its target. "Most small molecule drugs target proteins and take advantage of the fact that many proteins are enzymatic," Al-Hashimi says. "When a molecule is enzymatic, you can have an enzymatic assay to read the effect of a drug, so you can screen to assess the inhibitory activity of small molecules by simply asking, 'How well does this enzyme do what it's supposed to do in the absence or presence of a small molecule?'" The challenge with RNA, however, is that the majority of RNA targets are not enzymatic, so there isn't an easy way to create a high-throughput assay to measure a compound's effect on the target RNA.
There are methods that involve tagging RNA with modified compounds like fluorescent tags, Al-Hashimi says. "But because RNA is very fickle and flexible — a very delicate structure — having these large tags attached to it can cause problems in terms of perturbing the RNA," he adds. "Also, with these techniques that rely on tagging the RNA, often what happens is that the molecules that you would like to test have features or optical properties that make them ill-suited to these types of experiments because they happen to absorb light at the same wavelengths as the tag does. So there's quite of a bit of limitation as to the types of molecules you can test with these types of approaches."
RNA, camera, action
To address this challenge, Al-Hashimi and his group have developed a new technology to test molecules and see if they will bind RNA. In a perfect world, this would be done with a computer program, Al-Hashimi says, but he notes that most computer programs assume that RNA is a rigid molecule when it is a mobile, flexible structure that's "wriggling and dancing around, and assumes many different shapes." Instead of taking static images of RNA and then asking a computer program to predict which compounds will bind to it, Al-Hashimi records videos of RNA to capture the structure's fluctuations.
"What we do is we take different frames from our movie, highlighting the lock in different shapes, and then ... we test keys," Al-Hashimi says. "We test them not just against one frame, but against all of the different frames we have, and that gives us more shots on target. So we will not, for example, miss the key that can specifically bind to an unusual shape of the lock. With this new technology we can find these keys, and screen them more effectively."
Using NMR spectroscopy coupled with computational techniques that can predict which agents will bind RNA, Al-Hashimi can visualize RNA in motion and screen for existing compounds that could target the RNA to treat disease. He's already successfully identified one compound — netilmicin — which inhibits HIV replication, and continues to screen existing compound libraries to see whether any of the molecules there could be used to target RNAs. "We know a lot about proteins — we know a lot about what molecules they like to bind — so we have a history that we've accumulated over many, many years," Al-Hashimi says. "With RNA, it's just an open field — we really don't know the kind of keys that RNA is going to like, and it might be keys that we have never synthesized. ... The advantage of this computational approach is that we can test molecules that don't even exist, and see if there's a class of molecules that we ought to spend some effort making, because they could be the next generation of small molecules targeting RNA."
Too soon to tell?
Of course, as with the development of any drug, there are questions as to how the body will react, whether there will be toxic side effects, and whether there is potential for the disease to become resistant to the treatment. Al-Hashimi says that it's still too early to tell whether drugs targeting RNA will create adverse reactions or treatment resistance, but says there is evidence indicating that RNA-targeting compounds may escape resistance more effectively than traditional drugs. "Because of the sheer amount of RNA that one has, there are more goals to shoot at," he says. "So the chance that you have one RNA that has more favorable properties might be quite large, simply because of the potential different ways you could attack a disease through targeting RNA."
And, he adds, the potential for beating resistance with combination therapies is high with an RNA targeting approach. "I think the sheer number of targets that are out there and the different strategies one can go about inhibiting a given disease, you can imagine cocktail strategies where you have drugs that bind not one but multiple elements and that could probably really help with the resistance issue, because you're hitting the disease from so many different ends. It's hard for a mutant to occur that can defeat all of them simultaneously," Al-Hashimi says.
Suppressing toxicities would be a matter of making sure the compound has "exquisite selectivity" for its target, so that it affects one specific target RNA and not similar RNAs as well, he adds.
When it comes to RNAi, Merck's Caldwell says that, although it's likely some diseases will evolve resistance to certain RNA therapeutics, the advantage of RNAi drugs is that it's easier to identify potential resistance mutations in the pre-clinical testing stages and be ready with a backup against the mutated version of the disease.
Overall, researchers say that there is a great deal of potential in RNA therapeutics. "The connection of RNA to diseases is literally unfolding as we speak," Al-Hashimi says. "We really have many decades to go to figure out RNAs and develop the technology needed, but now that the interest is there, we'll definitely be able to learn more and figure out how things work."
[Everyone agrees that the RNA system is most likely the clue to "Coordinated Genome Function" and thus has an enormous potential. Presently, as documented by Merck having shut down its $1.1 Bn SiRNA-wing, the field experiences several difficulties. 1) RNA drugs will be difficult to get approved by FDA. 2) RNA drugs are difficult to deliver to the genome. 3) It is not well understood at all, how the RNA system works to yield "Coordinated Genome Function". Regarding the theoretical foundation of RNA system (how contravariant cRNA functors by means of interference with covariant ncRNA functors comprise the functional geometry by their eigendyads, see "The Recursive Genome Function of the Cerebellum: Geometric Unification of Neuroscience and Genomics". Announcement below, pre-publication here, and Abstract, References and Essential Geometric Concepts here - AJP]
High order chromatin architecture shapes the landscape of chromosomal alterations in cancer ["Fractal Defects" as root causes of cancer -AJP]
Geoff Fudenberg, Gad Getz, Matthew Meyerson & Leonid A Mirny
Nature Biotechnology (2011) doi:10.1038/nbt.2049
Received 09 September 2011 Accepted 21 October 2011 Published online 20 November 2011
ABSTRACT - The accumulation of data on structural variation in cancer genomes provides an opportunity to better understand the mechanisms of genomic alterations and the forces of selection that act upon these alterations in cancer. Here we test evidence supporting the influence of two major forces, spatial chromosome structure and purifying (or negative) selection, on the landscape of somatic copy-number alterations (SCNAs) in cancer1. Using a maximum likelihood approach, we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule model2, 3. This analysis suggests that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and that purifying selection, as well as positive selection, influences SCNAs during somatic evolution of cancer cells.
[Consistent with copyright principles that authors retain their intellectual property, the above Nature publication (other than the Abstract) is "for fee", a pre-publication .pdf can be found, with full text and full-size Figures, see Fig. above and text-excerpts below - AJP]
...Here, we ask whether the “landscape” of SCNAs across cancers can be understood with respect to spatial contacts in a 3D chromatin architecture as determined by the recently developed HiC method for high-throughput chromosome conformation capture or described theoretically via the fractal globule (FG) model ...Specifically, we investigate the model presented in Figure 1A, and test whether distant genomic loci that are brought spatially close by 3D chromatin architecture during interphase are more likely to undergo structural alterations and become end-points for amplifications or deletions observed in cancer...
[Some may have been wondering how aberrations of fractal properties of the genome (e.g. its "fractal folding", shown by the rotating 3D Hilbert curve in the header of this site) could lead to genomic pathology. The presented overall "fractal defect" (altering the "optimal functional closeness") is a major finding; leading researchers directly to root-causes of cancer. This article is to be compared to the pre-publication copy of the collective work of authors Pellionisz et al. 2011 (In Press), below - AJP]
Recursive Genome Function of the Cerebellum: Geometric Unification of Neuroscience and Genomics
Andras J. Pellionisz, Roy Graham, Peter A. Pellionisz, Jean-Claude Perez
Chapter in Press: The Cerebellum, Handbook by Springer (Ed. M. Manto). Submitted 20th of October, Accepted 1st of November, 2011
Contact: holgentech_at_gmail_dot_com
Abstract
Recursive Fractal Genome Function in the geometric mind frame of Tensor Network Theory (TNT) leads through FractoGene to a mathematical unification of physiological and pathological development of neural structure and function as governed by the genome. The cerebellum serves as the best platform for unification of neuroscience and genomics. The matrix of massively parallel neural nets of fractal Purkinje brain cells explains the sensorimotor, multidimensional non-Euclidean coordination by the cerebellum acting as a space-time metric tensor. In TNT, the recursion of covariant sensory vectors into contravariant motor executions converges into Eigenstates composing the cerebellar metric as a Moore-Penrose Pseudo-Inverse.
The Principle of Recursion is generalized to genomic systems with the realization that the assembly of proteins from nucleic acids as governed by regulation of coding RNA (cRNA) is a contravariant multi-component functor, where in turn the quantum states of resulting protein structures both in intergenic and intronic sequences are measured in a covariant manner by non-coding RNA (ncRNA) arising as a result of proteins binding with ncDNA modulated by transcription factors. Thus, cRNA and ncRNA vectors by their interference constitute a genomic metric. Recursion through massively parallel neural network and genomic systems raises the question if it obeys the Weyl law of Fractal Quantum Eigenstates, or when derailed, pathologically results in aberrant methylation or chromatin modulation; the root cause of cancerous growth. The growth of fractal Purkinje neurons of the cerebellum is governed by the aperiodical discrete quantum system of sequences of DNA bases, codons and motifs. The full genome is fractal; the discrete quantum system of pyknon-like elements follows the Zipf-Mandelbrot Parabolic Fractal Distribution curve.
The Fractal Approach to Recursive Iteration has been used to identify fractal defects causing a cerebellar disease, the Friedreich Spinocerebellar Ataxia – in this case as runs disrupting a fractal regulatory sequence. Massive deployment starts by an open domain collaborative definition of a standard for fractal genome dimension in the embedding spaces of the genome-epigenome-methylome to optimally diagnose cancerous hologenome in the nucleotide, codon or motif-hyperspaces. Recursion is parallelized both by open domain algorithms, and also by proprietary FractoGene algorithms on high performance computing platforms, for genome analytics on accelerated private hybrid clouds with PDA personal interfaces, becoming the mainstay of clinical genomic measures prior and post cancer intervention in hospitals and serve consumers at large as Personal Genome Assistants.
[This preview of the Abstract, with References and further material to be provided elsewhere, outlines the Non-Euclidean Geometrical Unification of Neuroscience with Genomics, since the authors state at the outset that "Our understanding of both the genome and the brain will remain partial and disjointed until we reach a unification of the intrinsic mathematics of structuro-functional geometry of both – as the first is without question a foundation of the second". The paper features the RNA System as a "Genomic Cerebellum", based on the dual valences (covariant and contravariant) of biological entities that represent invariants, such as movements in sensorimotor coordination in case of the cerebellar neural networks, and physically additive protein-synthesis with the use of RNA from "coding DNA" as well as measures of built proteins by RNA emanating from binding of proteins to "non-coding DNA", where their interference acts as the metric of the functional geometry of coordinated genome function. Based on conceptual breakthrough, the paper lays down an Agenda for a unification of Neuroscience and Genomics both in R&D and in the Industrialization of Genomics. As discussed in the FaceBook page of Andras Pellionisz, this paper is cited even before its appearance, and in view of an accelerated surge of both the demand and technology development (see Nature publication of a Korean school of scientists and technologists below), will lead to rapid advances both in our conceptual breakthrough from "frighteningly unsophisticated" notions of coordinated genome function (called "genome regulation") as well as massive deployment in health-care R&D and Industry, such as fractal diagnosis of cancer ]
Altered branching patterns of Purkinje cells in mouse model for cortical development disorder
Nature, Scientific Reports 1, Article number: 122 doi:10.1038/srep00122
[Purkinje neuron Fractal Dimension is a measure of disease - AJP]
Disrupted cortical cytoarchitecture in cerebellum is a typical pathology in reeler. Particularly interesting are structural problems at the cellular level: dendritic morphology has important functional implication in signal processing. Here we describe a combinatorial imaging method of synchrotron X-ray microtomography with Golgi staining, which can deliver 3-dimensional(3-D) micro-architectures of Purkinje cell(PC) dendrites, and give access to quantitative information in 3-D geometry. In reeler, we visualized in 3-D geometry the shape alterations of planar PC dendrites (i.e., abnormal 3-D arborization). Despite these alterations, the 3-D quantitative analysis of the branching patterns showed no significant changes of the 77 ± 8° branch angle, whereas the branch segment length strongly increased with large fluctuations, comparing to control. The 3-D fractal dimension of the PCs decreased from 1.723 to 1.254, indicating a significant reduction of dendritic complexity. This study provides insights into etiologies and further potential treatment options for lissencephaly and various neurodevelopmental disorders.
The formation of cellular layers and dendritic architectures is essential in the development of cortical structures in the mammalian brain1. Alterations in cortical structures are related to epilepsy, mental retardation, deficits in learning and memory, autism, and schizophrenia2, 3, 4. The alteration patterns of cortical structures are often studied using neurological mutation reeler5, which is characterized by ataxia, tremors, imbalance, and a reeling gait6, 7, 8, 9. In the reeler cerebellum, the cytoarchitecture of neural networks and neurons becomes gradually defective during the developmental process10, 11. The Purkinje cells (PCs) are not arranged in a regular plane but clustered in subcortical areas at early stages of corticogenesis. As a consequence of the ectopic location of such cells, an aberrant laminar organization occurs12, 13.
...3-D Fractal Dimension
This parameter reflects the degree of geometric complexity31 of the PC branching systems. Previous estimates from 2-D data varied among different authors31, 32, 33, 34. We extracted our values using instead 3-D data with the box counting method. The results for normal and reeler PCs were 1.71 ± 0.03 and 1.25 ± 0.02 (Table 2). Our fractal dimension for normal cells is consistent with previous results31, 32, 34, whereas for reeler cells it is significantly smaller, indicating a reduced geometric complexity. The lower geometric complexity is also consistent with the data of Figure 5 and could reflect reduced synaptic connections with other neurons.
...Fractal dimension
To estimate the fractal dimension of the PCs, we used the box-counting method. First, we embedded the data points of the PCs in the 3-D space. This space was divided in a grid of boxes with size r and we counted the number of boxes N(r) that contain at least one data point. A log-log plot of r versus N(r) could be fitted by a straight line with slope -D, where D is the fractal dimension (supplemental Fig. 1). A linear least square regression was performed to accurately evaluate this slope. To determine the scaling region and the slope, two end-points of the size giving the best linear fits were selected.
[It only took less than a quarter of a Century since the Fractal Model of the Purkinje Neuron for Korea to deploy 21 Century high-tech to actually measure in 3D the Fractal Dimension, to be used as a measure of disease. While this technology does not show how the growth of fractal physical geometry is governed by the fractal genome, FractoGene (2002) does. At the point when Samsung already announced by September 1, 2011 a genome analytics service for the World, Geometric Unification by Pellionisz of Neuroscience and Genomics (see announcement below and accelerated exposure above) is expected to result in a breakthrough. This comment can be discussed in the FaceBook page of Andras Pellionisz]
Geometric Unification of Neuroscience and Genomics
October 31, 2011
[Invited Chapter accepted to Springer, with the Editor's comment "A Decade Ahead" by Pellionisz et al. (submitted Oct. 20, 2011). Section "Future Directions" lays down a specific Agenda for Industrialization of Genomics. Inquiries to holgentech_at_gmail_dot_com]
...Initially Jobs sought alternatives to surgery
By Joseph Menn in San Francisco
Financial Times
October 21, 2011 2:44 am
[Section on Apple/Google omitted] ...
[Jobs' cancerous DNA analyzed too early, too late - AJP]
Jobs’ core beliefs are at a more emotional level, involving ideas, where the legal outcomes are less predictable, according to the book.
The 630-page book by Walter Isaacson, simply titled Steve Jobs, gives new details on many other areas of the secretive man’s professional and personal life, including his health and his romances.
It is based on dozens of interviews with Jobs that continued until just weeks before his death from cancer this month, as well as talks with family members and friends.
Some of the biggest revelations involve Jobs’ decisions on his medical treatment, where it appears that a man widely hailed as a genius made the poorest decisions possible.
It had been reported by Fortune magazine in 2008 that Jobs had delayed surgery for what he knew was a highly treatable form of cancer in his pancreas while he pursued alternatives. It emerges that Jobs resisted entreaties by his wife, a cancer survivor, former Intel chief Andy Grove and others close to him to have the small tumour removed because he did not want his body to be “violated”, Mr Isaacson told the CBS television show 60 Minutes.
After Jobs finally gave in, it may have been too late. Doctors discovered that the disease had spread to neighbouring tissue, Mr Isaacson said, and Jobs “regretted” his initial reluctance. The news programme posted an excerpt of its interview with Mr Isaacson on Thursday ahead of its full broadcast on Sunday night.
After Jobs accepted a traditional medical approach to his illness, he mastered it in detail and made the final decisions on all treatment, according to an account of the book in the New York Times. That included approving the sequencing of his own genes, which allowed for hand-tailored treatments, a pioneering approach that Jobs believed was key to the future of medicine. But not all that Jobs wrought, at Apple or in his personal life, was a success.
[Steve's cancerous DNA (compared to DNA of healthy tissues) was analyzed when his condition was too advanced, and the science was too early. Thus, we lost not only a giant, but a dear friend. Bill Gates had been asked if he wanted his DNA fully sequenced. After some hesitation, he said no ... but perhaps if I had cancer, I would. Now both Intel Founders Andy Grove and Gordon Moore should know that the genomics hinges on ... Informatics. (Gordon Moore had been sequenced twice, not for disease, but to compare Life Technologies' two very different sequencing platforms). Time is ripe for Informatics Giants to help programs, such as laid out in the invited Chapter submitted October 20th "Geometric Unification of Neuroscience and Genomics" (by Pellionisz et al.). This entry can be discussed in the FaceBook page of Andras Pellionisz]
Savoring an NGS Software Smorgasbord
In the latest crop of analysis tools for NGS data, functionality and ease-of-use are twin priorities.
By Allison Proffitt
September-October issue of Bio-IT World, 2011 | ‘Scaling to bigger and better hardware doesn’t help if your data is [sic] growing in size faster than your hardware,” says Titus Brown at Michigan State University. He and others in the NGS community are calling for software solutions to their NGS data woes instead of massive storage options. In an August post on his blog, “Daily Life in an Ivory Basement,” Brown wrote: “The bottom line is this: when your data cost is decreasing faster than your hardware cost, the long-term solution cannot be to buy, rent, borrow, beg, or steal more hardware. The solution must lie in software and algorithms.”
Thankfully, the options for both are expanding. Familiar names such as CLC bio, Geospiza, DNAnexus, GenomeQuest (see, p. 24), Omicia (see, p. 48) and others (see, “Next-Gen Sequencing Software: The Present and the Future,” Bio•IT World, Sept 2010) are being joined by a new batch of friendly competitors. For the most part, these offerings—from aligners to niche analytics—support the Illumina, 454, and SOLiD platforms, with some including Ion Torrent, Pacific Biosciences, and Complete Genomics data as well.
The software landscape for NGS analysis is broad and varied partly because “analysis” isn’t a cut and dried term, says Knome’s Nathaniel Pearson, director of research. “We’ve managed, as a community, to make people understand that analysis is as important as sequencing in the end… But now we have to tease out upstream and downstream analysis.”
Pearson defines “upstream analysis” as that closest to the sequencing machines, where the first work was done: base calling, variant calling, variant assessment, etc. “Now we’re seeing a focus moving toward downstream analysis, toward understanding many genomes at a time. As the stream of sequencing data from one machine comes together in a river with the streams coming from other machines, we need to make sense of that tide of data.”
Swimming Upstream
Knome’s area of interest can be summarized as “service with software,” says Pearson. kGAP—Knome’s Genome Analysis Platform—is the analysis software Knome uses to “richly annotate genomes and compare them to each other thoroughly,” says Pearson.
Knome’s sequencing and genome analysis service was launched in 2007. “Knome cut its teeth analyzing whole genomes for consumers. Given how costly whole genome sequencing remains, most of those consumers are still either healthy and wealthy aficionados of science and technology, or physician-aided families with urgent health problems—fairly small markets,” says Pearson.
“We do foresee that the consumer market will eventually democratize, as sequencing gets cheaper and insights for small numbers of relatively healthy genomes—especially in family settings—become more precise and useful,” he says.
Until then, Knome plans to keep refining its analysis pipeline and end-user software Today more than 95% of the firm’s customer base is researchers, about half from academia and half from industry, users that Pearson says can best understand diseases of widespread public interest.
When these customers receive Knome’s analysis they also receive software tools like KDK Site Finder, a simple query interface that lets clients find interesting sites in one or a set of genomes by “sensibly chosen criteria: allele frequency, call quality, novelty, zygosity—the usual suspects—as well as a rich archive of gene- and site-associated phenotype data from the literature.”
The current version of kGAP runs in the Cloud, which has greatly increased its throughput. But Pearson doesn’t expect analysis costs to fall at the rate of sequencing costs. “They’re going to drop slower than sequencing costs overall because we’re more tied to computational costs—which is more of a Moore’s Law scale,” he says. “Some software will fall quickly; it’ll get commoditized. But the very best software will always be costing a bit more because it will entail evermore complex underlying calculations to make the bottom line look much simpler to use.”
He believes future analysis options will do for sequencing what Photoshop did for photography. “I think we’ll see software for the end user for understanding genomes [in which] a lot of the underlying calculations will be done very swiftly and very cleverly under the hood. And the user’s experience will be very easy and very fast, but that’s going to cost a bit.”
The team at Real Time Genomics might disagree. The company’s “single and only intent is to provide the world’s best genomic analysis software,” says CEO Phillip Whalen. And they’re giving it away for free.
The venture-funded company based in San Francisco unveiled its website only a few months ago, but the technical team has been working on this problem for seven or eight years.
“The decision we made when we basically took the wrappers off,” says Whalen, “was that for organizations we wanted to charge a license fee, but if [researchers are] working on a project and they decide, ‘I’d like a really tight, easy to use pipeline,’ absolutely the use of our software by an individual investigator is unrestricted.”
RTG Investigator is made up of two such pipelines: one geared for variant detection and one for metagenomics. The software runs from a command line interface and is geared toward research teams that include both bioinformaticians and biological investigators. “Our customers wring the last bit of information out of their datasets, and the tension of discovery demands a collaborative effort,” says Stewart Noyce, RTG’s director of product marketing.
“Right at the core is this extremely fast and sensitive searching technology,” says Graham Gaylard, RTG’s founder. “When I say sensitive, we actually can search with mismatches in the search pattern right at the very start. “The variant detection pipeline does all of the alignment—it’s a fully gapped aligner—so it does full read matching assembly and also processing right through to variant calls such as SNPs, complex calls, indels, CNVs [copy number variations] and structural variations,” says Gaylard. “It handles paired ends natively, not as an add-on. That gives us far superior efficiency. We’re as accurate as all of them, but we’re faster than the BWA/GATK pipeline by 10x.”
And the numbers are even better for metagenomics. “One of the functions our search technology replaces is BLASTX, a translated nucleotide search of protein databases. We’re 1,000x faster than that.” The Genome Institute at Washington University acquired some of the early licenses for the product a couple of years ago and RTG has worked closely with them on the Human Metabolome Project. Gaylard says RTG has turned a 10-year compute task on their cluster into a three-month problem. “That has a big impact on how you do things,” he says.
The software is designed to make maximum use of the computing resources allocated to it, and will run on a laptop or cluster or can be pushed to the Cloud. Everything is proprietary—new algorithms, a new approach, and patent protected (or pending). “We have not gone out and taken something open source and tweaked it,” says Whalen. “We have attacked the problems from a computer science point of view with new ways of doing things. We’ve done that from scratch and come up with some results that our customers say are pretty compelling.”
Betting on Biologists
Though some users are happy at a command line, Enlis Genomics and others are betting that many biologists would like to dig into their data without also learning bioinformatics. Enlis’ “point-and-click genomics” software was designed by biologists, says founder Devon Jensen.
The software caught Illumina’s eye in July, winning the commercial category in the iDEA Challenge (see, “Illumina Showcases New Visions in Genomic Interpretation,” Bio•IT World, July 2011; part of the prize was a one year co-promotion marketing agreement with Illumina. Jensen says the details of that agreement are still being finalized).
This isn’t variant calling though. The software addresses the biologist’s question: After you have sequenced, assembled, and called variants, what do you do next? Tools like the Variation Filter and the Genome Difference tool let users query the genome and compare up to 100 genomes. “The focus of our software is making it easy to find what is biologically relevant in the sequence data of a patient, individual or research animal,” says Jensen.
The Enlis software comes with an import/annotation tool that creates a .genome file format encapsulating all the different types of genomic data into a single file to improve the process of handling and storing the data for the researcher. The focus is on speed and ease. “The software contains very fast algorithms for filtering variations and finding differences between whole genomes,” says Jensen. “We have organized all of the information in a way that allows a researcher to quickly assess whether a particular feature of a genome is important.”
SoftGenetics’ NextGENe product is also aimed at the individual biologist or clinician, says John Fosnacht, the company’s co-founder and vice president. “It’s a Windows-based program that’s easy to use. It has a lot of tools in it that [users] can use on multiple applications. It’s doesn’t require any kind of bioinformatics support.”
Fosnacht says the company has several groups of customers, including core labs that don’t have huge bioinformatics resources. The Mayo Clinic, for example, is using a networked version of the software. The software will process a whole human genome in ten hours, Fosnacht says.
In a partnership with the rare disease group at NIH, SoftGenetics developed a variant comparison tool as a module in NextGENe to identify which of thousands of variants are most likely to be causative mutations in rare genetic disorders. The software takes the total number of variants (more than 275,000 variants in a family of 6 in one example) and filters out silent variants, known mutations in dbSNP, and other parameters. The NIH researchers were left with a very manageable six candidate mutations.
“The filtering and prediction part takes less than half a day. That allows the molecular geneticist and researcher, instead of trying to do the impossible and look at 280,000 variants, to focus on relatively few,” says Fosnacht.
The software uses a modified Burrows-Wheeler transform method, and excels in indel discovery and somatic mutations. NextGENe was able to find a 55-basepair deletion in a 50-bp read. “This is a patented functionality in the software, that can elongate short reads,” says Fosnacht. “In reality it is a localized assembly. Once the reads are elongated the software can detect an indel up to 33% of the elongated length. The same process can be used to actually merge paired reads into one long read. When employed this process can produce Sanger quality reads from short reads.”
These types of projects make the most of what Fosnacht calls tertiary analysis tools. “We want to provide the third level of tools to the actual users to speed up the whole process. Unlike many “freeware” or other programs that just give you a list—an Excel spreadsheet basically—of all the variants that were found, you can actually see them in our browser… A lot of people like to touch and feel, you might say, their data.”
DNASTAR agrees. “There just aren’t enough bioinformaticians out there to handle the data deluge,” says Tom Schwei, DNASTAR’s VP and general manager. “And they don’t want to wait in line for a week or two weeks for that bioinformatics core group. We believe that the end user, the person who is sponsoring the experiment, knows best their research objectives and their data and is in the best position to do the analysis… You shouldn’t have to be a bioinformatician to parse through the data and understand what you see.”
The company views the NGS market as simply an extension of what their customers are already doing. As such, DNASTAR recently moved their next-gen data products under the Lasergene umbrella, a 15-year old brand name that also includes primer design software and cloning resources. SeqMan
NGen is the GUI-based assembler, SeqMan Pro the data analysis module. They are designed to work together, although they can be purchased separately.
Schwei says that the new Lasergene offerings are designed to be intuitive, fast, and easy to use. Users can easily compare their variants to dbSNP and the reference genome.
“SeqMan Pro’s strength is really the analysis of any number of samples. It can handle individual assemblies quite well, and it can handle multiple assemblies.” The software can manage 100 samples of a certain region, says Schwei. “We will do separation of the tags if people are running multiple samples in one lane on the assembly side. We’ll then report on those samples on the analysis side.”
The software is also affordable. “For less than $10,000, scientists can get all the software they need—and the computer to run it on—to do any next-gen assembly and analysis project they need to do,” he says, thanks to proprietary assembly algorithms. “Basically, it no longer relies on the amount of memory you have on your computer,” Schwei says. “There’s no correlation between the amount of RAM and the size of the genome you have to assemble.”
Avadis NGS by Strand Scientific Intelligence enables “NGS analysis for the rest of us,” says Thon de Boer, director of product management, software. With a strong focus on visualization, Avadis NGS has three major workflows: DNA-seq, CHIP-seq, and RNA-seq. De Boer says Strand is focusing on “the individual researcher with their individual [sequencer] and their individual piece of software.” The desktop software manages analysis after alignment, the “backend” analysis, de Boer calls it, and he says that Strand has been able to “sell to places that already have the Genomics Workbench from CLC bio, for instance, because people really like our visualization.”
“We have special never-seen-before visualizations around splicing—very informative alternative splicing analysis visualization. And the same goes for SNP analysis, what we call the variant supporter view, which is just a better way to look at all of the supporting reads for a particular SNP without being overwhelmed with the amount of data you have to look through.”
Strand has also had success partnering in the field. Ion Torrent is a reseller of Avadis NGS, and Strand recently announced a partnership with German-based BIOBASE to give all Avadis NGS users one-click access to BIOBASE’s Genome Trax curated biological database. “We bundle a lot of our software with publicly available data,” says de Boer, and the partnership with BIOBASE will expand the available data pool and “make it easy for customers to get all the information that they need right from our servers.”
Partek’s Genomics Suite is a complete start-to-end solution,” says Hsiufen Chua, Partek’s regional manager in Singapore. “Just one package off the shelf and you can use all the genomics data analysis you need in the lab.”
Partek’s product integrates sequencing data with microarray data or real time PCR because, as Chua points out, most labs have several types of data. “[Customers] would like to bring together two sets of data because they would have samples that have been run on different platforms.” Genomics Suite allows users to compare the results in the same platform.
“From the point that [researchers] obtain the reads from the next-gen sequencer, we take care of them. We have solutions to help them align the reads down to the point where they can do quality control to see if the data they have is good enough to proceed for further analysis. If so, then we have the tools for them to do the statistical analysis—all the statistics. Following that, we also have the same tools to do the biological interpretation.”
Service Segment
But if a do-it-all platform is not what a researcher wants, the analysis-as-a-service segment of the market is expanding. While BGI (see, p 31) and Complete Genomics will do sequencing and analysis, Samsung just launched beta testing of an analysis-only service.
Samsung’s Genome Analysis Service will provide analysis for whole-genome sequencing and RNASeq for Life Technologies and Illumina data, says SungKwon Kim, director of the bioinformatics lab at Samsung SDS, with support for the Ion Torrent sequencer ready by the end of 2011.
The algorithms that Samsung SDS is using are a combination of open-source and vendor-provided software with Samsung’s own proprietary “tweaks,” says Kim. Samsung has built its own genome browser, but all of the data are available for download if the customer prefers another option.
Samsung is offering analysis on its own Cloud infrastructure in Korea, which Kim expects it to be extremely efficient, safe, and fast. “I think our analysis job is much faster than other competitors,” he says. “Our whole genome analysis will take five days; our RNA analysis will take 3 days.”
He also cites Samsung’s reputation for enterprise-level IT. “We’ve been working with system innovation with banks, high-profile Fortune 500 companies, so when it comes to data security—I don’t think any other vendor companies should be able to match our capabilities in security and recovery handling.”
Kim says Samsung has been eyeing the NGS space for three years. “This [industry] is mainly driven by academics and research institutions who have some of the IT infrastructure and who have their own sequencers… but when the read price drops below $1,000, then I don’t think any research institute or academia will be able to handle [both] their own sequencing jobs and their own analysis jobs.”
With so many options, they shouldn’t have to. •
This article also appeared in the 2011 September-October issue of Bio-IT World magazine.
Foundations of XXI Century Industrialization of Genomics
We're at a frighteningly unsophisticated level of genome interpretation
http://www.signonsandiego.com/news/2011/sep/20/venter-institute-breaking-ground-35-million-center/
Venter Institute breaking ground on $35 million center
While the institute has borrowed money to launch the work, its leaders are hoping to pay for some of the project's cost with donations from local philanthropists.
''It's now easy with the new technology to generate a lot of different data, but there are very few groups or scientists generating knowledge out of this data. We're at a frighteningly unsophisticated level of genome interpretation.''
Read more: http://www.smh.com.au/world/genome-research-little-bang-for-buck-scientists-20100401-ri2t.html#ixzz1YztapdeV
http://www.sisbq.org/uploads/5/6/8/7/5687930/qbtherapy.pdf
Stagnaro, S. and Caramel, S. (2011) Italy
"...human bodies are a continuum of biological systems whose dynamics follow the laws of deterministic chaos (Lorenz 1963, Ruelle 1991, Cramer 1994, Stagnaro et al. 1996), which can be measured by means of nonlinear statistical invariants. Furthermore, there is the recent discovery that energy information and communication between DNA and bio-systems are strictly linked with quantum behavior."
As more and more people's genomes are decoded, we need better ways to share and understand the data.
FRIDAY, SEPTEMBER 23, 2011BY DAVID EWING DUNCAN
If the Internet cloud were actually airborne, it would be crashing down right now under the sheer weight of a quintillion bytes of biological data.This year, the world's DNA-sequencing machines are expected to churn out 30,000 entire human genomes, according to estimates in Nature magazine. That is up from 2,700 last year and a few dozen in 2009. Recall that merely a decade ago, before the completion of the Human Genome Project, the number was zero. At this exponential pace, by 2020 it may be feasible—mathematically, at least—to decode the DNA of every member of humanity in a single 12-month stretch.
The vast increase in DNA data is occurring because of dazzling advances in sequencing technology. What cost hundreds of millions of dollars a decade ago now costs a mere $10,000. In a few years, decoding a person's DNA might cost $100 or even less.
But what's missing, say a growing chorus of researchers, is a way to make sense of what these endless strings of As, Gs, Cs, and Ts mean to individuals and their health. "We are really good at sequencing people, but our ability to interpret all of this data is lagging behind," says Eric Schadt, director of the Mount Sinai Institute for Genomics and Multiscale Biology and chief scientific officer at California-based Pacific Biosciences, which sells sequencing machines.
Scientists don't yet know what all our DNA does—how each difference in genetic code might influence disease or the color of your hair. Nor have studies confirmed that all the genetic markers linked to, say, heart disease and most cancers actually increase a person's risk for these illnesses. Just as significant, the thousands of genomes being cranked out right now can't easily be compared. There is no standard format for storing DNA data and no consistent way to analyze or present it. Even nomenclature varies from lab to lab.
The industry is working to address these problems. ["Industry" will never solve a problem that is SCIENTIFIC - AJP]. Earlier this summer, at a meeting of geneticists and other experts that I attended in San Francisco, Clifford Reid, the CEO of Bay Area-based Complete Genomics, called for a consortium of gene companies to develop sorely needed standards for everything from consent procedures for DNA donors to methods of collecting, storing, and analyzing DNA specimens. Reid says the ultimate purpose is to "aggregate multiple data sets, providing broad access to data sets that are today in silos and largely unavailable to the broader scientific community."
The payoff from "interoperable" genomes will be faster research on the links between DNA and disease, scientists say. Researchers will be able to validate suspected links between genetic makeup and drug reactions or overall health by conducting much larger studies in which many people's genomes are compared. And physicians and individuals will be able to use standardized methods of reporting a person's genetic risks and advantages. That will matter as more and more ordinary people have their DNA decoded.
Another major initiative comes from Sage Bionetworks, a Seattle-based nonprofit cofounded by Schadt and Sage director Stephen Friend, formerly the leader of Merck's advanced technologies and oncology groups. Sage has raised $20 million to support a movement among biologists, computer scientists, patient advocacy groups, and businesses to standardize DNA databases that have sprung up over the years. "This won't happen overnight," says Schadt. "But it will be huge, like the Internet."
At some companies, efforts are under way to build an IT infrastructure capable of pooling and interpreting whole genomes on a larger scale. Jorge Conde, the CEO of Knome, a company in Cambridge, Massachusetts, that sells whole-genome sequencing as a service and uses a team of PhDs in India to analyze the results, says more drug companies now want to use full genomes to understand why drugs work or have side effects in some people and not others. "As the price has dropped, we are getting more interest from pharma and biotech companies," says Conde. Knome's price for its sequencing and analytical service has dropped from $350,000 in 2007 to under $10,000 today.
One of Knome's more recent ideas, still at an early stage, is to get drug companies to share genomes they have had decoded. The company has launched a cloud-based service called kGAP that would let customers process several hundred genomes at one time, studying them for the presence of 200,000 known links between DNA markers and genotypes for disease and other traits. The technology is still oriented toward facilitating big research projects, but eventually such engines might be used to compare an individual's genome with thousands of others and spit out personalized health tips and diagnoses. "The big play is when this information is available to be used by health-care providers and patients," says Conde. "But that's still several years away."
[The colossal threat of unsustainability of Industrialization of Genomics; a hyper-escalating number of available full DNA sequences glutting the supply-side, and an almost total lack of the "demand side", of overall theoretical understanding of how The Recursive Genome Function arises (in the form of iterative fractal recursion) from the Fractal DNA) I disseminated as early as it was possible once the US government admitted in 2007 in their ENCODE results, that the underlying assumptions have been false for over half a Century. The peer-reviewed science publication was The Principle of Recursive Genome Function, and the now outrageously evident facts were popularized in a Google Tech Talk YouTube (both in 2008), making the trivial point that Information TECHNOLOGY is more than ready, but Information THEORY of genome function is not. With the software-enabling recursive algorithms of FractoGene, HolGenTech, Inc. in Silicon Valley, working with global strategic partners, is ready to come to the rescue. This entry can be discussed at the FaceBook page of Andras Pellionisz]
Sam Waksal, Pfizer Venture Investments, and More: Moderator Looks Forward to All-Star Chat at New York Life Sciences 2031
Arlene Weintraub 9/6/11
Les Funtleyder, manager of the Miller Tabak Health Care Transformation Fund (MTHFX), recently told Xconomy that in a few years, investors are going to look back and wish they had invested more in healthcare today. That forward-thinking attitude prompted Xconomy to invite Funtleyder to moderate our first public New York event, Life Sciences 2031, a panel discussion that will take place October 13 at the Alexandria Center for Life Science.
Funtleyder, who is also author of the book Health Care Investing (McGraw Hill 2009), spends his days contemplating what the future will hold for pharmaceuticals, biotechnology, and health care—and searching for the companies that are best poised to capitalize on those trends. So he’s excited by the prospect of polling the event’s four panelists on current trends in those industries and what they portend for the next 20 years. “The level of expertise on this panel will give the current era some context,” Funtleyder says.
The panelists bring a wide range of experience to bear on what’s sure to be a lively discussion. Sam Waksal was the founder and CEO of ImClone Systems and now serves as CEO of Kadmon, a New York-based biotech startup working on drugs to treat cancer, autoimmune diseases, and infectious diseases. Barbara Dalton is a scientist-turned-investor—a Ph.D. trained in virology and immunology who is now VP of venture capital for Pfizer. Sam Isaly, founder of OrbiMed Advisors, manages the popular Eaton Vance Worldwide Health Sciences Fund. And Eric Schadt is a genomics expert who serves as the chief scientific officer of Pacific Biosciences, as well as the director of the New York-based Mount Sinai Institute for Genomics and Multiscale Biology.
Funtleyder expects all the panelists to weigh in on one of the top questions on everyone’s mind: What are the next areas of growth in research? “The fact that they’ve all been around a while will allow them to bring to the table some ideas about how we can increase R&D productivity in the industry,” Funtleyder says.
For his part, Funtleyder believes R&D has reached an inflection point. “We had Genomics Part 1—the sequencing of the human genome,” he says. “Now there’s Genomics Part 2. The cost of sequencing a genome has come down from $3 million to $1,000 and the speed has gone up. Is this going to lead us into better drug discovery in the next decade? Or are we having another fad and nothing will ever come from it? That will be an interesting question to discuss.”
Personalized medicine is one of the goals of genomics research, Funtleyder points out, and will no doubt be part of the discussion at New York Life Sciences 2031. “Personalized medicine is the holy grail. But are we there yet? I don’t know. It seems like it’s inevitable. But what’s the timing? It could be a while,” he says.
Pfizer’s Barbara Dalton will bring valuable perspective as someone from Big Pharma’s ranks, Funtleyder predicts. “What’s the business model of the future?” he wonders. “Pfizer is setting up campuses all over, in Massachusetts, New York, California. Is that the new model—hiring universities, basically, to do research for you?” Funtleyder is also interested in how Pfizer’s acquisition choices might impact the rest of the industry. “The old saying is ‘If Pfizer sneezes, the industry catches a cold,’” he says. “What Pfizer does will guide what other companies do, and it will also affect the decision making by small and mid-cap biotechs. They want to make themselves attractive to Big Pharma.”
Funtleyder also wonders what the future holds for small biotechs. Some promising startups are getting snapped up by Pfizer and other large companies, he notes. But what about the legions of small biotechs that need years of research to bring their ideas to fruition—and the capital to support those projects? He’s particularly interested in what “Sam and Sam” will have to say about that, seeing as Waksal is running a startup, and Isaly’s firm has a venture arm. “It seems like private equity and venture capitalists are taking less risky bets,” Funtleyder says. “Is this setting us up to no longer have a small biotech industry in the middle of the decade? The good ones will get acquired, the bad ones will go out of business, and we’re not seeing too many public offerings.”
The biggest macro-risk for the future of the industry, Funtleyder believes, is government spending. “We’re having cost problems in the U.S.—that’s not going to go away,” he says. “We’re going to have health care reform. How are we going to grapple with it over the next decade?” Funtleyder hopes to gather perspectives and advice on that issue from all four panelists.
This being a New York event, there will, no doubt, be plenty of discussion about what needs to happen for the city to become a biotech hotbed—long the goal of Mayor Michael Bloomberg and other politicians. “This has been a knock on Manhattan forever,” Funtleyder says. “We have eight research institutions and more people than Boston or San Francisco. It’s always been a mystery to me why we don’t have a thriving biotech industry.”
The four panelists should have some creative ideas for fostering growth in the city’s life sciences sector, Funtleyder believes. “This is a great industry from an economic-development point of view,” he says. “Having life sciences as a growth engine in this town would create other growth engines.”
Funtleyder won’t be the only one firing questions at our panelists, as we will leave plenty of time for audience Q&A. So please join us at Xconomy Forum: New York Life Sciences 2031 by registering here.
[Having spent 14 years as Professor of New York University Medical School, originating a tensor- and now fractal geometrical approach to identify the intrinsic mathematics of living systems (both in Neuroscience and Genomics, unifying the two) I have plenty observations and suggestions to share. This comment can be discussed in the FaceBook page of Andras Pellionisz]
Lost In Translation? Andy Grove blasts "Change the System!" in his Anti-Medical School Course at UC Berkeley
California Institute for Quantitative Biosciences
published by Adam Mann on Thu, 09/01/2011 - 16:57
In QB3’s Anti-Medical School course, UC Berkeley and UCSF bioengineering students try to solve real-world problems that doctors face in the clinic.
Wednesday night, QB3 hosted a talk at Byers Auditorium in Genentech Hall entitled “Translational Medicine: Key to Progress or Bridge to Nowhere.” Speaking at the lecture was Andy Grove, co-founder and former CEO of the semiconductor giant Intel.
Grove spoke about the problems currently facing drug development. In terms of time and investment, he said the closest equivalent process in history to the creation of a single drug is the construction of a single pyramid in ancient Egypt. According to his estimates, a pyramid cost the equivalent of $1.5 billion US dollars and took 20 years to construct—similar to the average drug development cost and time in the US since the early 90s.
Things are only set to get worse. Grove pointed out that modern science is currently facing a new age of genetic- and cellular-based personalized medicine. The complexity of delivering such individualized drugs repeatedly and effectively will require a new way of thinking about medicine. Researchers will need to rely heavily on integrative technology and collaboration. Grove cited the implantable artificial kidney under development in the lab of Shuvo Roy at UCSF as one such approach to overcoming such problems.
But other difficulties loom. Because of the extraordinary complexity of personalized medicine, researchers have a hard time telling the truly relevant effects from everything else, said Grove. [Algorithm of Fractal Recursive Iteration has "parameters" - keeping fractality intact, resulting in harmless human diversity, whereas "syntax fractal defects" breach the fractal integrity - a revolutionary way to tell chaff from needles in a haystack - AJP]. This unsettles investors, who consider biotech a “risky business at risk.” Currently, potential biotechnology seed investments are diverted to Silicon Valley social networking sites, he said.
And then he delivered the bad news.
According to Grove, the industry is not primed for the overwhelming change it will face in the coming decades. Transformation is needed, he said repeatedly. This revolution will only come if people can lower the resistance to flow of knowledge from labs and research organizations to pharmaceutical companies.
Ideally, Grove said, knowledge flows from a medical center to industry and dollars flow back. Biotech startups are the perfect transitional step between such institutions. Unfortunately, due to difficulties in integration and regulation, nothing flows either way, said Grove.
The solution for him is to do science and early research inside the industry. Grove cited his work at Intel, where he got rid of the R&D department and placed it entirely inside manufacturing, streamlining the process. Perhaps such a model could be a boon to clinical medicine, he suggested.
Regulation has also become a barrier to companies trying to develop new medicine, said Grove. Because it is so easy to file, the US patent office is overwhelmed with trivial and obvious inventions. This is threatening and kills innovation, he said. Furthermore, the agency has been slow to upgrade from paper-based technology to computers, which would greatly reduce waste and increase speed, he said. [A good illustration is that the USPTO requires, in its latest twist of an ardous struggle, more than 9 years of the priority date of submission in 2002 August a re-writing of FractoGene core-patent, incorporating attachments already submitted. The expense of this (and claiming extension for 9 years lost) forces a "shotgun marriage" with a giant that can handle this imposed burden - while benefitting a lucky US IT global leader, determined to edge into Genome Analytics 50/50 from the now incredibly precious early priority-date - AJP]
In addition, the FDA, while trying to help, has also slowed innovation. Every year, 800,000 scientific papers related to new drugs appear in the literature, said Grove. From these, 6,000 new drugs make it to phase 3 FDA trials. But because of stringent standards, only 20 new drugs come to market each year.
Still, Grove’s talk was not all about gloom and doom. He urged scientists and doctors to get involved in trying to change the system. Ultimately, researchers need to look into doing science that’s not just for itself, Grove said. They need to produce products that help people.
Comment on the Article by Pellionisz:
Happy Birthday, dear Andy! It speaks volumes that a truly great man celebrates his landmark birthday-eve by looking ahead to swim across rough waters of the future of human kind. The absolute need of "new ways to think about medicine" [and biology at large] recalls perhaps just two outstanding precedents. John von Neumann turned to an informatics-based biology having accomplished so much for the first serial computer architecture. Neumann looked ahead to times when "the von Neumann bottleneck" will halt what later became "Moore's Law". Gordon Moore's genome fully sequenced twice just recently most likely makes also Andy Grove think in overdrive how to put genome analytics code on a chip, as the future device capable to match the data-flow parallel processing by the genome itself. Prior to von Neumann, Nobelist Schrödinger addressed in "What's Life" the quantal hereditary code-script as information by aperiodical covalent bondings of hydrogen. Without turning the vision into reality of giants of thinker-architects of strategies for a better future human kind will do poorly. [This comment can be discussed at the Lost in the Translation original website, as well as in the FaceBook page of Andras Pellionisz]
[A particularly crass example of "scientists" who never produced anything to help people suffering from "Junk DNA diseases", but on the contrary as a detractor excelled only in badmouthing pioneers of genome informatics is a Canadian Academic in semi-retirement. With his blog "Revisiting the Central Dogma in the 21st Century", while 11 years late even with his calendar and unable to produce mathematically properly rounded percentage-numbers, the topic fetched to date an astronomical, close to 500 comments, the vast majority is totally off-topic cyber-sewage and cyber-libel. One trying to focus on the issue asked "what if Crick was dead wrong with his Dogma?" and an answer pointed out that even Crick considered that as a "game changer". Nonetheless, an avalanche of superficial comments from people who admitted their incompetence in science matters, produced an utterly worthless "papering over" of the issue itself, by digressing into a flabbergasting array of meaningless tangentials. Perhaps out of his embarrassment (over becoming infamous for his libelious badmouthing) the blog-owner (while still uphelding the legally faulty policy making him liable of letting "anonymous" postings) now openly censors out attempts to steer attention to the paradigm-shift necessitated by historical failures of the two mistaken dogmas of Old School Genomics - AJP]
W.M. Keck Foundation awards Jefferson scientists with $1M medical research grant [Rigoutsos - AJP]
Published on August 4, 2011 at 2:23 AM
Scientists at Thomas Jefferson University have been awarded a $1 million medical research grant from the W.M. Keck Foundation for an ambitious project looking at the little explored 98 percent of the human genome and what role it may play in the onset and progression of diseases.
The multidisciplinary team, led by Computational Medicine Center at Jefferson director Isidore Rigoutsos, Ph.D., will soon begin studying a particular group of DNA motifs-genomic combinations of letters that repeat more frequently than expected by chance-called pyknons. Researchers and physicians will be looking at what function they serve in the context of several types of cancers, platelet aggregation properties, two autoimmune disorders, and type-1 diabetes.
Dr. Rigoutsos, a world-renowned computational biologist, originally discovered pyknons in 2005 using computational analyses. In the time since their discovery, evidence has been slowly accumulating that these pyknon motifs mark transcribed, non-coding RNA sequences with potential functional relevance in human disease.
"This is very exciting. The grant comes on the heels of six years of research," said Dr. Rigoutsos. "It will help us get to the bottom of this story: an unexplored territory that we strongly suspect has something important to reveal about human disease. There is disconnected evidence, and we want to assemble all the pieces."
For many years, Rigoutsos, who came to Jefferson in 2010 following a nearly 18-year tenure at IBM's Research Division, focused on generating conspicuous tidbits of evidence computationally, the result of his not having access to experimental facilities. All of this has, of course, changed at his new home: the Computational Medicine Center, which he founded at Jefferson last year.
Now, he said, he can cast a wider and deeper net by studying pyknons using samples from a diverse collection of human conditions: prostate, colon and pancreatic cancer, chronic lymphocytic leukemia, type-1 diabetes, hyper- and hypo-reactivity in platelets, multiple sclerosis, and systemic sclerosis. Dr. Rigoutsos is also a member of the Kimmel Cancer Center at Jefferson.
The goal is to investigate the presence of pyknon-marked non-coding RNAs in these conditions and determine the rules governing the biogenesis, processing, and mechanisms of regulatory action of these transcripts. The planned research activity will involve a combination of computational analyses and modern experimental techniques.
The winning team comprises researchers and physicians from the Computational Medicine Center and several Departments of Thomas Jefferson University and Hospital, the Children's Hospital of Philadelphia, the University of North Carolina at Chapel Hill, and the University of Texas' MD Anderson Cancer Research Center.
"It is a great honor to be recognized by the W.M. Keck Foundation, which has a long history of supporting innovative and pioneering medical research," said Mark L. Tykocinski, M.D., Dean of Jefferson Medical College and Senior Vice President of Thomas Jefferson University. "This is a unique award for a unique area of human genome research that, with our multidisciplinary approach, will undoubtedly pave the way for breakthrough discoveries to help better treat and prevent diverse diseases."
[Nobel Prizes in Genomics will be awarded in Physics, for biophysicists, picking up the trail left by Erwin Schroedinger (What is Life? - 1944) who predicted that non-periodical covalent bondings of hydrogen encode life by a mathematics unknown to him - and the rest of the World. After the "Big Genome Letdown" decade since Full Human DNA sequencing (revealing the sequence of bondings, but absolutely not "cracking the code" how life is encrypted in a sequence, a new era started with biophysicists (often, with degrees in pure math, bioinformatics, computer science, etc) - who have been traditionally sidetracked as "rebels". Now, "rebels are becoming leaders". Isidore Rigoutsos is in the same rank as Eric Schadt or Eric Lander (soon, Francis Collins will also be widely known to have had quantum mechanics before his full M.D./Ph.D...). There will be no Nobel Prize for "the gene of cancer, schizophrenia, autism, diabetes etc. - since with the failed "gene discovery" is extremly unlikely to come up with (almost for sure, non-existent) "genes for complex hereditary syndromes". (This is not to deny, that about 2,800 so-called "Mendelian diseases" are already on record, since even a single letter of A,C,T,G can turn by mutation an amino-acid producing codon e.g. into a "premature termination codon" - thus resulting in truncated thus disfunctional or even toxic protein and thus dreadful disease. However, "Genome Regulation Diseases" will not be so "easy" to map - an entirely new science is needed to mathematically understand "Recursive Genome Function" - though as yet still a minority of leading scientists are fully aware of the game-changer of discarding an "open loop" obsolete axiom and carting in the "recursive, e.g. fractal iteration" algorithms. Eric Lander already joined the fray of the "fractal nature of DNA" - grabbing Grosberg's 20+ year old seminal concept of fractal folding of the 2m long "noodle" of DNA into the 6 micron radius nucleus of a cell. Lander commands the $600 M Broad Institute (by grace of Eli & Edythe Broad). Eric Schadt, a "rebel" who told off one of the biggest of Big Pharma that they'd have to become a "genome informatics company" was dismissed as an outright "rebel" - but now Eric Schadt, in addition to CSO of PacBio, assumed Directorship of the $100 M Mount Sinai Institute of Genomics and Multi-Scale Biology ("multi-scale" rhymes with "scale-free"...). Another mathematician, Isidore Rigoutsos, while enjoying the comfort of IBM Watson Center for almost 2 decades, graced the somewhat unwilling World by his "pyknon"-s (short repetitive segments of DNA snippets that appear throughout genes and "Junk" with frequency far exceeding non-significant repetitions. It took a year to get his work published - yet another "rebel" trampling on the no man's land of "Junk" considered undesirable - but now he has his own Institute to lead progress. While Isidore did not focus his immediate attention to the distribution-curve of pyknon-like elements of a full DNA, in my Cold Spring Harbor Laboratory presentation (2009), upon invitation by George Church I provided evidence that they follow the Zipf-Mandelbrot Fractal Parabolic Distribution Curve (see Figs. 11-14). It may be, that Academic Institutes like the above are needed before Industrialization of Genomics will make Sequencing Industry sustainable by matching Analytics Industry - or history will show that the next quantum-leap is a better solution, turning paradigm-shift science into an IT-led "New Pharma" based on "Genome Computing Business" (the latter already a fact accomplished by SAMSUNG). - This entry can be discussed in the FaceBook page of Andras Pellionisz]
Samsung Launches Genome Analysis Service, Offers Free Genome
Bio-IT World
By Allison Proffitt
August 23, 2011
Samsung SDS is launching beta testing of its new next generation sequencing data analysis service beginning September 1. The Samsung SDS Genome Data Analysis Service will provide analysis services for whole genome sequencing and RNASeq for both Life Technologies and Illumina sequencers. The service will be in beta testing mode from September to November and will be offering free genome analysis (one genome per researcher) during the testing phase.
The service will be Cloud-based, with all analysis being done on the Samsung cloud in Korea. Users can either upload their data, or send a hard drive. “The customer will be actually going to our website and filling out the order form… [to get] their whole genome analyzed and their RNA analyzed,” SungKwon Kim, director of the Bioinformatics Lab, told Bio-IT World. Results are returned to the user online via Samsung’s own genome browser. “User should be able to easily navigate their genomic analysis with our web browser once we finish the genomic analysis.”
The algorithms that Samsung SDS is using for analysis are a combination of open source and vendor provide softwares with Samsung’s own proprietary “tweaks”.
Bioinformatics is a newer area for Samsung, but Kim expects it to be a growth area for the company in the years to come. “We are known for the enterprise-level IT business. When I say enterprise level, we’ve been working with system innovation with banks, high profile, Fortune 500 companies, so when it comes to data security… I don’t think any other vendor companies should be able to match with our capabilities in security, recovery handling, those kinds of things.”
Kim also believes that Samsung will offer faster genome analysis than its competitors or in house options. He expects the final service to take 5 days for whole genome analysis and 3 days for RNA analysis.
Interested parties can sign up for beta test beginning on September 1 on the Samsung Genome website http://www.samsunggenome.com.
[I predicted in 2004 that the formative event, as far as technology and business are concerned, would be when the "tectonic plates" of IT and Genomics will pile up, resulting in the "earthquake" in Industrialization of Genomics. I would never guess that it would be Korea (Samsung) beating the World to the punch, including IBM (Rigoutsos just left...), Intel (pioneering investment into sequencing, but Schadt has just left for most of his activities from Silicon Valley to New York City), Microsoft, Google, Oracle or even Dell or HP not really engaged into Genome Analytics in earnest, and even Sony, Fujitsu or Japan only explored the option but now are beaten in the all-important, indeed crucial "rush to market". One wonders if the world will rush to ship their full genomes to Korea (with the dubious claim that their security beats everyone...), and - as pointed out in my YouTube (2008) Genome IT means not one, but two things. Genome Information Technology is the relatively "easy" part (with Samsung now in the lead), but Genome Information Theory (intrinsic algorithms of recursive genome function) are much harder. HolGenTech, Inc. in Silicon Valley put IP since 2002 into the focus Genome Information Theory - and is now ready to beat the "combination of open source and vendor provide softwares with Samsung’s own proprietary “tweaks”" - This entry can be discussed on the Facebook page of Andras Pellionisz]
Comment by Andras J. Pellionisz to New York Times "Cancer's Secrets coming into Sharper Focus"
The following (unpublished) comment is better suited to this more specialized audience:
It is a pleasure to see in the superb coverage of NYT that both mistaken and very seriously stated axioms are finally discarded; Crick's over half-a-Century "Central Dogma", held from 1956 to his passing in 2004, and Ohno's "So much "Junk" DNA in our genome" erroneous theory published in 1972 and held until his death in 2000. The establishment acknowledged first in 2007 when the NIH-organized ENCODE project revealed that the human DNA is "pervasively transcribed", and now that entirely new conceptual basis is available to mathematically formulate genome regulation in terms of advanced (software-enabling) algorithms of fractal iterative recursion, as well as explaining how aberrant methylation and chromatin modulation of regulatory sequencing leads to uncontrolled (cancerous) growth. The peer-reviewed science paper is The Principle of Recursive Genome Function (2008) and the misregulation by aberrant methylation and chromatin modulation leading to cancer is specifically demonstrated at min. 30:00 of the Google Tech Talk YouTube (2008) and subsequent Cold Spring Harbor Presentation invited by George Church (2009). Indeed, present NIH Head Dr. Collins called upon conclusion of ENCODE in 2007 that "the community of scientists have to re-think long-held beliefs". A lucky few did not have to "re-think" old dogmas, since they never believed in them in the first place, for reasons of cross-disciplinary domain expertise of informatics-essentials of biophysics - but publication of formerly "lucid heresy" (on both counts, since iterative fractal recursion "violated" both the Central Dogma and Junk DNA rules of the establishment) was only possible floated in the self-edited Cambridge University Press book-chapter Neural Geometry: Towards a Fractal Model of Neurons (1989). The NIH Grant Proposal -duly acknowledged in the book chapter- was rejected, and an ongoing NIH Grant was discontinued. Nearly ¼ Century later, since “our concepts of genome regulation are frighteningly unsophisticated”, the immediately utility of advanced algorithmic (software enabling) approaches is now palpable. This post can be discussed in the FaceBook page of Andras Pellionisz.
Everything Scientists Thought They Knew About Cancer Might Be Totally Wrong
A 2000 study called The Hallmarks of Cancer is the most-referenced paper in the journal Cell, one of the most influential journals in the world. Turns out that paper might be wrong.
And that might partly explain why cancer death rates are falling slowly.
The theory, which is illustrated in the video above, basically says that sometimes cells lose their ability to regulate growth and go crazy, creating cancer tumors. The New York Times reports that at the recent annual meeting of the American Association for Cancer Research in Orlando, Florida, scientists had a whole lot of other theories up their sleeves.
One states that microbes, which include tiny creatures like bacteria and make up 90 percent of the cells in our body, sometimes turn against us to cause cancer.
Another theory is that what scientists used to think was "junk DNA" and makes up 98 percent of our DNA (only two percent is the kind that actually instructs genes) is not junk at all but a mechanism for causing cancer, among other things.
Lastly, micoRNAs might be the culprit. Until recently scientists thought they didn't do much, but now they're starting to think that microRNAs might intercept or block messages from DNA to messenger RNA.
If all of that seems crazy-complicated, check out this pretty awesome video the New York Times created for you.
Of course, cancer ain't talkin' so these theories might be totally wrong too. But one thing's for certain: smoking is still bad. Damn.
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Cancer’s secrets are coming into sharper focus
By George Johnson / New York Times News Service
Published: August 16. 2011 4:00AM PST
For the last decade cancer research has been guided by a common vision of how a single cell, outcompeting its neighbors, evolves into a malignant tumor. Now recent discoveries are providing new details. Cancer appears to be even more willful and calculating than previously imagined.
Through a series of random mutations, genes that encourage cellular division are pushed into overdrive, while genes that normally send growth-restraining signals are taken offline.
With the accelerator floored and the brake lines cut, the cell and its progeny are free to rapidly multiply.
More mutations accumulate, allowing the cancer cells to elude other safeguards and to invade neighboring tissue and metastasize.
These basic principles — laid out 11 years ago in a landmark paper, “The Hallmarks of Cancer,” by Douglas Hanahan and Robert Weinberg, and revisited in a follow-up article this year — still serve as the reigning paradigm, a kind of Big Bang theory for the field.
But recent discoveries have been complicating the picture with tangles of new detail.
Most DNA, for example, was long considered junk — a netherworld of detritus that had no important role in cancer or anything else. Only about 2 percent of the human genome carries the code for making enzymes and other proteins, the cogs and scaffolding of the machinery that a cancer cell turns to its own devices.
These days “junk” DNA is referred to more respectfully as “noncoding” DNA, and researchers are finding clues that “pseudogenes” lurking within this dark region may play a role in cancer.
“We’ve been obsessively focusing our attention on 2 percent of the genome,” said Dr. Pier Paolo Pandolfi, a professor of medicine and pathology at Harvard Medical School. This spring, at the annual meeting of the American Association for Cancer Research in Orlando, Fla., he described a new “biological dimension” in which signals coming from both regions of the genome participate in the delicate balance between normal cellular behavior and malignancy.
As they look beyond the genome, cancer researchers are also awakening to the fact that some 90 percent of the protein-encoding cells in our body are microbes. We evolved with them in a symbiotic relationship, which raises the question of just who is occupying whom.
“We are massively outnumbered,” said Jeremy Nicholson, chairman of biological chemistry and head of the department of surgery and cancer at Imperial College London. Altogether, he said, 99 percent of the functional genes in the body are microbial.
In Orlando, he and other researchers described how genes in this microbiome — exchanging messages with genes inside human cells — may be involved with cancers of the colon, stomach, esophagus and other organs.
These shifts in perspective, occurring throughout cellular biology, can seem as dizzying as what happened in cosmology with the discovery that dark matter and dark energy make up most of the universe: Background suddenly becomes foreground and issues once thought settled are up in the air. In cosmology the Big Bang theory emerged from the confusion in a stronger but more convoluted form. The same may be happening with the science of cancer.
Exotic players
According to the central dogma of molecular biology, information encoded in the DNA of the genome is copied by messenger RNA and then carried to subcellular structures called ribosomes, where the instructions are used to assemble proteins. Lurking behind the scenes, snippets called microRNAs once seemed like little more than molecular noise. But they have been appearing with increasing prominence in theories about cancer.
By binding to a gene’s messenger RNA, microRNA can prevent the instructions from reaching their target — essentially silencing the gene — and may also modulate the signal in other ways. One presentation after another at the Orlando meeting explored how microRNAs are involved in the fine-tuning that distinguishes a healthy cell from a malignant one.
Ratcheting the complexity a notch higher, Pandolfi, the Harvard Medical School researcher, laid out an elaborate theory involving microRNAs and pseudogenes. For every pseudogene there is a regular, protein-encoding gene. (Both are believed to be derived from a common ancestral gene, the pseudogene shunted aside in the evolutionary past when it became dysfunctional.) While normal genes express their will by sending signals of messenger RNA, the damaged pseudogenes either are mute or speak in gibberish.
Or so it was generally believed. Little is wasted by evolution, and Pandolfi hypothesizes that RNA signals from both genes and pseudogenes interact through a language involving microRNAs. (These signals are called ceRNAs, pronounced “sernas,” meaning “competing endogenous RNAs.”)
His lab at Beth Israel Deaconess Medical Center in Boston is studying how this arcane back channel is used by genes called PTEN and KRAS, commonly implicated in cancer, to confer with their pseudotwins. The hypothesis is laid out in more detail this month in an essay in the journal Cell.
In their original “hallmarks” paper — the most cited in the history of Cell — Hanahan and Weinberg gathered a bonanza of emerging research and synthesized it into six characteristics. All of them, they proposed, are shared by most and maybe all human cancers. They went on to predict that in 20 years the circuitry of a cancer cell would be mapped and understood as thoroughly as the transistors on a computer chip, making cancer biology more like chemistry or physics — sciences governed by precise, predictable rules.
Now there appear to be transistors inside the transistors. “I still think that the wiring diagram, or at least its outlines, may be laid out within a decade,” Weinberg said in an email. “MicroRNAs may be more like minitransistors or amplifiers, but however one depicts them, they still must be soldered into the circuit in one way or another.”
In their follow-up paper, “Hallmarks of Cancer: The Next Generation,” he and Hanahan cited two “emerging hallmarks” that future research may show to be crucial to malignancy — the ability of an aberrant cell to reprogram its metabolism to feed its wildfire growth and to evade destruction by the immune system.
Unwitting allies
Even if all the lines and boxes for the schematic of the cancer cell can be sketched in, huge complications will remain. Research is increasingly focused on the fact that a tumor is not a homogeneous mass of cancer cells. It also contains healthy cells that have been conscripted into the cause.
Cells called fibroblasts collaborate by secreting proteins the tumor needs to build its supportive scaffolding and expand into surrounding tissues. Immune system cells, maneuvered into behaving as if they were healing a wound, emit growth factors that embolden the tumor and stimulate angiogenesis, the generation of new blood vessels. Endothelial cells, which form the lining of the circulatory system, are also enlisted in the construction of the tumor’s own blood supply.
All these processes are so tightly intertwined that it is difficult to tell where one leaves off and another begins. With so much internal machinery, malignant tumors are now being compared to renegade organs sprouting inside the body.
As the various cells are colluding, they may also be trading information with cells in another realm — the micro-organisms in the mouth, skin, respiratory system, urogenital tract, stomach and digestive system. Each microbe has its own set of genes, which can interact with those in the human body by exchanging molecular signals.
“The signaling these microbes do is dramatically complex,” Nicholson said in an interview at Imperial College. “They send metabolic signals to each other — and they are sending chemicals out constantly that are stimulating our biological processes.
“It’s astonishing, really. There they are, sitting around and doing stuff, and most of it we don’t really know or understand.”
People in different geographical locales can harbor different microbial ecosystems. Last year scientists reported evidence that the Japanese microbiome has acquired a gene for a seaweed-digesting enzyme from a marine bacteria. The gene, not found in the guts of North Americans, may aid in the digestion of sushi wrappers. The idea that people in different regions of the world have co-evolved with different microbial ecosystems may be a factor — along with diet, lifestyle and other environmental agents — in explaining why they are often subject to different cancers.
The composition of the microbiome changes not only geographically but also over time. With improved hygiene, dietary changes and the rising use of antibiotics, levels of the microbe Helicobacter pylori in the human gut have been decreasing in developing countries, and so has stomach cancer. At the same time, however, esophageal cancer has been increasing, leading to speculation that H. pylori provides some kind of protective effect.
At the Orlando meeting, Dr. Zhiheng Pei of New York University suggested that the situation is more complex. Two different types of microbial ecosystems have been identified in the human esophagus. Pei’s lab has found that people with an inflamed esophagus or with a precancerous condition called Barrett’s esophagus are more likely to harbor what he called the Type II microbiome.
“At present, it is unclear whether the Type II microbiome causes esophageal diseases or gastro-esophageal reflux changes the microbiome from Type I to II,” Pei wrote in an email. “Either way, chronic exposure of the esophagus to an abnormal microbiome could be an essential step in esophageal damage and, ultimately, cancer.”
Unseen enemies
At a session in Orlando on the future of cancer research, Dr. Harold Varmus, the director of the National Cancer Institute, described the Provocative Questions initiative, a new effort to seek out mysteries and paradoxes that may be vulnerable to solution.
“In our rush to do the things that are really obvious to do, we’re forgetting to pay attention to many unexplained phenomena,” he said.
Why, for example, does the Epstein-Barr virus cause different cancers in different populations? Why do patients with certain neurological diseases like Parkinson’s, Huntington’s, Alzheimer’s and Fragile X seem to be at a lower risk for most cancers? Why are some tissues more prone than others to developing tumors? Why do some mutations evoke cancerous effects in one type of cell but not in others?
With so many phenomena in search of a biological explanation, “Hallmarks of Cancer: The Next Generation” may conceivably be followed by a second sequel — with twists as unexpected as those in the old “Star Trek” shows. The enemy inside us is every bit as formidable as imagined invaders from beyond. Learning to outwit it is leading science deep into the universe of the living cell.
[The Cancer Establishment "threw in the towel" - giving up on the old axiom of looking for "cancer gene". Now they bank on more sophisticated approaches, where the two mistaken axioms ("Junk DNA" and "Central Dogma") are given up. Good time for the "Jim Clark"-s of Postmodern Genomics to jump on the fractal bandwagon of HolGenTech, Inc.! - This post can be discussed on the FaceBook page of Andras Pellionisz]
Author(s): Dinsa Sachan
Issue: Aug 15, 2011
Down to Earth (India)
Personal genomics makes individualised healthcare possible; but experts remain wary.
SOUMYA Swaminathan’s 16-year-old son, Sudarshan, loves sports, and is training to be a footballer. Two years ago, he was confused which sport to train in. “He would come home and say he wants to play cricket. Next week it would be football,” says Swaminathan. Then she heard about the sports DNA test offered by Super Religare Laboratories (SRL). The Mumbai-based firm had just launched the test, which would examine DNA from your saliva sample for variants of a gene linked with sporting prowess. It turned out that Sudarshan had a genetic predilection for power sports. “So we decided to focus on football and basketball,” she adds.
Gene testing is no longer restricted to paternity testing and DNA fingerprinting for criminal cases. It has varied avenues from giving details about what diseases you are likely to contract to which sports are suited for you. Experts say the personal genomics is the future of science. All you have to do is give saliva or blood sample and within days a comprehensive feedback on your health, which includes what diseases you are more or less likely to develop over your lifetime, is handed over to you.
“Personalised medicine means different things to different people. Some see it as targeted genomics where changes in specific genes predict responses to specific therapies,” says John Tomaszewski, president, American Society for Clinical Pathology. Others might see it as cellular engineering where one’s own cells are removed, re-engineered to treat a specific disease, and then re-infused into the patient, he adds. Both of these strategies are in limited use today, but hold hope for individualised healthcare.
Beginning and future
The seeds of personalised genomics were first sown in 1990 when the Human Genome Project (HGP) was conceived by a team of scientists in the US. The project, which lasted 13 years, identified and mapped the entire human genome—approximately 30,000 genes.
The Personal Genome Project (PGP), pioneered by George Church, one of HGP’s founders, is the next step. HGP mapped the genome of an anonymous person, while the PGP, when completed, will have genetic information of several individuals as well as their phenotypic information. Through this researchers can study the connections between gene functions and physical traits. The project is in process gathering genotypical information for 100,000 participants.
Rush to capitalise on science
Besides the research-based projects like PGP, the more visible side of the personal genomics industry are commercial enterprises like 23andme, Navigenics and Knome that offer different genetic tests to people. Companies like the California-based 23andme, which was floated in 2006, take a saliva sample of the customer and predict susceptibility to 199 genomic conditions including cancer and diabetes. It also has a research division, 23andWe, which published its first paper in June 2011 pinpointing genetic origins of the Parkinson’s disease.
23andme, along with ventures like Navigenics, belongs to the direct-to-consumer bracket that rely on SNP (single nucleotide polymorphism) genotyping. SNPs are particular point-locations in human DNA showing errors. Some SNP’s influence traits like physical appearance and susceptibility to diseases.
23andme sequences only a portion of one’s genome. Other players like Knome and Illumina, whose technology is used by a number of projects, offer whole genome sequencing services, which list every minute detail of your genetic fingerprint. Knome’s DNA analytics service is priced at US $4,998. When it was launched, 23andme charged US $999 for its sequencing service. The cost was slashed to US $399 two years later. Now, the company offers a package for USD $99, which includes a year’s mandatory subscription for US $9 per month in which participants are informed about any new updates from their single saliva sample.
“The price to sequence bases has fallen a million times from when HGP started with three billion ,” says Richard Resnick, CEO of GenomeQuest, a genome sequence management company. He adds that the cost will drop to as low as US $1,000 till next year.
Where India stands
Although companies like 23andme have not yet arrived in India, the country has been making strides in the field of personal genomics. Institute of Genomics and Integrative Biology (IGIB) in New Delhi mapped the first human genome in 2009. “Though India started slow in the field we are catching up,” says Rajesh Gokhale, Director, IGIB.
On the commercial side, only SRL [Super Religare Laboratories] has set its shop in the country. It takes a cheek swab and tests it for variations within the gene (ACTN3) associated with athletic performance in numerous studies. SRL tests children for two variants of the gene. According to B R Das, research and development chief at SRL, individual having the R variant of the gene have a possibility of excelling in sports that require short, powerful bursts of energy. The other variant, X, may be more useful in endurance sports like cricket. The test has been taken by 3,000 people across India. It costs Rs 2,000.
Binay Panda, head of Ganit Labs, Institute of Bioinformatics and Biotechnology in Bengaluru, believes India should develop its genomic potential in the direction which can benefit population at large. He sees a potential in medicine. His lab is trying to pin down the genetic causes of cancer. “This can help us devise specific diagnostic and prognostic techniques that would detect cancer at an early stage,” he adds.
In doubt
But experts remain critical about the disease assessments made by companies. “As far as sequencing some parts of genome and making predictions is concerned, we do know a fair amount about specific diseases and mutations associated with them,” says Kevin Rosenblatt, director, Center for Clinical Proteomics at The University of Texas Medical School at Houston. But when we consider the whole genome and the vast number of mutations that each of us carries in our DNA, science is not that well developed, he adds.
Gil Atzmon, assistant professor of genetics and medicine, Albert Einstein College of Medicine in the US, says, “Risk is not necessarily illness, and the probability of getting a disease is dependent on many factors, especially when epigenetics—response of gen ome to stress, diet, toxins and much more—is introduced to the equation.” He adds that two people can have the same genetic blueprint but are vulnerable to different diseases based on the environment they interact with. Andras Pellionisz, head of HolGenTech Inc, which makes genome analysing software, believes analysing genomes presents various challenges which current technology is not adequately prepared to meet. “The biggest challenge is interpretation. Genome is a changing repository of information because mutations and actual sequence alterations happen throughout life.”
Indian researchers are also wary of such endeavors. “The test by SRL measures one factor of energy metabolism. Will you become a sportsperson or not depends on other factors like environment also,” says Gokhale. Panda dubs the tests as “recreational genomics”.
Ethical and legal issues
When genomics companies were sprouting, the debate revolved aro und ethical issues and these still continue to haunt it. Critics question the repercussions of telling people what diseases they are vulnerable to. There are also concerns that health insurance companies might exploit the information and not insure one based on the probability of having a specific disease. Though US has a law— The Genetic Information Nondiscrimination Act—that protects US citizens from “genetic discrimination” no other country has such a legislation.
[I made a technical comment on "biological information" to the Article (click on the headline-link to view). Additionally, I attribute the biggest significance to this news that it amounts to yet another entry to the "DTC becames global" trend, with Mumbai-based Super Religare Laboratories. This comment can be discussed at the FaceBook page of Andras Pellionisz]
Researchers uncover a new method of checking for skin cancer
01 July 2011 @ 11:41 am EDT
Medical Daily
[Skin Cancer? There is an app for it ... AJP]
Skin Scan, a Romanian startup, claims it has found a way to measure the amount of risk a mole represents using a proprietary algorithm combined with an iPhone image of the mole which it says can measure the likelihood of it representing skin cancer.
The app raises awareness of a particularly aggressive disease which kills when people aren't aware of the serious nature of a mole that might have developed from over-exposure to harmful sunlight.
"It is accepted that human tissues have a fractal-like structure." says P.hD Mircea Olteanu, brains behind the app, "Consequently, during the last decades scientists tried to classify different types of tumors by computing their fractal dimension and numerical characteristics... Skin Scan is a skin cancer prevention tool which tells users when to look for a professional medical investigation."
Commonly found in Caucassions 1:80, the disease is largely preventable by precautionary measures and Skin Scan hopes to take that a step forward by getting people to check for their mole's more regularly - with an iPhone.
The app measures the mole's size and uses various characteristics, such as it's fractal nature - smoothness, colour and other irregularies to keep an eye on more serious ones.
If the app detects a change in the mole, it sends them to the doctor.
"Skin Scan is a skin cancer prevention tool which tells users when to look for a professional medical investigation...Our team encourages the use of this modern technology that alerts users to seek medical help in time." continued Mircea Olteanu.
The team is run by Mihai Mafteianu of Cronian Labs in Romania. It’s headed up by CEO Victor Anastasiu, co-founder of Romanian seed fund SeedMoney.
The team secured $50,000 dollars of startup and costs $6.50 on the iPhone app store.
Download the Skin Scan app: http://itunes.apple.com/us/app/skin-scan/id434196122?mt=8&ls=1
Access the company's website: http://www.skinscanapp.com/index_3
[Both the utility of mobile devices and fractality of tissues (as well as fractality of the genome has been pioneered since 2002, see FractoGene and Barcode Shopping App YouTube). Fractals are eminently recursive algorithms, however, and thus FractoGene used to be a "lucid heresy" violating Crick's "Central Dogma" (while Crick was still alive, forbidding protein-to-DNA recursion) as well as Ohno's "Junk DNA" dogma (that Ohno actually meant seriously in 1972 such that any recursion to the "Junk" would be pointless - his 4.5 page meeting abstract deteriorated into a ludicrious misnomer now "seriously" upheld only by an occasional lonely moron, who is unable to produce a properly rounded percentage number, let alone understand advanced mathematics of postmodern genome informatics). Obsolete dogmas had to be demolished once Crick was gone, and the ENCODE results were published (2007), clearing the path towards The Principle of Recursive Genome Function (see peer-reviewed science paper and popularization by Google Tech Talk YouTube, both in 2008). The YouTube shows at min. 31 how aberrant methylation of perused auxiliary information (from the "Junk") results in uncontrolled fractal growth - the genome misregulation diseases a.k.a. cancer. Today, there is a veritable groundswell of observations, papers, and "the tip of the iceberg", an already deployed $6.50 tool as an app to help prevent cancer - based on the realization that the "double lucid heresy" was crazy, and actually was crazy enough to be true! (Paraphrasing Niels Bohr's theory, when the Copenhagen Group heard Bohr's thesis, and concluded "We all agree that your theory is crazy - the only question we have if it is crazy enough to be true"). HolGenTech, Inc. now focuses on cancer where the fractal genome, because of its "fractal defects" violates the genome's own mathematical rules, and thus genome misregulation results in clearly fractal, uncontrolled cancerous growth. In addition to several papers covered in this column, below is a list of recent observations and findings substantiating the case:
http://www.ncbi.nlm.nih.gov/pubmed/21319994
http://www.ncbi.nlm.nih.gov/pubmed/21514387
http://lambda.qsensei.com/content/1pmhrw
http://www.biomedcentral.com/1471-2407/10/260
http://www.cancertherapyblog.com/cancer-news/fractal-dimension-analysis-helps-breast-cancer-prognosis/
This entry can be discussed on the FaceBook page of Andras Pellionisz]
How accurate is the new Ion Torrent genome, really?
Genetic Future
By Daniel MacArthur July 21, 2011
New sequencing technology Ion Torrent has made a splash with a paper in today’s issue of Nature. There’s no question the high-impact publication is a massive boost for the young platform, now nestled within the embrace of the giant Life Technologies (who acquired the startup for a surprisingly large price last August) and bracing for the impending launch of its most serious competitor, Illumina’s MiSeq.
The paper jumps the new platform through the standard hoops: some basic kicking-the-wheels, a test run on three bacterial genomes (Vibrio fisheri, Escherichia coli, and Rhodopseudomanas palustris), and then the traditional main event: the sequencing of a complete human genome. The genome in question is that of Intel co-founder Gordon Moore, the eponymous originator of Moore’s Law. There’s some pleasing symmetry here: Moore’s Law is frequently cited in the context of the massive decline in the costs of DNA sequencing; in addition, the Ion Torrent technology is based on the same kind of semiconductor technology pioneered by Moore. Refreshingly, the paper refers to Moore by name, which is a pleasant change from the rather affected pseudo-anonymity of other published genomes (e.g. Patient Zero).
Anyway I’m not going to comment at all here on the technical and bacterial work, which I have no doubt will be covered in detail by my esteemed colleagues Keith Robison and Nick Loman. My main interest in this paper is what it tells us about the ability of Ion Torrent as a potential platform for large-scale sequencing of human genomes, and a rival to current sequencing market leader Illumina. I also want to spend some time berating the authors of the paper for a thoroughly misleading piece of statistical sleight-of-hand that makes their accuracy numbers sound far better than they actually are.
What did they do?
The company sequenced Moore’s genome using their technology to an average coverage of 10.6x. This just means that on average each base in the genome was covered by 10.6 separate Ion Torrent reads, albeit with substantial variation: some bases had lots more reads, and some had fewer. You can see the distribution of read counts per base (in red), compared with the ideal distribution (a Poisson distribution, in green) in Figure 4b of the paper – I’ve copied a thumbnail to the right. It’s clear that there are plenty of positions in the genome with substantially less than 10 reads.
Let’s be very clear about this up front: by modern standards, this is a poor-quality genome. An average coverage of 10x means that most positions in the genome will be covered by at least one read – 99.21%, in this case – but in many of those locations, the number of reads will be too low to have any chance of accurately calling a heterozygous SNP (a base change where both different versions are present, one on the maternal and one on the paternal chromosome). This isn’t a function of the raw data quality – it’s simply a statistical consequence of sampling error at small sample sizes, that can only be overcome by additional sequencing.
It’s also an extremely expensive genome: even at this low coverage the sequencing burned through around 1,000 Ion Torrent chips, and in an NY Times piece yesterday sequencing guru George Church estimated the total cost of this project at around $2 million. That would be substantially lower at today’s prices, but still north of $200,000 for a poor-quality genome compared to less than $5,000 for a high-quality sequence from Complete Genomics. The yield of the Ion platform (in terms of bases per dollar) is of course going up rapidly, but I think it’s important to emphasise that Ion Torrent is not yet a remotely competitive technology for affordable whole human genome sequencing.
So how accurate is the genome sequence, really?
The authors attempted to explicitly estimate their error rate by sequencing Moore’s genome a second time using an independent technology: in this case, Life Technologies’ SOLiD platform, to a total coverage of around 15x. (The higher depth of the SOLiD sequencing understates the far higher yield from that platform compared to Ion Torrent; for this paper the authors ran over 1,000 chips on the Ion Torrent, whereas the SOLiD coverage was presumably achieved in a single run.) 15x coverage isn’t much better than 10x, so the SOLiD sequence would be expected to be missing plenty of heterozygous sites as well.
So, the authors have two separate low-coverage genomes, both of which would be expected to be missing plenty of SNPs – that means we would expect to see plenty of sites that differ between the two sequences (reflecting changes that by chance were detected by one platform but missed by the other). Yet the paper appears to cite a “validation rate” for the SNPs called by the Ion Torrent that is implausibly high:
To confirm the accuracy of our analysis, we also sequenced the G. Moore genome using ABI SOLiD Sequencing43 to 15-fold coverage and validated 99.95% of the heterozygous and 99.97% of the homozygous genotypes (Supplementary Tables 1 and 2). [my emphasis]
There’s absolutely no conceivable way that a comparison between a 10x genome sequence and a 15x genome sequence could possibly result in a “validation rate” of 99.95% for heterozygous sites, at least not for any reasonable definition of the term “validation rate”. It takes some digging in the supplementary data to figure out what’s going on here. This is the definition of the term in the legend of Table S2, where the metric is referred to as the “percent same genotype”:
In cases where both datasets call the same type of SNP (heterozygote or homozygous variant) the proportion for which the genotype call is the same
The only way I can parse that sensibly is as follows: for sites that are called as heterozygous in both the Ion Torrent and SOLiD data, the “validation rate” is the proportion where the same two alleles are present. In other words, non-validated sites would only be sites where both platforms called a heterozyous SNP, but one platform said it was an A/G SNP while the other said it was an A/C SNP.
This is a near-useless metric, and does not correspond to any meaningful definition of the term “validation rate”. It gives us no information about what we actually want to know about, the proportion of sites where a SNP is called by one platform but not by the other – those are simply excluded from the comparison entirely. This is simply a measure of the platform’s ability to call the correct non-reference base at sites that are genuinely polymorphic, something that would be extremely high for virtually any half-decent sequencing technology. The only useful thing this metric does is provide a percentage with lots of convincing nines in it, which I’m sure the investors love, but I’m seriously perplexed that it managed to sneak past the manuscript reviewers.
Let’s take a more sensible definition of the term “validated”: for instance, let’s say it’s the proportion of sites called as heterozygous by Ion Torrent that also show some evidence of variation in SOLiD (we’ll generously say that the variant can be either homozygous or heterozygous in the SOLiD calls). Using this more plausible definition, the validation rate for Ion Torrent SNPs is just 88.0% at homozygous sites and 84.4% at heterozygous sites.
Ion Torrent could no doubt argue that this calculation is unfair to them: in many (probably most) cases, a discrepancy between Ion Torrent and SOLiD will be due to SNPs that were missed by the SOLiD technology, and thus aren’t really errors made by Ion Torrent. This is absolutely true, and in response I say: so do a proper job of validating your variants. Being a part of Life Technologies, one might imagine, should give the chaps at Ion Torrent a decent amount of access to SOLiD machines, and one more run of Moore’s genome on a SOLiD 4 would have given a far cleaner genome sequence for comparison. LIFE might even have one or two of those old capillary sequencing machines around that they used to sell: just 100-200 targeted capillary reactions around sites discrepant between the Ion Torrent sequence and a high-quality SOLiD sequence would have given plenty of data for an accurate estimation of the platforms real false positive and false negative rates.
Lack of proper validation is even more of an issue for larger structural variants. Here the authors steer clear of attempting to discover new variants, focusing instead on figuring out whether Moore carries any of the known structural variants called by the 1000 Genomes pilot project (PDF). Of 7,565 large deletions and inversions found by 1000 Genomes, the authors find evidence for 3,413 of them in Moore’s genome. That seems like a surprisingly large proportion to me, and it’s unclear how many of these calls are real: the authors report the results of a simulation using random genomic regions to estimate that 99.94% of their called events are real, but this number is not particularly meaningful as true deletion breakpoints are not well-represented by random chunks of the genome. And here there is absolutely no experimental evidence brought to bear – for instance, as far as I can tell no attempt was made to see how many of these apparent deletions also showed support in the SOLiD data, and certainly no attempt to independently validate the variants using a simple PCR assay.
All in all, a disappointing showing. This clearly isn’t a great genome sequence – it simply can’t be at 10x coverage, no matter how good the raw accuracy is – but the authors haven’t done enough experimental work to get a good sense of how accurate it really is. That means there’s very little we can say about the utility of Ion Torrent for whole human genome sequencing, apart from the fact that it’s currently too expensive to be practical.
What does Moore’s genome tell us about him?
Not much. The authors make a fairly cursory attempt at genome interpretation, pulling annotations from 23andMe’s database and OMIM, but their results aren’t particularly useful. That’s not a criticism, by the way: the point of this paper was demonstrating a sequencing technology, not a functional annotation pipeline. (Incidentally, 23andMe’s database was apparently used without any formal collaboration with the company, suggesting the researchers simply scraped the information from the company’s website: it’s intriguing to see one of the companies attacked by the FDA and Congress as “snake oil” being used as the go-to source for functional annotation.)
However, I note that the indefatigable Mike Cariaso has already run Moore’s genome through his interpretation pipeline Promethease – you can get the results here. It appears Moore has an increased risk of baldness (check), altered responses to various drugs, and a potentially highly elevated risk of age-related macular degeneration. However, nothing that he couldn’t have learnt from a 23andMe test, at less than 0.1% of the cost.
Where to next for Ion Torrent genomes?
This has been a pretty negative post, because I’ve focused solely on a section of the paper that – I’ll be frank – was done pretty badly. It’s not intended to be a critique of the Ion Torrent technology as a whole, and I’ll leave an evaluation of the technical merits of the platform to others who know it far better than I.
Still, I can’t help but wonder if Torrent made a mistake in including a human genome in this paper at all. I mean, I know it’s traditional, and sequencing Moore makes for some easy headlines, but the Torrent platform simply isn’t currently suited to whole-genome sequencing and won’t be until its yield improves substantially (there are clear signs in the paper that this is happening, albeit perhaps a little slower than we were promised). In sequencing a human genome with this early-stage, low-yield technology, Ion Torrent was forced into a dilemma of its own making: either spend an obscene amount of money to generate a high-quality sequence, or spend a simply lewd amount of cash to generate a crappy sequence. In the end they opted for the second approach, and I suspect they would have been better off simply leaving Moore’s genome out of the paper entirely.
In any case, I should emphasise that given the slow pace of publishing, this is a genome that was put together using the technology of maybe 12 months ago. There’s no question that Torrent technology has been improving over that time, and while it’s still not at the stage of competing with Illumina on cost right now, it’s certainly possible that this will be more viable in 12 months’ time. Hopefully the next genome sequence published using this technology comes complete with sufficient validation data to get a real impression of its quality.
[With TWO Former Intel Presidents (legendary Andy Grove and "Moore's Law" Gordon Moore) personally involved, with their own genomes, in "Genome Informatics" one may assume there is much talk at Intel about the future. As for the "present perfect", the Battelle Study (avilable on the web free full text) already assessed the economic impact of The Human Genome Project at $796 Bn in the USA alone - with Intel taking a vital part in the investments into quick and affordable full DNA sequencing by Pacific Biosciences since July 14, 2008 (first $100 M - a formative event in the history of "Genomics becoming Informatics"). It is unclear from the coverage from this article and the one below, why Gordon Moore got fully sequenced (once by Ion Torrents new platform, another time by the well-established SOLiD platform of the same company (Life Technologies) - and why is it that Andy Grove is not on public record of having been fully sequenced by any platform (thus missing from the approximately 2,700 individual humans whose DNA has been fully sequenced to date). From the viewpoint of Intel as a company and lead investor, it certainly makes a lot of sense to cross-compare the existing and emerging "full DNA sequencer technologies" for e.g. accuracy, costs, speed - and e.g. clinical applications (for instance with PacBio's capability of calling not only nucleotides, but also the methylation-status). It follows, that we might expect e.g. IT celebs with genomic conditions (cancer, Parkinsons', Alzheimers' - already a list of about 30 serious hereditary conditions that remained unpublished by Newsweek) - if I were Steve Jobs or Andy Grove could probably afford and be extremely keen on including a "Full DNA Sequencing" in my "Annual Physical" - but presently costing about $5k and needing only a blood sample, I would absolutely kindly but firmly demand a "before and after" full DNA sequencing for any therapy (say, chemo) with a propensity or requirement that it should do something about my diseased genome (at the very least monitoring its methylation-pattern, if full DNA sequencing would be deemed too costly, by the inexpensive Illumina methylation-interrogation by their microarrays). If I were a former leader of a major IT company with my opinion heavy as gold, I would probably not advise to have some other country to grab, beyond a point of no return, the software development of modern genome informatics (ask me why). This entry can be discussed in the FaceBook page of Andras Pellionisz]
[Former Intel President] Grove Backs an Engineer’s Approach to Medicine
(see also similar coverage by Kristen Bole)
May 17, 2010, 4:52 PM
New York Times
By ANDREW POLLACK
Andrew S. Grove, the former chief executive of Intel, is taking the next step in his quest to infuse the engineering discipline of Silicon Valley into the development of new medical treatments.
Mr. Grove has pledged $1.5 million so that the University of California campuses in San Francisco and Berkeley can start a joint master’s degree program aimed at so-called translational medicine — the process of turning biological discoveries into drugs and medical devices that can help patients.
The idea is to expose students to both the engineering prowess of Berkeley and the medical research of San Francisco to train a new breed of medical innovator.
“What we have learned from decades of rapid development of information technology is that the key is relentless focus on ‘better, faster, cheaper’ — in everything,’’ Mr. Grove said in a statement. “The best results are achieved through the cooperative efforts of different disciplines, all aimed at the same objective.”
Mr. Grove first broached the idea of the joint-campus program in November.
Mr. Grove’s views reflect in part his personal frustrations with waiting for better treatments for prostate cancer, which he had, and Parkinson’s disease, which he has now.
“I have my own decade-plus experience with a number of diseases that have dozens of ways of curing mice if mice have them and don’t progress toward clinical implementation,’’ Mr. Grove said in an interview.
Translational medicine is indeed a big buzzword these days. Everyone seems to recognize that there is a gap in getting from “bench to bedside.’’ Various programs are being set up to try to speed the process. Interdisciplinary work is another buzzword and trend among medical researchers.
Clearly, medical innovation can stand to be improved. Spending by big pharmaceutical companies on research and development has roughly doubled in the last decade, without any increase in the number of new drugs getting to market.
Yet whether Mr. Grove has the right prescription is open to debate. Some medical researchers have ridiculed his criticisms of their work, saying it is simply not possible to apply the techniques of the electronics business to the far greater complexity of human biology. ["Is Andy Grove a Kook?" - some old schooler biochemists who can't even produce properly rounded percentages, let alone come near to understanding a (now old) Itanium CPU, consisting of 2 Billion transistors, would probably put Andy Grove into the same category of ridicule as they did Barbara McClintock - to get her Nobel four decades delayed - AJP]
“Mr. Grove, you can print out the technical specs for your chips,’’ Derek Lowe, a pharmaceutical industry chemist, wrote in 2007. “We don’t have them for cells.’’
--
Comment (Pellionisz)
Harvard Medical School medicine was successfully united with MIT engineering to have created the Broad Institute. With $600 M charitable contribution from Eli and Edythe Broad – since a hereditary and “incurable” (!) syndrome, appeared in the family - a single frustrated venture philantrophist family provided the wherewithal to put Informatics and Genomics together. Broad is under directorship of Dr. Lander, with first degree in mathematics, now a Science Advisor to the President. Lander et al. 2009 Science cover article amounted to the outry “Mr. President, the Genome is Fractal!”. Andy Grove certainly is one the Worlds best experts how to wrestle the 500 pound gorilla of Informatics – but seems to be frustrated by old school medicine to handle his serious hereditary syndromes (prostate cancer and Parkinsons’). Mr. Grove is forcefully vocal both about his conditions and even more so about the need and extreme timeliness to seek entirely new avenues. Now the Battelle Study (assessing that the Human Genome Project had a $796 Bn impact on the US economy alone) rests on the pillar that “Genomics turned into Informatics”. It would take just minutes for Andy Grove to verify that the genome is a code of Informatics how to (mis)regulate genome function of a massively parallel 2-bit dataflow-“machine” – totally impossible without massive deployment of the best defense-computers. Assembling full human DNA has proven this thesis, BTW a trivial note for Silicon Valley movers and shakers, over a decade ago. More in my Google Tech Talk YouTube under “Pellionisz”.
Loophole found in genetic traffic laws [Death Certificate of Crick Central Dogma - AJP]
Altered molecule causes protein-making machinery to run stop signs
By Tina Hesman Saey July 16th, 2011; Vol.180 #2 (p. 8)
Science News
Biology’s rules may be full of exceptions, but a new discovery has uncovered a violation in a rule so fundamental that geneticists call it the central dogma.
The molecular equivalent of writing one RNA letter in a different font can change the way a cell’s protein-building machinery interprets the genetic code, Yitao Yu and John Karijolich of the University of Rochester in New York report in the June 16 Nature. They found that occasional conversions of a genetic letter found in RNA into a slightly different form can cause a cell’s protein-building machinery to roll right through a stop sign.
That might seem like a run-of-the-mill molecular traffic violation, but it results in an entirely different protein than the one encoded by DNA — a clear violation of the central dogma.
The central dogma holds that DNA is the repository for all genetic instructions in a cell. The tenet declares that those instructions are carefully transcribed into multiple messenger RNA, or mRNA, copies, which are then read in three-letter chunks called codons by cellular machinery called ribosomes. Ribosomes then convert the mRNA instructions into proteins.
Yu and Karijolich studied pseudouridine, a slightly different version of the RNA component uridine. The enzymes that copy DNA to RNA and vice versa can’t tell the difference between the two components, but the subtle chemical tweak — akin to writing a letter in a hard-to-read, byzantine font — gives an entirely different meaning for the ribosome, the researchers suggest.
The result is “groundbreaking,” says Nina Papavasiliou, a molecular biologist at Rockefeller University in New York City. “It says that we don’t fully understand how ribosomes decode RNAs.”
That discovery could also mean that genes contain more information than scientists have realized, Papavasiliou says.
Pseudouridine is already known to be important for the function of many types of RNA in cells. Yu and Karijolich engineered a system to discover whether mRNAs containing the modified letter might also have a slightly different function than those with plain old uridine. The researchers created a flawed copper-detoxifying gene called CUP1 that contained an early signal to stop making protein. The team also created a system that would cause yeast cells to edit the mRNA, replacing the uridine in the stop codon with pseudouridine. If pseudouridine behaved just like uridine, then cells would prematurely halt production of the detoxifying protein and wouldn’t be able to grow in the presence of copper.
Yeast cells that replaced uridine in the stop sign with pseudouridine could grow on copper, the researchers found. Looking more closely, the team found that instead of reading the stop sign as stop, ribosomes interpreted the pseudouridine-containing codon as an instruction to insert the amino acids serine, threonine, phenylalanine or tyrosine into the protein.
That choice of amino acids by the ribosome has biologists reeling, because those aren’t even the amino acids usually chosen when the protein factories do occasionally run stop signs.
“When you know the literature, you would expect other [amino acids],” says Henri Grosjean, a biochemist and geneticist at the University of Paris-South.
Apparently ribosomes haven’t read those papers.
Whether pseudouridine plays a part in changing the genetic code in nature remains to be seen, but researchers are betting that it does. The implications for health and disease could be great, says Juan Alfonzo, a molecular biologist at the Ohio State University. Pseudouridines may be required to make some proteins correctly, but “misplacing a pseudouridine could make things a physiological mess,” he says, causing some proteins to have flaws, even fatal ones.
And Yu and Karijolich’s technique might be used to fix genetic errors, too. Introducing stop sign–busting pseudouridine into an RNA may one day help people with rare genetic diseases in which one of their genes contains an early stop codon, Alfonzo says.
[Here you go, again. Crick' infamous "Central Dogma of Molecular Biology", totally falsely pontificating that sequence information NEVER recurses from proteins and RNA to DNA, was immediately laughed about by Nobelists Jacobs and Monod as far back as half a Century ago (1961). "Facts don't kill theories", however - some lonely morons (who can not produce properly rounded numbers!) could still dismiss this experimental discovery as "not fitting into their ideology"). Obsolete theories are killed by superior theories - in this case The Principle of Recursive Genome Function peer reviewed paper and its popularization in Google Tech Talk YouTube. - This entry can be discussed in the FaceBook page of Andras Pellionisz]
Editing the genome - Scientists unveil new tools for rewriting the code of life
BOSTON, MA (July 14, 2011) — The power to edit genes is as revolutionary, immediately useful and unlimited in its potential as was Johannes Gutenberg's printing press. And like Gutenberg's invention, most DNA editing tools are slow, expensive, and hard to use—a brilliant technology in its infancy. Now, Harvard researchers developing genome-scale editing tools as fast and easy as word processing have rewritten the genome of living cells using the genetic equivalent of search and replace—and combined those rewrites in novel cell strains, strikingly different from their forebears.
"The payoff doesn't really come from making a copy of something that already exists," said George Church, a professor of genetics at Harvard Medical School who led the research effort in collaboration with Joe Jacobson, an associate professor at the Media Lab at the Massachusetts Institute of Technology. "You have to change it—functionally and radically."
Such change, Church said, serves three goals. The first is to add functionality to a cell by encoding for useful new amino acids. The second is to introduce safeguards that prevent cross-contamination between modified organisms and the wild. A third, related aim, is to establish multi-viral resistance by rewriting code hijacked by viruses. In industries that cultivate bacteria, including pharmaceuticals and energy, such viruses affect up to 20 percent of cultures. A notable example afflicted the biotech company Genzyme, where estimates of losses due to viral contamination range from a few hundred million dollars to more than $1 billion.
In a paper scheduled for publication July 15 in Science, the researchers describe how they replaced instances of a codon — a DNA "word" of three nucleotide letters — in 32 strains of E. coli, and then coaxed those partially-edited strains along an evolutionary path toward a single cell line in which all 314 instances of the codon had been replaced. That many edits surpasses current methods by two orders of magnitude, said Harris Wang, a research fellow in Church's lab at the Wyss Institute for Biologically Inspired Engineering who shares lead-author credit on the paper with Farren Isaacs, an assistant professor of molecular, cellular and developmental biology at Yale University and former Harvard research fellow, and Peter Carr, a research scientist at the MIT Media Lab.
In the genetic code, most codons specify an amino acid, a protein building block. But a few codons tell the cell when to stop adding amino acids to a protein chain, and it was one of these "stop" codons that the Harvard researchers targeted. With just 314 occurrences, the TAG stop codon is the rarest word in the E. coli genome, making it a prime target for replacement. Using a platform called multiplex automated genome engineering, or MAGE, the team replaced instances of the TAG codon with another stop codon, TAA, in living E. coli cells. (Unveiled by the team in 2009, the MAGE process has been called an evolution machine for its ability to accelerate targeted genetic change in living cells.)
While MAGE, a small-scale engineering process, yielded cells in which TAA codons replaced some but not all TAG codons, the team constructed 32 strains that, taken together, included every possible TAA replacement. Then, using bacteria's innate ability to trade genes through a process called conjugation, the researchers induced the cells to transfer genes containing TAA codons at increasingly larger scales. The new method, called conjugative assembly genome engineering, or CAGE, resembles a playoff bracket—a hierarchy that winnows 16 pairs to eight to four to two to one—with each round's winner possessing more TAA codons and fewer TAG, explains Isaacs, who invokes "March Madness."
"We're testing decades-old theories on the conservation of the genetic code," Isaacs said. "And we're showing on a genome-wide scale that we're able to make these changes."
Eager to share their enabling technology, the team published their results as CAGE reached the semifinal round. Results suggested that the final four strains were healthy, even as the team assembled four groups of 80 engineered alterations into stretches of the chromosome surpassing 1 million DNA base pairs. "We encountered a great deal of skepticism early on that we could make so many changes and preserve the health of these cells," Carr said. "But that's what we've seen."
The researchers are confident that they will create a single strain in which TAG codons are completely eliminated. The next step, they say, is to delete the cell's machinery that reads the TAG gene — freeing up the codon for a completely new purpose, such as encoding a novel amino acid.
"We're trying to challenge people," Wang said, "to think about the genome as something that's highly malleable, highly editable."
[Starting from the conclusion of the authors' Press Release, the philosophical message is: "The Genome is NOT your Destiny". This drastically altered world-view, introduced by Genome Informatics, is similar in scope to the Heisenberg "Principle of Uncertainty" - that forever changed the former deterministic physics to probabilistic physics. As emphasized in the 2088 Google Tech Talk YouTube, the radically different new philosophy instills scientific reason for hope ("The Principle of Recursive Genome Function" presented as "The Circle of Hope"), replacing the gloomy former attitude that "Your Genome was Your Destiny" - both a reason for gloom and doom, as well as avoiding any genome testing, in the (mistaken) belief that "there is nothing one can do about it". Beyond philosophical world-view and attitude of the masses, the paradigm-shift practical application is also evaluated (by some interesting metaphors) in various leading Journals (New York Times, Los Angeles Times, Forbes, etc, etc.). Let's just start with the metaphors used in the authors' Press Release. Is the new breakthrough like "Gutenberg' Press", or "The Word Processor (software)"? In terms of "Synthetic Biology", entirely new "texts" can be composed of the A,C,T,G letters. Most exciting! In terms of "The Principle of Recursive Genome Function", however, (see one follower below), an even more immediate - and patently useful - "word processing" could e.g. be made in time in vivo for humans - by the "replace" function of e.g. the "fractal defects" of the genome with snippets that do obey the genome's own fractal mathematics - thus fractal iterative recursion function of genome regulation no longer "hicks up" on the glitches! Those familiar with (man-made) software know, that the instructions are first run through a "Syntax Checker"; to see if all instructions fully conform with the formal requirements. Suppose an instruction is found to have a "structral variant" (gibberish), the coder uses "global replace" throughout the code to straighten out all such glitches. (Those familiar with code also know, that impeccable syntax is a necessary but not sufficient condition for any software to run - but actual compilation could never take place before all instructions obey the rules). The articles below, to "replace" codons (or even just a single nucleotide in case of Progeria...) show the enormous potential power of the breakthroughs for IT-led "New Pharma". The issue can be debated in Andras Pellionisz' FaceBook page. - AJP]
An Explanation of Molecular Polymorphisms and Why Many Molecular Structures Can Be Preserved Although They Are Not Predominant
DNA AND CELL BIOLOGY
Volume 29, Number 10, 2010
Mary Ann Liebert, Inc.
Pp. 571-576
DOI: 10.1089/dna.2009.094
Borros M. Arneth
Institute of Clinical Chemistry and Laboratory Medicine, Johannes Gutenberg University, Mainz, Germany
The existence of many processes that regulate RNA expression poses a challenge to the idea that the cell is the culmination of a highly efficient interplay of individual proteins, each with specific, highly specialized functions.
It will be demonstrated here the extent to which the cell may undergo evolutionary processes that also occur in the macrocosmos, specifically with reference to the rules of mutation and preservation. These molecular evolutionary processes could facilitate a better understanding of the development of molecular structures and the functioning of the cell and could give an explanation of the molecular polymorphisms and also explain why many molecular structures can be preserved although they are not predominant...
Pellionisz (2008) describes the principle of recursive genome function and how DNA is affected by this recursion through proteins.
[See the 2008 paper in full here - AJP]
Clue to kids' early aging disease found [The Colossal Paradigm-Shift - AJP]
By Madison Park, CNN
July 1, 2011 12:08 p.m. EDT
[Dr. Francis Collins wrote the book on the colossal paradigm shift; Personalized (Genomic) Medicine - AJP]
(CNN) -- Her name was Meg, 23, featherweight and feisty.
Standing 3 feet tall, Meg didn't look like her peers. Bald and skinny, her body was aging rapidly because she had a rare genetic disease called Hutchinson-Gilford progeria syndrome.
People with progeria wrinkle and develop the same circulation and joint ailments as the elderly -- except most of them die by age 13.
Progeria affects 200-250 children worldwide, but research into the disease could offer clues on cellular function and how it affects human aging and other age-related diseases.
This week, a study about a possible treatment was published in Science Translational Medicine. Dr. Francis Collins, director of the National Institutes of Health, is one of the authors.
About 30 years ago, Collins, then a young Yale University doctor, met Meg. He realized there was little he could do for his patient, but he couldn't look away.
"It was compelling to try to understand why someone's body is melting away in the ravages of age," he said. "You couldn't be involved without marveling at it and wanting to do something to understand the situation."
Collins offered his concern and compassion, but there was no treatment for her disease.
Despite her grave prospects and appearance, Meg did not shy away from the public eye. Instead, she became an outspoken advocate for disabled people in Milford, Connecticut.
Long before it became customary to do so, "She got that town to become friendly to the disabled," Collins said. "She made it happen."
Just because she was diminutive, it didn't mean people could step all over her. Meg could also "curse like a sailor" in her birdlike voice, he said.
Meg Casey died in 1985, but she never faded from the doctor's memory.
Collins' role as a geneticist is to decode the most complex puzzles of human life. He is best known as a leader of the Human Genome Project that mapped and sequenced the human DNA.
The mystery of progeria remained one of his interests. Collins and seven others are authors of a study that found an immune suppressing drug, called rapamycin, could possibly treat progeria.
There have been no approved drugs or treatment to slow the course of the disease.
Children with this rare genetic condition lose their hair as infants, while they're learning to talk. Their minds develop normally, but their bodies age rapidly.
As toddlers, their skin begins to wrinkle and sag. Most of them die of age-related causes, like heart disease, heart attack or stroke, before they start high school.
Clues found in mysterious childhood aging disease
The cause: A single letter in a progeria patient's genome is out of place. This genetic defect causes the child to accumulate too much of a toxic protein called progerin and the cells can't get rid of it. [The single letter clue was actually found in 2003 by Vastag B 2003 Cause of progeria's premature aging found: expected to provide insight into normal aging process. JAMA 289:2481-2482 - AJP]
"Cells have a normal way of removing byproducts," said Dr. Dimitri Krainc, an author in the study. "You accumulate trash and you take it out. That's what happens in cells. As they work, they start accumulating byproducts. There has to be a system to remove those byproducts."
Progerin is seen in small amounts in healthy people's cells as they begin to age. The difference is that healthy cells can get rid of the damaged molecules and unneeded proteins.
The researchers chose to test rapamycin on cells from progeria patients because research suggested its effectiveness in extending the lives of mice. Rapamycin is an immune-suppressing drug given to transplant recipients to prevent organ rejection.
Krainc, an associate professor of medicine at Harvard University, said cells from progeria patients look "very sick."
"When you treat them with rapamycin, it looks normal," he said. The drug appeared to activate a cellular system that removes the waste.
"Rapamycin or similar drugs of that same class are capable of revving up that cleanup system," Collins said.
But the drug comes with major risks because it raises cholesterol levels and suppresses the immune system, making patients more vulnerable to infections. Rapamycin was derived from bacteria found in the soil of Easter Island in the 1960s.
The results of the progeria study triggered discussions about a potential human clinical trial.
"I can't say what the drug will do before clinical trial," Krainc said. "We're very hopeful, because of the dramatic effect (in cells) was replicated."
The protein buildup in cells is seen in other diseases, such as Alzheimer's and Parkinson's disease. Alzheimer's patients have tau protein tangles and another protein called the beta amyloid plaques in their brains. Parkinson's patients have a buildup of a protein called alpha-synuclein.
Some scientists hypothesize that the cell's inability to dispose of unnecessary protein as humans age is what could lead to severe illnesses.
"This is a fundamentally important pathway by which cells maintain their own health," Collins said. "Yet as we age, we don't do it quite as well and the buildup starts to happen."
The Progeria Research Foundation, a nonprofit that supports affected families and promotes scientific research on the disease, supplied tissue and cell samples for the study.
Dr. Leslie Gordon, the foundation's medical director and a mother of a boy with progeria, said scientists are considering the use of RAD001, a modified version of rapamycin that has fewer side effects in a potential human trial.
"Nothing is in place," she said. "These things take instructional and federal scrutiny. We're considering this based on this very study."
For years, progeria research languished as another orphan disease. Rare diseases struggle to attract attention and research dollars because they affect very few people.
Drug development favors common diseases that could lead to blockbuster medications. This leaves patients and families of orphan diseases with little support or medical options.
"The first thing that we discovered after my son was diagnosed was there was nothing out there to help researchers to do research -- no cells or tissue, no clinical information bank, no outreach for families," said Gordon. "There was nothing to work with."
The foundation established a cell and tissue bank and started efforts to identify more progeria patients. It is involved in a different clinical trial involving a three-drug cocktail to treat progeria.
It hasn't been easy trying to bring attention and resources to a rare disease, Gordon said.
"For all the times people could not help, there are people like Francis (Collins) who say yes," Gordon said. "When we approached him, it was the fact that he cared and the fact that it bothered him that we didn't have the answer."
"He's not only interested in science, he's interested in people."
Collins has met her young son, Sam.
Patients with progeria are enthusiastic, precocious and embrace life, Collins said.
"They're taking what most of us look at as a really discouraging situation and saying, 'No, darn it. I'm going to make the most of it. If I have a shortened time being here, I'm not going to waste it feeling sorry for myself.
"I'm going to make the most of it.'"
[Given a single-letter glitch in the DNA (that, in the wrong place, could make the codon produce inappropriate and/or toxic protein), as elaborated in e.g. Dr. Collins' book can be theoretically dealt with in two entirely different manner. One is to CURE the DNA by patching the erroneous code (see early animal research results of e.g. Dr. George Church' lab in the news below). The traditional ways and means of Big Pharma aim at THERAPY by means of compounds (that, as Dr. Collins elaborates in his book) may or may not work in individual cases - plus could have serious side-effects in SOME, causing FDA to withhold or pull a compound such that it might never become an approved drug (as FDA is mandated by a generation-old legislation to apply a "one drug fits all" - scientifically clearly obsolete - criteria. The paradigm-shift towards "New Pharma" (led by genome informatics, to understand how the genome malfunctions and/or misregulated e.g. in the case of cancer, and how to measure the efficacy in genomic (not often too late protein) results, as well as how to modify the genome such that "The Genome is NOT your Destiny") is, indeed, colossal. The issue can be debated in Andras Pellionisz' FaceBook page. - AJP]
Researchers Use Genome Editing Methods to Swap Stop Codons in Living Bacteria
July 14, 2011
Genomeweb
By Andrea Anderson
NEW YORK (GenomeWeb News) – In Science today, an international research group led by investigators at Harvard University and the Massachusetts Institute of Technology reported that they have advanced their genome editing technology, using these tools to develop Escherichia coli strains in which one stop codon has been replaced by another.
"We're able to, at a genome-wide scale, make codon replacements for an entire codon across the whole genome," co-first author Farren Isaacs, who performed the research as a post-doctoral researcher at Harvard University and is currently a researcher at Yale University, told GenomeWeb Daily News. "Basically we use living cells as a template and we make changes within the living cells."
To do this, the team split the E. coli genome into dozens of pieces and then used a method called multiplex automated genome engineering, or MAGE, to introduce codon changes to each region in differents E. coli cultures, trading the guanine nucleotide in the TAG stop codon for an adenine to make the synonymous stop codon TAA. From there, they came up with a hierarchical "conjugative assembly genome engineering" (CAGE) strategy to amalgamate these changes so that increasingly large stretches of the genome were recoded in each intermediate strain.
At the moment, the team has four E. coli strains that they plan to use to generate a single strain in which every TAG has been converted to TAA.
"These four strains, which contain up to 80 modifications per genome, can be combined to complete the assembly of a fully recoded strain containing all 314 TAG-to-TAA codon conversions," the study authors wrote.
The long-term objectives of such experiments are to develop the technology to make large-scale changes to genomes and introduce new functions into organisms, Isaacs explained, and, eventually, to create organisms with new genetic codes, including those capable of producing proteins from amino acids not currently found in nature.
"That could lead to entirely new classes of drugs, industrial enzymes, biopolymers that could be used to make new types of materials, and so on," he said, noting that similar strategies could also be used to genetically isolate organisms, thwarting potential viral pathogens, and to contain genetically engineered organisms within restricted environments.
Members of the team previously used MAGE to reprogram a biosynthesis pathway in E. coli cells leading to enhanced production of the compound lycopene — work that they reported in Nature in 2009.
Now, researchers have shown that they can build on this method, putting together pieces of the genome containing MAGE-induced changes to produce bacterial strains with specific codon changes across larger and larger swaths of the genome.
The 20 amino acids and "stop" signal are encoded by 64 triplet nucleotide sequences, meaning there are more than three times as many codons as there are functions for which they code.
The researchers were able to exploit this redundancy in the genetic code, Isaacs explained, trading the stop codon TAG, which usually appears 314 times in the E. coli genome, for another stop codon, TAA, that's recognized by a different release factor during translation.
The team first introduced targeted alterations into 32 E. coli cultures by MAGE using oligonucleotides containing the desired changes, Isaacs said, gauging the functional consequences, if any, along the way.
"We decided to pursue a strategy whereby we would divide up the strains and quickly introduce all of these changes to small pools to, one, verify that they're viable and, two, be able to detect and quantify phenotypes," he explained. "It was important to be able to obtain enough resolution on the changes that we're making to see if any of them led to any sort of strange phenotype."
They then merged the MAGE-produced alterations into their final four intermediate strains using CAGE. Coupled with selection, this hierarchical genome engineering method let the researchers transplant well-defined stretches of DNA from one genome to another without introducing unintended changes to the recipient genome.
The researchers are now continuing to use CAGE to take the E. coli strains with the most extensive recoding to the next stage — assembling a strain in which every TAG in the genome has been converted to TAA.
In the future, they also plan to try trading out other codons, including those coding for amino acids. The team will likely attempt to use their codon replacement strategy in other bacterial species, Isaacs noted, and, perhaps, in eukaryotic cells as well.
"Our methods treat the chromosome as both an editable and an evolvable template," the researchers wrote, "permitting the exploration of vast genetic landscapes."
[Look for a piece of news that appeared a few minutes after this one. (Should be shown here ASAP). Putting 1+1 together, you'll see crystal clear why and how we are facing the biggest paradigm-shift of science & technology of all times. The issue will be up for debate in Andras Pellionisz' FaceBook page. - AJP]
The Mathematics of DNA [is Fractal - says Dr. Perez]
The number of triplets that begin with a T is precisely the same as the number of triplets that begin with A (to within 0.1%).
The number of triplets that begin with a C is precisely the same as the number of triplets that begin with G.
The genetic code table is fractal - the same pattern repeats itself at every level. The micro scale controls conversion of triplets to amino acids, and it’s in every biology book. The macro scale, newly discovered by Dr. Perez, checks the integrity of the entire organism.
Perez is also discovering additional patterns within the pattern.
I am only giving you the tip of the iceberg. There are other rules and layers of detail that I’m omitting for simplicity. Perez presses forward with his research; more papers are in the works, and if you’re able to read French, I recommend his book “Codex Biogenesis” and his French website. Here is his English paper.
(By the way, he found some of his most interesting data in what used to be called “Junk DNA.” It turns out to not be junk at all.)
OK, so what does all this mean?
Copying errors cannot be the source of evolutionary progress, because if that were true, eventually all the letters would be equally probable.
This proves that useful evolutionary mutations are not random. Instead, they are controlled by a precise Evolutionary Matrix to within 0.1%
When organisms exchange DNA with each other through Horizontal Gene Transfer, the end result still obeys specific mathematical patterns
DNA is able to re-create destroyed data by computing checksums in reverse - like calculating the missing contents of a page ripped out of a novel.
No man-made language has this kind of precise mathematical structure. DNA is a tightly woven, highly efficient language that follows extremely specific rules. Its alphabet, grammar and overall structure are ordered by a beautiful set of mathematical functions.
More interesting factoids:
The most common pair of letters (TTT and AAA) appears exactly 1/13X as often as all the letters combined – consistently, the genomes of humans and chimpanzees.
If you put the 32 most common triplets in Group 1 and the 32 least common triplets in Group 2, the ratio of letters in Group1:Group2 is exactly 2:1. And since triplet counts occur in symmetrical pairs (TTT-AAA, TAT-ATA, etc), you can group them into four groups of 16.
When you put those four triplet populations on a graph, you get the peace symbol:
Does this precise set of rules and symmetries appear random or accidental to you?
My friend, this is how it is possible for DNA to be a code that is self-repairing, self-correcting, self-re-writing and self-evolving. It reveals a level of engineering and sophistication that human engineers could only dream of. Most of all, it’s elegant.
Cancer has sometimes been described as “evolution run amok.” Dr. Perez has noted interesting distortions of this matrix in cancer cells. I strongly suspect that new breakthroughs in cancer research are hidden in this matrix.
I submit to you that the most productive research that can possibly be conducted in medicine and computer science is intensive study of the DNA Evolution Matrix. Like I said, this is just the tip of the iceberg.
There is so much more here to discover!
When we develop computer languages based on DNA language, they will be capable of extreme data compression, error correction, and yes, self-evolution. Imagine: Computer programs that add features and improve with time. All by themselves.
What would that be like?
Perry Marshall
P.S.: Dr. Perez and I are friends. Perez worked on HIV research with the man who originally discovered HIV, Luc Montagnier. Perez also worked in biomathematics and Artificial Intelligence at IBM. I’m familiar with this work because last spring I had the privilege of helping him translate his groundbreaking research paper about this into English. [See link above - AJP]
In the 1940′s, the eminent scientist Barbara McClintock damaged parts of the DNA in corn maize. To her amazement, the plants could reconstruct the damaged section. They did so by copying other parts of the DNA strand, then pasting them into the damaged area.
This is a lot like ripping an entire page out of a mystery novel and somehow being able to re-construct the missing text, even though the page is destroyed.
This discovery was so radical at the time, hardly anyone believed McClintock’s reports. [To the contrary, her pioneering was opposed at every step of the way, ignorant detractors called Barbara McClintock a "Kook" - she got her Nobel several decades after her "lucid heresy"]
And we still wonder: How does a tiny cell possibly know how to do…. that???
A French HIV researcher and computer scientist has now found part of the answer. Hint: The instructions in DNA are not only linguistic, they’re beautifully mathematical.
Imagine that someone gives you a mystery novel with an entire page ripped out.
And let’s suppose someone else comes up with a computer program that reconstructs the missing page, by assembling sentences and paragraphs lifted from other places in the book.
Imagine that this computer program does such a beautiful job that most people can’t tell the page was ever missing.
DNA does that.
In the 1940′s, the eminent scientist Barbara McClintock damaged parts of the DNA in corn maize. To her amazement, the plants could reconstruct the damaged section. They did so by copying other parts of the DNA strand, then pasting them into the damaged area.
This discovery was so radical at the time, hardly anyone believed her reports. (40 years later she won the Nobel Prize for this work.)
A French HIV researcher and computer scientist has now found part of the answer. Hint: The instructions in DNA are not only linguistic, they’re beautifully mathematical. There is an Evolutionary Matrix that governs the structure of DNA.
Computers use something called a “checksum” to detect data errors. It turns out DNA uses checksums too. But DNA’s checksum is not only able to detect missing data; sometimes it can even calculate what’s missing. Here’s how it works.
In English, the letter E appears 12.7% of the time. The letter Z appears 0.7% of the time. The other letters fall somewhere in between. So it’s possible to detect data errors in English just by counting letters.
In DNA, some letters also appear a lot more often (like E in English) and some much less often. But… unlike English, how often each letters appears in DNA is controlled by an exact mathematical formula that is hidden within the genetic code table.
When cells replicate, they count the total number of letters in the DNA strand of the daughter cell. If the letter counts don’t match certain exact ratios, the cell knows that an error has been made. So it abandons the operation and kills the new cell.
Failure of this checksum mechanism causes birth defects and cancer.
Dr. Jean-Claude Perez started counting letters in DNA. He discovered that these ratios are highly mathematical and based on “Phi”, the Golden Ratio 1.618. This is a very special number, sort of like Pi. Perez’ discovery was published in the scientific journal Interdisciplinary Sciences / Computational Life Sciences in September 2010.
Before I tell you about it, allow me to explain just a little bit about the genetic code.
DNA has four symbols, T, C, A and G. These symbols are grouped into letters made from combinations of 3 symbols, called triplets. There are 4x4x4=64 possible combinations.
So the genetic alphabet has 64 letters. The 64 letters are used to write the instructions that make amino acids and proteins.
Perez somehow figured out that if he arranged the letters in DNA according to a T-C-A-G table, an interesting pattern appeared when he counted the letters.
He divided the table in half as you see below. He took single stranded DNA of the human genome, which has 1 billion triplets. He counted the population of each triplet in the DNA and put the total in each slot:
When he added up the letters, the ratio of total white letters to black letters was 1:1. And this turned out to not just be roughly true. It was exactly true, to better than one part in one thousand, i.e. 1.000:1.000.
Then Perez divided the table this way:
Perez discovered that the ratio of white letters to black letters is exactly 0.690983, which is (3-Phi)/2. Phi is the number 1.618, the “Golden Ratio.”
He also discovered the exact same ratio, 0.690983, when he divided the table the following two alternative ways:
Again, the total number of white letters divided by the total number of black letters is 0.6909, to a precision of better than one part in 1,000.
[We will never know fractal computer languages of DNA without appropriate (and potentially eminently lucrative) funding. Dr. Perez is an independent scientist in France, who cited his motivated to write his papers by publications of Pellionisz (2008) The Principle of Recursive Genome Function, Pellionisz, A. (2009) From the Principle of Recursive Genome Function to Interpretation of HoloGenome Regulation by Personal Genome Computers. Cold Spring Harbor Laboratory; Personal Genomes, Sept. 14-17, 2009 and Lander et al. (2009 October 9th Science cover article). While Dr. Lander is Science Advisor to the US President and thus could help ensure that the USA emerges in her race with China, Japan, Korea, India - and even Russia, the US government is out of funds - fractal clues to e.g. cancer (as voiced in the Google Tech Talk YouTube by Dr. Pellionisz, 2008), may come from "Venture philantrophists" like Dr. Lander's own Broad Institute (Eli and Edythe Broad) - AJP].
Cell Surface as a Fractal: Normal and Cancerous Cervical Cells Demonstrate: Different Fractal Behavior of Surface Adhesion Maps at the Nanoscale
M. E. Dokukin,1 N.V. Guz,1 R. M. Gaikwad,1 C. D. Woodworth,2 and I. Sokolov1,3,* 1Department of Physics, Clarkson University, Potsdam, New York 13699-5820, USA 2Department of Biology, Clarkson University, Potsdam, New York 13699-5820, USA 3Nanoengineering and Biotechnology Laboratories Center (NABLAB), Clarkson University, Potsdam, New York 13699-5820, USA
(published 8 July 2011)
Here we show that the surface of human cervical epithelial cells demonstrates substantially different fractal behavior when the cell becomes cancerous. Analyzing the adhesion maps of individual cervical cells, which were obtained using the atomic force microscopy operating in the HarmoniX mode, we found that cancerous cells demonstrate simple fractal behavior, whereas normal cells can only be approximated at best as multifractal. Tested on 300 cells collected from 12 humans, the fractal dimensionality of cancerous cells is found to be unambiguously higher than that for normal cells.
DOI: 10.1103/PhysRevLett.107.028101
China genomics institute outpaces the world
(Xinhua)
Updated: 2011-06-14 17:19
SHENZHEN - Many people were surprised when BGI (formerly Beijing Genomics Institute), a Chinese genomics institute, sequenced a strain of E. coli bacterium responsible for the outbreak in Germany that killed at least 18 people earlier this month.
But it was no surprise for Qin Junjie, deputy head of BGI's microorganism genomics researcher center, whose team sequenced the bacterium in three days. "We have the greatest output of genomics data and the best team to analyze it," Qin said.
BGI is more like a factory than a lab, according to Qin. The BGI facility, a converted shoe factory in Shenzhen city, now houses 137 top-of-the-line genome-sequencing machines and high-speed computers.
BGI pumped out 500 Tb of genomics data in 2010 - ten times the amount of data the US National Center for Biology Information (NCBI) produced in the past twenty years. BGI expects to produce 100 Pb of data in 2011, Qin said [That will be two hundred times, in a single year, of NCBI output in the past twenty years - meanwhile, NCBI closes some of its data-services, due to lack of continued government support - AJP] .
In addition, BGI used Ion Torrent, a newly-developed sequencing machine that is much quicker. "Even half an hour counts in the fight against epidemics," said Yang Bicheng, spokeswoman for BGI.
To cope with the vast amount of data, BGI needs a robust, young staff. The institute has 3,000 scientists who are younger than 25 on average. At 29, Qin is one of the oldest.
Li Yingrui was just a college student and an intern of BGI when he published his first paper in Nature Journal in 2007. Now, Li, 24, directs the bioinformatics department and its 1,500 computer scientists. He has become one of BGI's leading scientists with five papers published in Science Magazine and Nature Journal.
In BGI, college, or even high school students, lead cutting-edge projects and publish papers in top science journals. Yet despite the impressive work of these young scientists, their pay isn't so world-class. A recent college graduate gets about 3,000 yuan ($462) per month. The average monthly salary in Shenzhen is more than 4,000 yuan.
Having an army of scientists at a comparatively low cost contributes to BGI's competitiveness, Yang said.
At BGI, young people can work with world's leading scientists and participate in international projects," Yang said. "They also have the opportunity to lead research in new areas, and such motivation is more powerful than anything else."
The growing fame of BGI in the world shows China's efforts in promoting scientific advancement is starting to pay off, said Wang Jian, BGI's director.
"The scientific outlook on development is a key policy of China, and it requires the government to focus on supporting research facilities like BGI," Wang said.
In addition, China has been striving for progress in medical reform, agriculture and environmental protection, which in turn boosts bioscience research, he added.
In a visit to BGI on June 4, two days after it completed the sequencing of the bacterium, Xu Qin, mayor of Shenzhen, said the city will give all-out support to boost the leap-frog development of BGI.
Shenzhen, a boomtown near Hong Kong, is the base of some of China's most innovative companies such as ZTE and Huawei.
[It is not that in the USA there is nothing done. There is a lot of competition, for instance. The E. coli German strain has also been sequenced by Pacific Biosciences SMRT molecular sequencer - while China favored Life Technologies' Ion Torrents sequencer (that is way over ten times cheaper as a machine). Illumina is also in the race - and of course for full human DNA sequencing there is Complete Genomics. All US companies develop their separate (and largely incompatible) software for the "sequencing" part (assembly), as well as for the crucial "analytics" part (interpretation). This is exactly where China differs; and appears to develop a 200x lead over US government (in a single year, beating the two decades worth of US government support). In "assembly", China (BGI) already announced a GPU-assisted software cloud tool kit. Unless the US entrepreneurship wakes up (it will take either a miracle, or some movers & shakers getting sick with cancer, or just getting sick of the feeling of a mortal dependence...) the US will find Genome Informatics going the same way as the entire consumer electronics did ("Invented and designed in the USA - made in China" - with the "tiny" difference that consumer electronics is mostly for fun, but Genome Informatics is a life or death issue). - This entry can be discussed on the FaceBook of Andras Pellionisz]
Searching For Fractals May Help Cancer Cell Testing
Researchers use a new tool to determine that cancerous cells have different geometries than healthy cells.
Jul 5, 2011
By Phillip F. Schewe
Inside Science News Service
[The above figure shows a cell imaged by SEM (scanning electron microscope) and AFM (atomic force microscope).- excerpt from the Sokolov paper - AJP]
(ISNS) -- Scientists have long known that healthy cells looked and behaved differently from cancer cells. For instance, the nuclei of healthy cells -- the inner part of the cells where the chromosomes are stored -- tend to have a rounder surface than the nuclei in cancerous cells.
A new experiment looks at the shapes of healthy and cancerous cells taken from the human cervix and has attempted to quantify the geometrical differences between them. The research, carried out at Clarkson University in Potsdam, N.Y. finds that the cancerous cells show more fractal behavior than healthy cells.
Fractal is the name used for heavily indented curves or shapes that look very similar over a variety of size scales. For example, the edge of a snowflake, when observed with a microscope, has a lacelike structure that looks the same whether at the level of a millimeter, or a tenth of a millimeter, or even a thousandth of a millimeter. The position of galaxy clusters in the sky seems to be fractal. So does the snaking geometry of streams in a river valley, or the foliage of leaves on a tree. The shape of coastlines and clouds reveals a fractal, "self-similar" geometry. Even the "drip" paintings of Jackson Pollack are fractal.
Fractal geometry apparently also appears in the human body. The pattern of heartbeats over long intervals looks fractal. How about the geometry of cells? And could the observation of fractal geometry be used to identify cancer cells?
Igor Sokolov and his Clarkson colleagues used an atomic force microscope to view cells down to the level of one nanometer, or a billionth of a meter (one-millionth of a millimeter). Just as the needle on a record player rides over the groove of a rotating vinyl record to read out the music stored on the record's surface, so the sharp needle forming the heart of an atomic force microscope rides above a sample reading out the contours of matter just below at nearly atomic resolution.
Previous studies of cells at the microscopic level produced two-dimensional maps of the cells' surface. The new study produces not only three-dimensional surface maps of geometry. But with their atomic force microscope device the Clarkson scientists can also map properties such as the rigidity of the cells at various points on its surface or a cell's adhesion, its ability to cling to a nearby object, such as the needle probe of the atomic force microscope itself.
The Clarkson measurements show that cancerous cells feature a consistent fractal geometry, while healthy cells show some fractal properties but in an ambiguous way. The fact that the adhesive map is fractal for cancerous cells but not for healthy cells was not known before.
Being able to differentiate clearly between healthy and cancerous cells would be important step toward a definitive diagnosis of cancer. Can a fractal measurement of cells serve as such a test for malignancy?
Sokolov believes it can.
"The existing cytological screening tests for cervical cancer, like Pap smear, and liquid-based cytology, are effective and non-invasive, but are insufficiently accurate," said Sokolov.
These tests determine the presence of suspicious abnormal cells with sensitivity levels ranging from 80 percent all the way down to 30 percent, for an average of 47 percent.
The fractal criterion used in the Clarkson work was 100 percent accurate in identifying the cancerous nature of 300 cells derived from 12 human subjects, Sokolov said. He intends now to undertake a much wider test.
"We expect that the methodology based on our finding will substantially increase the accuracy of early non-invasive detection of cervical cancer using cytological tests," Sokolov said.
Sokolov asserts that physics-based methods, such as his atomic force microscope maps of cells, will complement or even exceed in detection ability the more traditional biochemical analysis carried out at the single cell level.
"We also plan to study how fractal behavior changes during cancerous transformation, when a normal cell turns into a fully developed malignant cell, one with a high degree of invasiveness and the ability to reproduce itself uncontrollably," Sokolov added.
Robert Austin, an expert on biological physics at Princeton University in N.J., believes it is important to learn more about the properties that make cancer cells lethal, such as their ability to metastasize, to invade new parts of the body. About the Clarkson paper, which is appearing in the journal Physical Review Letters, [This column will excerpt the Letter, to appear on July 8th by courtesy of Prof. Sokolov - AJP] Austin said "Perhaps this is a step in the direction of connecting physical aspects of cancer cells with the biological reality that their proliferation and invasiveness is what makes them deadly."
[FractoGene (2002, web) and The Principle of Recursive Genome Function (2008, YouTube from 30:00 minutes) are rapidly closing on cancer. While the Fractal Recursive Iteration (developing fractal brain cells was shown as early as in 1989, the "lucid heresy", running head-to-head with both of the (totally wrong) axioms of Old School Genomics ("Junk DNA" and "Central Dogma") met with violent opposition at every step of the long way (including some detractor, alas with inaptitude in math not only to - admittedly - grasp advanced tensor and fractal geometry, but unable to produce a properly rounded number!). Today, a massive evidence is shown below to substantiate the Principle of Recursive Genome Function, where methylation of perused auxiliary (intergenic and intronic) genomic information is of the essence to keep fractal growth bounded (properly regulated). While Prof. Sokolov does not seem to connect the enhanced fractality of cancerous cells with genomic shifts (e.g. in methylation), it is clear that fractal geometry has an immediate utility e.g. for diagnosis by measuring significant difference in fractal dimension of already developed cancerous cells. Imagine how important it is to conduct a targeted search for "fractal defects" of the genome BEFORE the uncontrolled protein-production is spotted from the very early signs of genomic shifts e.g. in methylation! History will not be kind to those whose tardiness contributes to hundreds of millions of people dying of "Junk DNA diseases" (such as cancers), while those smart enough to advance (also in mathematics, in algorithmic - software enabling) to PostModern analytics of genome function will be richly rewarded - This entry can be discussed on the FaceBook of Andras Pellionisz]
A quest for better genetics [from Moscow...]
The Moscow News
By Oleg Nikishenkov at 27/06/2011 21:56
Genetics, advanced mathematics and new online solutions can help humans quantify their behavior with the ultimate goal of living longer.
That’s the message from Esther Dyson, a top-50 global IT investor and Skolkovo advisor who is promoting a new project in genome analysis designed to fulfill the goal.
“Biology becomes more and more information processing, and you have some of the best mathematicians and algorithm people here in Russia,” Dyson told The Moscow News on the sidelines of RIA Novosti’s Future Media Forum in Moscow last week.
The process of getting personal genetic analysis is fairly simple, despite its scientific complexity. All you have to do is deposit saliva into a test tube and send your sample to the lab. But it has geographical and even political restrictions. If you are in Russia, you need a special permission to send a sample of your saliva to the US, where the 23andMe (equal to the number of human chromosomes) labs are. And some US states prohibit the purchase of genetic data by private individuals on the assumption of reaction to this knowledge could be unpredictable. “We keep the information very secure but if you fear more than fear of death, we won’t get you genus [genomic] test done,” Dyson said.
Nikolai Mityushin, head of investments of the ABRT venture fund, said such projects are quite promising, as they deal with real needs like healthcare.
“On the other hand, the specifics of the project attribute it to the biotech industry, which has its own regulation,” he said.
Dyson said some Russian businesspeople she knows have already sent samples of their saliva to the project.
The higher math and strong computer systems required to analyze SNPs (Single Nucleotide Polymorphism), which are variations each our gene has.
“What we look for is specific little correlations of data, which come in different varieties,” Dyson said. They occur once in every 300 nucleotides on average, which means there are roughly 10 million SNPs in the human genome.
Currently genetic labs can analyze 1 million SNPs, but “in two years it will probably be 3 million and in a few years the entire genome will be collected,” Dyson said. That might help predict an individual’s reaction to certain drugs and the risk of some diseases, including heart failure, diabetes and cancer.
But so far the genetic testing only gives “hints” on how to change your behavior.
“The remainder of the proof is left as an exercise for the reader,” said Dyson quoting popular math professors’ saying. She explained that the next trend is user-generated data for research, as opposed to user-generated content. “And this research is about ourselves,” she said.
Pavel Gitelman, who coordinates the Red Quest project, said that in Russia such an innovation could be well received by wealthy clients. “We still have quite a huge category of people who mistrust genetics, but fully believe in all kinds of fortune tellers,” he said.
[Some of the World's best algorithm-creators, like Sergey Brin and Larry Page originate from Russia (Sergey was born in Moscow) - now running the World's most powerful IT company (Google, Inc.). Shura Grosberg published - while in Moscow, in the early 90-s - the seminal concept of the fractal folding structure of the DNA-strand (to become the foundation of Lander et. al. 2009 Oct 9 Science Cover article "Mr. President, the Genome is Fractal!"). As I mention towards the end of my 2008 Google Tech Talk YouTube , for those who know that the Internet itself is fractal (and depends on "recursion", moreover putting "cookies" to perused pages...) it should be obvious that the genome functions based on The Principle of Recursive Genome Function (Pellionisz, 2008, see the seminal Fractal Model of brain cell 1989). Interestingly, as shown below (a Figure from Pellionisz' 2009 presentation in Cold Spring Harbor Laboratories, upon invitation by Harvard Professor of Genetics George Church, even Esther Dyson's father (physicist Freeman Dyson) suspected that the origin of life may be fractal by showing the Mandelbrot fractal set on the cover of his book - though neither the index mentions "fractal", nor citation is not made to "Mandelbrot". Some of such "near misses" may be even more likely where the gap between "biologist" Lisenko and outstanding mathematicians let to some mistrust of genetics. To the advantage of the European Schools, the two mistaken dogmas of Genomics (the "Junk DNA" misnomer, rendered obsolete by Pellionisz' Symposium in Budapest, 2006, and Nobelist Crick's huge mistake "Central Dogma") have never been entrenched to such a degree in Europe than in America. In the USA, the establishment till the end of ENCODE 2007 and some detractor individual of Toronto, without the elementary competence in mathematics if a number was rounded correctly or not (!) inflicted severe damage on the development of PostModern Genomics. Huge downloads (much more from the Ukraine compared from Russia) from this website show a torrent of interest now in PostModern Genome Informatics. This entry can be discussed on the FaceBook of Andras Pellionisz]
Study Suggests Widespread Loss of Epigenetic Regulation in Cancer Genomes
June 27, 2011
By Andrea Anderson
Genomeweb
[Figure from Nature Genetics - by courtesy of Dr. Feinberg ]
NEW YORK (GenomeWeb News) - Cancer coincides with a widespread loss of epigenetic regulation affecting large chunks of DNA in the genome, according to a study in the early, online version of Nature Genetics yesterday. ["We suggest a model for cancer involving loss of epigenetic stability of well-defined genomic domains that underlies increased methylation variability in cancer that may contribute to tumor heterogeneity."].
Using custom microarrays, American researchers assessed methylation patterns in five types of cancer, focusing on regions of the genome that were shown to be differentially methylated in cancer in the past. The team found that cancer genomes show dramatically different methylation patterns compared to corresponding normal tissues, including a lack of defined methylation boundaries around so-called CpG islands where cytosine and guanine nucleotides frequently neighbor one another.
In addition, they reported, the cancers showed dramatic variability in their methylation levels, along with changes in some parts of the genome that are known to be differentially methylated in other tissue types or in undifferentiated cells.
Moreover, their bisulfite sequencing studies of colon cancer, pre-cancerous polyps, and normal colon tissue uncovered large stretches of DNA that were differentially methylated in the cancer samples, leading to altered expression of some cell cycle and cell matrix-related genes in these regions.
"Epigenetics, specifically DNA methylation, is losing its regulation in cancer and we think that that's helping cancer thrive," co-first author Winston Timp, a post-doctoral fellow in Andrew Feinberg's lab at Johns Hopkins University's Center for Epigenetics, told GenomeWeb Daily News.
"It may be a very early event which acts in conjunction with mutations to cause cancer," he added. "And it seems to happen in all types of cancer."
In 2009, Feinberg, Johns Hopkins biostatistics and epigenetics researcher Rafael Irizarry, and colleagues published a Nature Genetics study describing methylation differences in colon cancers compared to normal colon tissue. In particular, they found cancer-specific differences not necessarily within CpG islands, but more often on the "shores" of these islands.
In addition, many of the same regions that were differentially methylated in the colon cancers correspond to those that tend to be differentially methylated in various tissue types or in undifferentiated cells.
Consequently, the team decided to look at several cancer types in the current study, Timp explained. "If these differences seem to control differentiation state, so to speak, maybe they'll show differences in other cancers too."
Using Illumina custom bead arrays, the team first tested 122 colon, lung, breast, and thyroid cancers and Wilms' tumors, a childhood kidney cancer. They also tested 30 pre-malignant samples, along with 141 matched control samples, focusing on 384 sites in 151 cancer-specific differentially DNA-methylated regions detected in colon cancer in the past
Indeed, researchers reported, cancer and normal samples from each tissue type had very distinct methylation patterns. While both normal tissues and cancers tended to fall in distinct clusters based on their methylation patterns, methylation in the cancer samples was far more variable.
"This seems to show a loss of control - a loss of regulation - of methylation in cancer compared to [matched normal tissue]," Timp said. "The five different normal tissues also cluster out very well from each other … but all the cancers are much more variable."
To explore the methylation patterns across the genome in cancer cells, meanwhile, the researchers did shotgun, whole-genome bisulfite sequencing of three colorectal tumor samples — along with matched normal colon tissue and two pre-cancerous adenomatous polyp samples — using the ABI SOLiD platform.
From this sequence data, the team found altered methylation in large chunks of the cancer genomes. These blocks frequently had lower methylation levels than normal colon tissue, though, again, methylation patterns were far more variable in the cancer samples.
Consistent with these methylation changes, the expression of genes within these hypo-methylated blocks of the cancer genomes typically showed higher but more variable expression.
"We think that what's happening is a loss of [methylation] control rather than a concerted shift," Timp explained.
In addition, he noted, some of these differentially methylated regions in cancer appear to coincide with regions previously reported to be partially methylated in stem cells or other tissue types, while others overlapped with regions known to have chromatin alteration or epigenetic marks related to lamina function.
"We can say with some certainty that these areas are important for both differentiation and cancer," Timp said. "We would propose that maybe there's a link here and maybe cancer is losing regulation of these areas and reverting to a more primitive or less controlled state."
Although more research is needed to determine whether that is the case or whether there is some other explanation for the importance of the areas in both cancer and tissue development or differentiation, those involved in the study argue that their findings may ultimately lead to improved tests or treatments for cancer.
"Maybe the big lesson learned from our observation of this universal [epigenetic] chaos is that we may need to think not so much about just killing cancer cells, but also about ways of helping cancer cells figure out how to be what they're supposed to be, and re-educate them so they can stay truer to their normal identities," Feinberg said in a statement.
[This landmark experimentation substantiates the prediction of The Principle of Recursive Genome Function (also popularized in the YouTube, both in 2008) by Pellionisz. Fractal Iterative Recursion, to grow organelles, organs and organisms (FractoGene) requires not only a recursion to intergenic and intronic regions of the DNA to pick up auxiliary information to maintain growth, but also the auxiliary information must be cancelled (e.g. by methylation) to prevent uncontrolled use, resulting in cancerous protein production. The two snapshots from YouTube show normal methylation (at 31:04 of YouTube, while at 31:50 of the YouTube the epigenomic control by methylation is shown to be defective, leading to unduly repeated perusal of "non-coding" information. This entry can be discussed in the FaceBook page of Andras Pellionisz]
A surge of top-quality papers pointing into "methylation-defects" as predicted by FractoGene as culprits for cancer
Scientists Report that Methylation Chaos in Cancer Contributes to Cell Adaptability
http://www.genengnews.com/gen-news-highlights/scientists-claim-methylation-chaos-in-cancer-contributes-to-cell-adaptability/81245352/
Scientists claim cancer cells have lost the ordered patterns of methylation demonstrated by normal tissues and demonstrate chaotic methylation across the genome that could help them adapt to changing environments in growing tumors and facilitate metastasis. Research by aJohns Hopkins University-led team has found that in contrast to normal tissues that demonstrate specific patterns of methylation at CpG islands or in large blocks of DNA, cells from a range of different tumor types showed vastly differing methylation patterns at the same sites, which they termed differentially-methylated regions (cDMRs).
In colon cancer this was evidenced by loss of methylation stability both at the boundaries of CpG islands and the presence of blocks of hypomethylation affecting over half the genome, which together led to significant variability in gene expression. Andrew P. Feinberg, M.D., professor of molecular medicine and director of the Center for Epigenetics at the Johns Hopkins University School of Medicine’s Institute for Basic Biomedical Sciences, and colleagues, report their findings in Nature Genetics. In their paper, titled “Increased methylation variation in epigenetic domains across cancer types,” the authors point out that their findings could indicate that the use of nonspecific DNA methylation inhibitors as cancer therapy could have unwanted effects by activating tumor-promoting genes in hypomethylated blocks.
Work by Dr. Feinberg’s team has previously demonstrated that colon cancers exhibit changes in the degree of methylation at regions of lower CpG density near CpG islands. These cDMRs corresponded in the main to the same regions that show DNA methylation variation in normal spleen, liver, and brain tissues, or tissue-specific DMRs (tDMRs), suggesting their involvement in cell differentiation in normal tissues.
To investigate cancer-related changes in global methylation further, the researchers designed an array to analyze over 151 cDMRs that were consistently altered across colon cancer, and compared methylation within these regions in another 290 samples including matched normal and cancer samples from colon, breast, lung, thyroid, and Wilms’ tumor. They found that almost all of the cDMRs were altered across all cancers tested. “Specifically, the cDMRs showed increased stochastic variation in methylation level within each tumor type, suggesting a generalized disruption of the integrity of the cancer epigenome,” the team suggests.
To look further at this phenomenon, the team carried out genome-scale bisulfite sequencing of three colorectal cancers, matched normal colonic mucosa and two adenomatous polyps. The study was designed to obtain methylation estimates with enough precision to detect differences of 20% methylation. The results demonstrated a significant loss of methylation stability in colon cancer, which involved CpG islands and their boundary regions (shores), and also identified large blocks (5010 kb) of hypomethylation. “Remarkably, these hypomethylated blocks in cancer corresponded to more than half of the genome, even after accounting for the number of CpG sites within the blocks,” the authors note.
Over 5,800 hypermethylated and 4,300 hypomethylated small DMRs (less than 5 kb) were also identified. Interestingly, the research comfirmed previous findings that hypermethylated cDMRs are enriched in CpG islands, whereas hypomethylated cDMRs are enriched at CpG island shores. The thousands of small DMRs frequently involved loss of boundaries of DNA methylation at the edge of CpG islands, shifting of DNA methylation boundaries, or the creation of novel hypomethylated regions in CG-dense regions that are not normally recognized as islands. The knock-in effect of this variability in cancer cell methylation was both an increase in gene silencing and a substantial enrichment of genes with increased expression variability in the methylated blocks, Dr. Feinberg and colleagues state.
These data underscore the importance of hypomethylated CpG island shores in cancer, the authors note: “Shores associated with hypomethylation and gene overexpression in cancer are enriched for cell cycle related genes, suggesting a role in the unregulated growth that characterizes cancer.” Regions of altered methylation in cancer were also found to match those in normal tissues associated with controlling cell differentiation into specific cell types. “Targeting those regions might help the cells become more normal,” suggests Rafael Irazarry, Ph.D., professor of biostatistics at the Johns Hopkins University Bloomberg School of Public Health and lead author of the Nature Genetics paper.
From the cancer perspective, methylation chaos is helpful because it means tumors can turn genes on and off in an uncontrolled way, increasing their adaptability, Dr. Feinberg adds. This indicates that increased epigenetic heterogeneity in cancer at cDMRs may play a role in the ability of cancer cells to adapt rapidly to changing tissue environments, such as increased oxygen in regions of neovascularisation, then decreased oxygen with necrosis, or metastasis to a new intercellular milieu.
Current efforts to exploit DNA methylation for cancer screening have focused on identifying narrowly defined cancer-specific profiles. However, the Johns Hopkins research suggests broader evaluation of the cancer epigenome may be more relevant. “Given the importance of boundary regions for both small DMRs and large blocks identified in this study, it will be important to focus future epigenetic investigations on the boundaries of blocks and CpG islands (shores) and on genetic or epigenetic changes in genes encoding factors that interact with them,” the authors conclude.
Measuring DNA Methylation
http://asweetlife.org/a-sweet-life-staff/featured/dna-methylation-the-coolest-new-technology-at-the-ada-scientific-sessions/17772/
What is this promising new technology? A means of “Using Differentially Methylated Circulating DNA for In Vivo Detection of Beta-Cell Death in Diabetes,” as presented by Dr. Eitan Akirav.
Come again? Using differentially methy-what?
Okay, I admit the technology is not very consumer-friendly, but it’s really cool, so bear with me, and we’ll start from the beginning.
Beta-cell death is a problem in both type 1 and type 2 diabetes; in the former, cell death is induced as cells react to their increasingly toxic, auto-reactive environment, and in the latter, cell death is induced as cells become over-stressed. Currently, beta-cell death is very hard to measure. The best way is to look directly at tissue in the pancreas—but that’s not possible if you want to keep your human patient or mouse models alive. Without tissue samples, imaging of a living pancreas would be useful, but that proves difficult and inaccurate because of the location and nature of the pancreas. So, researchers will usually quantify beta-cell death using proxies like C-peptide (as an indicator of how much insulin is being secreted).
These proxies, though, are not reliable or accurate; ideally, if we want to understand exactly what causes beta-cell death and what we can do to manipulate the process, we should be able to measure the degree of beta-cell death while the subject is still living, and without too much indirection.
This sort of problem is not unique to diabetes; researchers face a similar issue with, for example, diagnosing cancers. Many cancers come with tangible symptoms or lumps, but how can you test for internal cancers in asymptomatic patients before it’s too late? Much research has been done to identify unique biological signatures, called biomarkers, for tumors. People started by looking for specific proteins or RNA sequences, but these have not proved reliable indicators of many tumors. More recently, though, a promising new biomarker is being tested—the methylation of DNA fragments circulated freely in the blood [1].
Relationship exists between mutations in tumors and methylation patterns found in their genomes
http://triplehelixblog.com/2011/07/methylation-the-cause-of-brain-tumor/
Methylation: The Cause of Brain Tumor?
When one thinks of the word “cancer” breast cancer, lung cancer, and skin cancer are among the various types that first come to mind. One type of cancer that is often neglected is Brain Tumor. According to the National Tumor Society, more than 500 people per day are diagnosed with primary or metastatic brain tumor and what’s worse is that the mortality rates for those diagnosed with brain and nervous system tumors haven’t improved over the past decade. The desperate need for new treatments and therapies for brain tumor is evident and it is the hope of many people whose loved one have suffered the wrath of this incurable disease that 2011 will bring new treatments and bring the path to the cure closer than ever before [1].
Neuroscience is an area of science that has faced its fair share of failures, yet that doesn’t mean scientists should give up on the field itself. Recently, researchers at the Alpert Medical School made an important discovery that may change the face of brain tumor treatments and diagnosis forever.
This new discovery is developed from the hypothesis that a relationship exists between mutations in tumors and methylation patterns found in their genomes (2). Chemically speaking, methylation is the addition of a methyl group to a substrate or the substitution of an atom or group by a methyl group. When DNA is methylated, gene silencing often occurs which could be the cause of the tumor. Gene silencing is a process of gene regulation which “switches off” a gene through a mechanism. Researchers and neuroscientists speculate that the methylated regions mark the genes involved in metabolic processes which explain the abnormal behavior of tumor cells [2]
New Method Used by Cells to Reverse Silenced Genes Discovered
New Method Used by Cells to Reverse Silenced Genes Discovered http://www.medindia.net/news/New-Method-Used-by-Cells-to-Reverse-Silenced-Genes-Discovered-87312-1.htm#ixzz1ScKlWBcS
Novel mechanism used by body cells to turn on the silenced genes has been identified by researchers at Fox Chase Cancer Center. This process is critical in preventing the development of cancer suggesting the possibility of new therapies that might target the specific changes underlying the disease. The findings will be published online in the journal Cell.
Read more: New Method Used by Cells to Reverse Silenced Genes Discovered http://www.medindia.net/news/New-Method-Used-by-Cells-to-Reverse-Silenced-Genes-Discovered-87312-1.htm#ixzz1ScL7s4vP
The process investigated by Alfonso Bellacosa, M.D., Ph.D., Associate Professor at Fox Chase, and his colleagues, is called methylation, in which the cell chemically tags genes to turn them off. More specifically, the cell silences a gene by adding a chemical compound known as a methyl group; without that methyl group, the gene remains active.
Read more: New Method Used by Cells to Reverse Silenced Genes Discovered http://www.medindia.net/news/New-Method-Used-by-Cells-to-Reverse-Silenced-Genes-Discovered-87312-1.htm#ixzz1ScLKxrKH
"So What?" - if you separate Fractal Defects from Structural Variants of Human Diversity? - In Vivo Genome Editing!
[Zinc Finger Nucleases inserted into intron ("Junk"...) fix Hemophilia in Mouse - AJP]
Genome editing, a next step in genetic therapy, corrects hemophilia in animals.
Using an innovative gene therapy technique called genome editing that hones in on the precise location of mutated DNA, scientists have treated the blood clotting disorder hemophilia in mice. This is the first time that genome editing, which precisely targets and repairs a genetic defect, has been done in a living animal and achieved clinically meaningful results. As such, it represents an important step forward in the decades-long scientific progression of gene therapy -- developing treatments by correcting a disease-causing DNA sequence.
In this new study, researchers used two versions of a genetically engineered virus (adeno-associated virus, or AAV) -- one carrying enzymes that cut DNA in an exact spot and one carrying a replacement gene to be copied into the DNA sequence. All of this occurred in the liver cells of living mice. "Our research raises the possibility that genome editing can correct a genetic defect at a clinically meaningful level after in vivo delivery of the zinc finger nucleases," said the study leader, Katherine A. High, M.D., a hematologist and gene therapy expert at The Children's Hospital of Philadelphia. High, a Howard Hughes Medical Institute Investigator, directs the Center for Cellular and Molecular Therapeutics at Children's Hospital, and has investigated gene therapy for hemophilia for more than a decade.
The study appeared online today in Nature.
High's research, a collaboration with scientists at Sangamo BioSciences, Inc., makes use of genetically engineered enzymes called zinc finger nucleases (ZFNs) that act as molecular word processors, editing mutated sequences of DNA. Scientists have learned how to design ZFNs custom-matched to a specific gene location. ZFNs specific for the factor 9 gene (F9) were designed and used in conjunction with a DNA sequence that restored normal gene function lost in hemophilia. By precisely targeting a specific site along a chromosome, ZFNs have an advantage over conventional gene therapy techniques that may randomly deliver a replacement gene into an unfavorable location, bypassing normal biological regulatory components controlling the gene. This imprecise targeting carries a risk of "insertional mutagenesis," in which the corrective gene causes an unexpected alteration, such as triggering leukemia.
In hemophilia, an inherited single-gene mutation impairs a patient's ability to produce a blood-clotting protein, leading to spontaneous, sometimes life-threatening bleeding episodes. The two major forms of the disease, which occurs almost solely in males, are hemophilia A and hemophilia B, caused respectively by a lack of clotting factor VIII and clotting factor IX. Patients are treated with frequent infusions of clotting proteins, which are expensive and sometimes stimulate the body to produce antibodies that negate the benefits of treatment. In the current study, the researchers used genetic engineering to produce mice with hemophilia B, modeling the disease in people. Before treatment, the mice had no detectable levels of clotting factor IX. Previous studies by other researchers had shown that ZFNs could accomplish genome editing in cultured stem cells that were then injected into mice to treat sickle cell disease. However, this ex vivo approach is not feasible for many human genetic diseases, which affect whole organ systems. Therefore the current study tested whether genome editing was effective when directly performed in vivo (in a living animal). High and colleagues designed two versions of a vector, or gene delivery vehicle, using adeno-associated "Genome editing, a next step in genetic therapy, corrects hemophilia in animals." PHYSorg.com. 26 Jun 2011. http://www.physorg.com/news/2011-06-genome-genetic-therapy-hemophilia-animals.html Page 1/2 virus (AAV). One AAV vector carried ZFNs to perform the editing, the other delivered a correctly functioning version of the F9 gene. Because different mutations in the same gene may cause hemophilia, the process replaced seven different coding sequences, covering 95 percent of the disease-carrying mutations in hemophilia B.
The researchers injected mice with the gene therapy vector, which was designed to travel to the liver—where clotting factors are produced. The mice that received the ZFN/gene combination then produced enough clotting factor to reduce blood clotting times to nearly normal levels. Control mice receiving vectors lacking the ZFNs or the F9 minigene had no significant improvements in circulating factor or in clotting times. The improvements persisted over the eight months of the study, and showed no toxic effects on growth, weight gain or liver function, clues that the treatment was well-tolerated. "We established a proof of concept that we can perform genome editing in vivo, to produce stable and clinically meaningful results," said High. "We need to perform further studies to translate this finding into safe, effective treatments for hemophilia and other single-gene diseases in humans, but this is a promising strategy for gene therapy." She continued, "The clinical translation of genetic therapies from mouse models to humans has been a lengthy process, nearly two decades, but we are now seeing positive results in a range of diseases from inherited retinal disorders to hemophilia. In vivo genome editing will require time to mature as a therapeutic, but it represents the next goal in the development of genetic therapies."
[The Nature paper concludes:
Studies showing that ZFNs can mediate gene correction efficiently through the introduction of site-specific DSBs, and can induceHDRin cultured cells, have provided important proof-of-concept results for the clinical application of engineered nucleases for diseases affecting cells that can be removed and returned to the patient. However, the necessity to isolate and manipulate cells ex vivo limits the application of this technology to a subset of genetic diseases. Our results show that AAV-mediated delivery of a donor template and ZFNs in vivo induces gene targeting, resulting in measurable circulating levels of factor IX.
This therapeutic strategy is sufficient to restore haemostasis in a mouse model of haemophilia B, thus demonstrating genome editing in an animal model of a disease. Clinical translation of these results will require optimization of correction efficiency and a thorough analysis of off-target effects in the human genome, an issue that we have begun to monitor. Together, these data show that AAV-mediated delivery of ZFNs and a donor template gives rise to persistent and clinically meaningful levels of genome editing in vivo, and thus can be an effective strategy for targeted gene disruption or in situ correction of genetic disease in vivo.]
[When presenting paradigm-shifts, the most American question is "so what?". To be able to separate Fractal Defects (wherein the genome violates its own mathematics) from at least 4 million Structural Variants that only reflect "Human Diversity" (with the fractality intact) is nice. However, in the not-so-distant future when Big Pharma delivers drops of liquid containing a harmless virus that carries a "patch" to fix genomic glitches, not unlike when your computer updates lethal Windows defects by simply patching them, will make a similar, but multiple quantum leaps like when Polyo was rendered harmless - this entry can be discussed in FaceBook of Andras Pellionisz]
23andMe-Led Team Reports on Findings from Web-Based Parkinson's GWAS
June 24, 2011
By a GenomeWeb staff reporter
NEW YORK (GenomeWeb News) - In a paper appearing online last night in PLoS Genetics [See Excerpts and link below - AJP], researchers from the personal genetics firm 23andMe and the Parkinson's Institute in California reported that they have identified two new loci contributing to Parkinson's disease risk.
The team's web-based genome-wide association study approach involved 3,426 individuals with Parkinson's disease - enrolled over the span of a year-and-a-half through a collaboration between 23andMe, the Michael J. Fox Foundation, the National Parkinson Foundation, and the Parkinson's Institute and Clinical Center - and 29,624 unaffected control individuals who enrolled as customers of 23andMe's personal genome services.
Using custom Illumina HumanHap 550+ arrays to genotype these individuals, researchers not only verified 20 Parkinson's associations identified previously, but also detected two new risk loci - one SNP falling near the lysosomal integral membrane protein type 2 coding gene SCARB2 and another in the vicinity of the SREBF1 and RAI1 genes. Two other variants - in and around the RIT2 gene and the USP25 gene - fell just shy of significantly associating with the disease.
"Not only are these new genetic findings significant, but we've also shown that the data collected by 23andMe support discovery of new associations as well as replication of previously known associations," lead author Chuong Do, a research scientist at 23andMe, said in a statement.
"This study is a rigorous 'proof of principle,'" he added, "and clearly demonstrates that web-based phenotyping works for a disease of real public health significance."
Based on their findings so far, the team estimated that at least a quarter of Parkinson's disease risk can be attributed to genetic factors. Consequently, they say, additional studies are needed to further tease apart the genetic environmental contributors to the disease.
23andMe's Parkinson's disease cohort now consists of more than 5,000 individuals with the condition, the company said. Meanwhile, some 76,000 individuals who use the 23andMe database have reportedly consented to participate in the Parkinson's disease or similar 23andMe research projects.
For instance, the company is currently using a similar web-based approach to study sarcoma, using a cohort that includes more than 500 individuals with that disease.
"We believe this paper proves the potential of our approach of combining genetic information with web-based data about specific conditions to make novel research discoveries," Anne Wojcicki, president and CEO of 23andMe, said in a statement. "This approach has the potential to be used in many other conditions."
[Excerpts and Link of the PLOS paper - AJP]
Chuong B. Do1*, Joyce Y. Tung1, Elizabeth Dorfman1, Amy K. Kiefer1, Emily M. Drabant1, Uta Francke1, Joanna L. Mountain1, Samuel M. Goldman2, Caroline M. Tanner2, J. William Langston2, Anne Wojcicki1, Nicholas Eriksson1*
We conducted a large genome-wide association study (GWAS) of Parkinson's disease (PD) with over 3,400 cases and 29,000 controls (the largest single PD GWAS cohort to date). We report two novel genetic associations and replicate a total of twenty previously described associations, showing that there are now many solid genetic factors underlying PD. We also estimate that genetic factors explain at least one-fourth of the variation in PD liability, of which currently discovered factors only explain a small fraction (6%-7%). Together, these results expand the set of genetic factors discovered to date and imply that many more associations remain to be found. Unlike traditional studies, participation in this study took place completely online, using a collection of cases recruited primarily via PD mailing lists and controls derived from the customer base of the personal genetics company 23andMe. Our study thus illustrates the ability of web-based methods for enrollment and data collection to yield new scientific insights into the etiology of disease, and it demonstrates the power and reliability of self-reported data for studying the genetics of Parkinson's disease.
We found two novel associations at a genome-wide level of significance near SCARB2 (rs6812193) and SREBF1/RAI1 (rs11868035), both of which were replicated in data from [23]. We also report two novel associations (near RIT2 and USP25) just under the level of significance, one of which (RIT2) was also replicated. While it is difficult to pinpoint any causal genes from a GWAS, there are a few biologically plausible candidates worthy of discussion.
The PD-associated SNP rs6812193 lies in an intron of the FAM47E gene, which gives rise to multiple alternatively spliced transcripts, many of which are protein-coding; the functions of these hypothetical proteins are unknown. A more attractive candidate, located kb centromeric to the SNP, is SCARB2 (scavenger receptor class B, member 2), which encodes the lysosomal integral membrane protein type 2 (LIMP-2). LIMP-2 deficiency causes the autosomal-recessive disorder Action Myoclonus-Renal Failure syndrome (AMRF), which combines renal glomerulosclerosis with progressive myoclonus epilepsy associated with storage material in the brain [34]. LIMP-2 is involved in directing -glucocerebrosidase to the lysosome where it hydrolyzes the -glycosyl linkage of glucosylceramide [35]. Deficiency of this enzyme due to mutations in its gene (GBA) causes the most common lysosomal storage disorder, Gaucher's disease. Recently, mutations in GBA have also been identified in PD [36], pointing to a possible functional link between the newly identified candidate gene SCARB2 and PD.
rs11868035 appears in an intron of the alternatively spliced gene, SREBF1 (sterol regulatory element-binding transcription factor 1), within the Smith-Magenis syndrome (SMS) deletion region on 17p11.2. SREBF1 encodes SREBP-1 (sterol regulatory element-binding protein 1), a transcriptional activator required for lipid homeostasis, which regulates cholesterol synthesis and its cellular uptake from plasma LDL [37]. Studies of neuronal cell cultures have implicated SREBP-1 as a mediator of NMDA-induced excitotoxicity [38]. rs11868035 is directly adjacent to the acceptor splice site for the C-terminal exon of the SREBP-1c isoform of the protein [39], suggesting that the effect of the polymorphism may be specifically related to the splicing machinery for this protein. The mutation is also in strong LD with rs11649804, a nonsynonymous variant in the nearby gene RAI1 (retinoic acid-induced protein 1), which regulates transcription by remodeling chromatin and interacting with the basic transcriptional machinery. Heterozygous mutations in RAI1 reproduce the major symptoms of SMS, such as developmental and growth delay, self-injurious behaviors, sleep disturbance, and distinct craniofacial and skeletal anomalies [40]. Future work is needed to identify the functionally important variant(s) responsible for this association.
The SNP rs4130047, slightly below the genome-wide significance threshold, lies in an intron of the RIT2 (Ras-like without CAAX 2) gene that encodes Rit2, a member of the Ras superfamily of small GTPases. Though we do not claim this SNP as a confirmed replication, there are a number of reasons to suspect that this association may also be real. Rit2 binds calmodulin in a calcium-dependent manner, and is thought to regulate signaling pathways and cellular processes distinct from those controlled by Ras [41]. It localizes to both the nucleus and the cytoplasm. Independent of our study, RIT2 was previously proposed as a candidate gene for PD, based on the possibility that dopaminergic neurons may be especially vulnerable to high intracellular calcium levels, perhaps through an interaction with -synuclein [42]. The PD-associated region contains another biologically plausible candidate gene, SYT4 (synaptotagmin IV), which encodes synaptotagmin-4, an integral membrane protein of synaptic vesicles thought to serve as sensor in the process of vesicular trafficking and exocytosis. It is expressed widely in the brain but not in extraneural tissues [43]. Homozygous Syt4−/− mouse mutants have impaired motor coordination [44]. SYT4 is particularly interesting as a SNP near SYT11 (synaptotagmin XI) has been associated with PD in [22], and the encoded protein, synaptotagmin-11, is known to interact with parkin [45].
The suggestively associated SNP rs28233572 lies in a gene-poor region with only one candidate gene downstream, USP25, encoding ubiquitin specific peptidase 25, which regulates intracellular protein breakdown by disassembly of the polyubiquitin chains. Other ubiquitin-specific proteases (USP24, USP40) have been proposed as candidate genes for PD [46] (although USP24 fails to replicate here, see Table 3).
Our heritability estimates, which suggest that genetic factors account for at least one-fourth of the total variation in liability to PD, represent the tightest confidence bounds determined for the heritability of PD to date. These estimates, which rely on observed genetic sharing rather than predicted relationship coefficients, avoid confounding from shared environmental covariance by restricting attention to very distantly related individuals. Furthermore, they complement estimates of heritability from twin studies by considering large numbers of individuals with low amounts of genetic sharing, rather than small numbers of twin pairs with large amounts of genetic sharing.
These estimates should only be interpreted as lower bounds on the actual heritability of liability of PD for two reasons. First, they only reflect phenotypic variation due to causal variants in LD with SNPs on the genotyping platform. Second, they only capture the contribution to additive variance that arises from a polygenic model of many SNPs of small effect, but do not include the variance arising from known specific associations. This limitation is most apparent in our estimate of heritability based on only early-onset cases (), which is considerably lower than reported in prior twin studies (e.g., in [10]). In early-onset PD, mutations in six specific genes (SNCA, PRKN, PINK1, DJ1, LRRK2, and GBA) have been reported to account for 16% of cases [47]; these specific mutations are not directly accounted for in our estimate, which is based on a polygenic model. We note that a similar effect may explain the low heritability estimate for early-onset PD in [48]. Thus, the actual heritability of PD, and the corresponding true upper bound on discriminative accuracy achievable through genetic factors, may be even higher than the estimates we provide.
Our estimates also indicate a substantial genetic component for late-onset PD (), for which previous estimates of heritability have been inconclusive due to the lack of statistical power (e.g., 0.068 in [10] and 0.453 in [48]). One might ask, if late-onset PD is indeed so heritable, why do cases frequently appear sporadically in the general population? Following the analysis of [49], if one were to assume a heritability of and an average of three children per family, then the proportion of sporadic cases (i.e., no parent, child, sibling, grandparent, aunt or uncle, or first cousin with PD) among all PD cases would be 64% for a prevalence of ; in the 23andMe cohort, 69% of PD cases would be considered sporadic by this definition based on self-reported family history. Similarly, the expected proportion of PD cases with no affected parent or sibling would be 88% under the same assumptions, compared with 84% as reported in [50], or 89% based on the cohort in [51]. These examples illustrate the fact that the presence or absence of a familial pattern cannot always be used to determine pathogenesis, especially for diseases that are rare and have a complex etiology.
Overall, our risk prediction results are consistent with a measured AUC of roughly 0.6. The cross-validated AUCs presented here should be distinguished from more usual measurements of AUC in genome-wide association studies, which are typically only estimated on the development set, and which rely on weighted combinations of SNPs with independently estimated odds ratios. In some cases, the bias resulting from lack of proper external validation can be quite large. For example, a simple genetic profile score based on multiplying together odds ratios for the SNPs in Table 2 appears to achieve an AUC of in the 23andMe data (or if no covariate adjustment is performed) making it appear competitive with some of the best models described in Table 5. However, when the same model is evaluated in the NINDS data, the AUC drops to , exhibiting a drop in performance characteristic of models that have been overfit to their training data. In contrast, the consistency between the internal and external validation results in the models shown in Table 5 demonstrate not only the predictiveness of our models within the 23andMe cohort but also their ability to generalize to other populations.
Our empirical demonstration that including SNPs beyond the genome-wide significant level provides improved discriminative power mirrors the recent results of [32], which also studied the performance of sparse regression methods in a risk prediction setting. In an applied setting where the goal is to achieve the best predictive accuracy rather than to isolate the contribution of individual genetic factors, however, even higher discriminative accuracies may be possible if one were to incorporate these covariates as part of the predictive models. Even without these, however, significant improvements in risk prediction are likely still possible, with our heritability analyses indicating asymptotic target AUCs above 0.8.
Our AUCs are generally conservative for a number of reasons. In the internal experiments, they were obtained by training on only 80% of the data. In the external experiments, the models included only the SNPs in common between the 23andMe and NINDS datasets and thus excluded several SNPs with large effects in LRRK2 and GBA that may add a percent or more to the AUC if included. Furthermore, our analyses adjusted for confounding from population structure and other covariates so as to ensure that the discriminative accuracies we reported were specifically due to genetic effects.
Finally, we note that data for the 23andMe cohort used in this study were acquired in a novel manner, using genotype and survey data acquired through a commercial online personal genetic testing service. The use of self-reported phenotype data raised some unique challenges. For example, our cohort was not a true population sample for a number of reasons, such as the general bias toward higher socioeconomic status, as typical of 23andMe customers. In general, however, we would not expect these ascertainment biases to substantially affect our conclusions unless their effects varied differentially between the case and control sets.
As another example, in compiling the cohort, we used participants with varying levels of completeness in their self-reported data (see Materials and Methods). Out of the 3,426 cases in the 23andMe cohort, though most cases reported having PD in a questionnaire, 482 affirmatively stated they had PD upon entry to the research study but did not fill out any PD-related questionnaire during the study. However, we did not see a large difference between those answering questions and not. Among the 11 associations presented in Table 2, only the association with MAPT showed a significant difference between the cohort who answered a questionnaire and those who did not (see Table S7). Also, approximately 84% of the cases filled out a questionnaire, and of them, over 96% reported a PD diagnosis. Even if a larger fraction (say 10–15%) of those who did not take a questionnaire did not have PD, the gain in power from the additional cases would more than offset the loss of power from having some 50 more false positive cases.
Despite the challenges associated with using self-reported data collected through online surveys, ultimately, our results lend credibility to the accuracy of this novel research design. For example, the agreement between our study and previous studies in terms of the ORs estimated for the 19 associations replicated in Table 3 strongly suggests that our cohort is similar to those used in other PD studies. Similarly, the consistency of AUCs and heritability estimates across our cohort and the NINDS cohort both suggest a limited role of bias in our study.
Importantly, our mode of data collection also provided a number of clear benefits. The use of internet-based techniques enabled rapid recruitment of a large patient community. The 3,426 cases in this study were enrolled in about 18 months, with over half joining in the first month of the study. Also adding significantly to the power and robustness of this study was the availability of a large cohort of controls derived from the 23andMe customer base. By using a non-traditional recruitment approach, we thus were able to attain good power for our study through large sample sizes. To our knowledge, this study represents the largest genome-wide association study of Parkinson's disease conducted on a single cohort to date, with only a recent meta-analysis achieving a larger number of cases [22]. We suggest that this methodology for study design may prove advantageous for other conditions where the advantage of having a large cohort is paramount for detecting subtle genetic effects.
...
In summary, we have for the first time used a rapid, web-based enrollment method to assemble a large population for a genome-wide association study of PD. We have replicated results from numerous previous studies, providing support for the utility of our study design. We have also identified two new associations, both in genes related to pathways that have been previously implicated in the pathogenesis of PD. Using cross-validation, we have provided evidence that many suggestive associations in our data may also play an important role. Using recently developed analytic approaches developed for GWAS that take into account the ascertainment bias inherent in a case-control population, we have estimated the genetic contribution to PD in this sample. These findings confirm the hypothesis that PD is a complex disorder, with both genetic and environmental determinants. Future investigations, expanded to include environmental as well as genetic factors, will likely further refine our understanding of the pathogensis of PD, and, ultimately, lead to new approaches to treatment.
Researchers Develop Methylation-Based Model for Predicting Age from Spit DNA
June 23, 2011
By a GenomeWeb staff reporter
NEW YORK (GenomeWeb News) – In a study appearing online last night in PLoS ONE, a University of California at Los Angeles research team demonstrated that it's possible topredict a person's age from saliva samples using DNA methylation patterns.
Those involved in the study say the findings could have both forensic applications and medical implications, particularly for finding individuals whose biological age differs from their chronological age.
The researchers used microarrays to look at DNA methylation patterns in spit samples from nearly three-dozen male twin pairs between the ages of 21 and 55 years old, tracking down nearly 90 sites where methylation coincides with age. From follow-up experiments involving another group of men and women between the ages of 18 and 70 years old, the team was able to come up with a model that predicts an individual's age to within an average of five years based on methylation status at two sites in the genome.
"Our approach supplies one answer to the enduring quest for reliable markers of aging," senior author Eric Vilain, a professor of human genetics at the University of California at Los Angeles who is also affiliated with the school's Center for Society and Genetics, said in a statement. "With just a saliva sample, we can accurately predict a person's age without knowing anything else about them."
DNA modifications change with tissue development, differentiation, and age, Vilain and his co-authors explained. For the current study, they looked at whether it was possible to exploit these shifts to find age markers, focusing on methylation at cytosine residues — first in identical twins and then in unrelated individuals from the general population.
"While certain methylation changes are genetically controlled, environmental exposure and stochastic processes can lead to a change in methylation patterns," they explained. "In this context, identical twins can be considered replicates of the same developmental and aging experiment."
Using Illumina HumanMethylation27 arrays, the researchers assessed methylation patterns at nearly 16,100 CpG sites in the genomes of 34 pairs of identical male twins between the ages of 21 and 55 years old.
When they sifted through the data for the twins using an analytical approach known as weighted correlation network analysis, the team found five methylation modules containing loci with comparable methylation patterns.
Within the modules, researchers narrowed in on 88 loci at which cytosine methylation status depended on an individual's age, including 69 showing positive correlation and 19 showing negative correlation. The sites fell in and around 80 different genes, they noted, including several genes that have been implicated in cardiovascular, neurological, and other conditions.
Because methylation at three sites in the promoter regions of the EDARADD, TOM1L1, and NPTX2 genes were particularly well correlated with age, the team used either targeted bisulfite sequencing or Sequenom MassArrays to evaluate CpG methylation profiles for these three genes in DNA from saliva samples from 22 of the twins and from another 31 men and 29 women who were between 18 and 70 years old.
Although methylation status for all three genes corresponded to age in the DNA from the male saliva samples, the researchers reported, methylation profiles for just two of these — EDARADD and TOM1L1 — coincided with age in the females.
Meanwhile, the team's predictive model, based on methylation profiles for cytosine residues near the EDARADD and NPTX2 genes, explained some 73 percent of the age variance and could predict age to within a 5.2 year window based on average.
"[O]ur ability to predict an individual's age to an average accuracy of 5.2 years could be used by forensic scientists to estimate a person's age based on a biological sample alone, once the model has been tested in various biological tissues," the study authors wrote.
And, they say, such predictive models may find favor for diagnosing and treating some age-related diseases and for finding situations in which an individual's biological age differs from their age in years.
"Doctors could predict your medical risk for a particular disease and customize treatment based on your DNA's true biological age, as opposed to how old you are," Vilain said in a statement.
Epigenetic Predictor of Age [Full free text of PLOS]
Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, et al. (2011) Epigenetic Predictor of Age. PLoS ONE 6(6): e14821. doi:10.1371/journal.pone.0014821
Published: June 22, 2011
Abstract
From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites—in the promoters of the EDARADD, TOM1L1, and NPTX2 genes—is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.
Introduction
Throughout development, cells and tissues differentiate and change as the organism ages. This includes alterations to telomeres, accumulation of DNA mutations, decay of cellular and organ structures, and changes in gene expression [1]. Both differentiation of tissues, and ageing effects are at least partially caused by chemical modifications of the genome, such as DNA methylation.
Monozygotic (MZ) twins form an attractive model to study methylation changes with age. At the time of separation both embryos have nearly identical methylation patterns. While certain methylation changes are genetically controlled, environmental exposure and stochastic processes can lead to a change in methylation patterns. In this context, identical twins can be considered replicates of the same developmental and ageing experiment.
Several studies have investigated the epigenetic state of a small number of selected genes or CpG islands in subjects of varying age [2] or have measured the global changes in DNA methylation with increasing age [3]. Recently, unbiased genomewide studies have documented age effects on DNA methylation in cultured cells [4], mice [5], and humans [6], [7], [8]. Most of these reports' subjects were of a limited age range, and the continuity of the age related changes has been unclear. Therefore, estimating the age of a biological sample based on methylation values has not been possible.
[This paper, reporting results that the authors bumped into something quite different that they were looking for, stands out in significance far beyond what the authors claim in their PLOS paper. Their claim is focused on the predictability of "biological age". This is very noteworthy, but pales in comparison with the modern and now clinched fact that the genome is NOT "static", but changes (as its function is regulated in a recursive manner, see book below and references to two reversed axioms of genome informatics). Monitoring the "aging process" - that is rendering auxiliary information unreadable upon perusal in recursion, is a physiological process; now proven by this experimental finding. However, the pattern of change of methylation, as e.g. shown in Google Tech YouTube can also be pathological (e.g. cancerous) - thus the Genome (mis)Regulation by Fractal Iterative Recursion, a technology IP of HolGenTech, Inc. is now even firmer on experimentally verified ground. This entry can be discussed on the FaceBook page of Andras Pellionisz]
What the new field of epigenetics reveals about how DNA really works.
By Christine Kenneally
Posted Monday, June 20, 2011, at 9:52 AM ET
There are almost as many metaphors for genes as there are genes. One of the most familiar, and the hardest to let go of, is the tidy blueprint, at once reassuringly clear and oppressively deterministic: Our genome is the architectural plan for who we are. It tells our body how to build itself, setting our height, our health, and even our moods since before we are born. Small wonder that we imagine if we can read our genome, we will discover not just the truth of ourselves but perhaps our future, too. Remember the high hopes that spurred on the Human Genome Project in the 1990s? Though the genetic catalog is now largely complete, we still await many of the anticipated insights, and in Epigenetics: The Ultimate Mystery of Inheritance, Richard Francis, a writer with a biology Ph.D., traces the emergence of a different genetic paradigm. Our DNA shapes who we are, Francis reports from the research forefront, but it is far from a static plan or an inflexible oracle; DNA gets shaped, too. For good or ill, the forces that determine our fate can't be captured by anything so neat as a blueprint.
Francis's primer introduces a new field, whose roots predate the rise of pure genetic determinism. How is DNA itself shaped? The search for answers begins in the late-19th-century work of scientists such as Hans Driesch, whose study of sea urchin embryos revealed that the cell plays a key administrative role in an organism's development. He discovered that if you take cells from one location in the embryo—the area that will become, say, the spines--and plant them in another—the mouth area--their function changes: You don't get spines growing out of the mouth, you get a normal mouth. A cell's identity doesn't arise from a preordained genetic recipe inside it. Crucially, it is the cues that a cell gets from neighboring cells that affect how the genes inside it behave.
Epigenetics has taken its cue from this process, and sets out to explore not just how cells control the genes inside them but also how altered genes are passed on when cells reproduce—both within an organism's lifetime and, more fantastically, across generations. If you detect another historical antecedent, you're right. Looming over this new field is the once-derided Lamarck, who proposed in the 18th century that if a giraffe, for example, consistently stretches its neck to reach leaves, its children will be born with longer necks. Lamarck's ideas about how traits are acquired and passed down were mostly wrong. But the basic notion that an event in a parent's life can sculpt fundamental traits in a child, once consigned to the dustbin of biology, has been revived. The epigenetic quest is to discover how chemical attachments to genes shape the fate of an animal by altering the genes' long-term expression.
If cellular regulation of genetic expression sounds complicated, it is, which is one reason—aside from our allegiance to the idea of some foreordained pattern to our lives—the epigenetic field has been slow to develop. The research that has been accumulating for decades upends the conception of "controller" genes that are either "on" or "off." Francis is a thorough guide to the many ways in which personality and health can play out through our genes but not be coded for in DNA. He proceeds step-by-step. After all, this is unsettling terrain: The notion of environmental forces that can be genetically determining does not fit our deeply etched nature vs. nurture categories. Francis begins by explaining what he calls "garden variety," or short term—rather than epigenetic—gene regulation, by way of androgens, like testosterone. This happens in normal development, but also in abnormal situations, such as when athletes abuse steroids. Where normal testosterone changes gene expression, extra testosterone causes a frantically altered gene expression, which leads to strong muscles, shrunken testes, and out-of-control aggression. The changes are direct. You take the steroids, they affect some of the cells in your body, the gene activity inside those cells changes, and then your body changes. The changes in garden-variety gene regulation end with the affected cells, and with you. When cells divide they do not pass along the abnormal genetic activation. The children of a steroid abuser inherit their parent's genes, but they do not inherit the synthetic steroid-induced changes to gene expression.
But gene expression doesn't change merely when you put chemicals in your body. The connections between people may shape it, too. In the 1990s, scientists began to explore how social status can influence biology. In one kind of African cichlid fish, for example, the males are either "territory-owning" tough guys who have vividly bright colors, huge testes, larger neurons, and lots of testosterone. Or they are nonterritorial and much less striking. The low-testosterone males do not get to breed. Scientists discovered they could manipulate the social status of fish, their testosterone level and all the hoopla that accompanies it, by changing only the fish's "friends." If they put big, territorial fish in a tank with much bigger, territorial males, the former-breeders lost color, their testes and neurons shrank, and they literally transformed into nondominant fish. When they put nonterritorial wallflowers in a tank with females and smaller males, they too were transformed, but in the other direction. As Francis points out, we obviously can't run this kind of experiment with humans. It nevertheless shows how context can change the way genes work.
Changes that arise from normal gene regulation happen in the short term, but epigenetic changes alter the way that genes react to the world for a very long time—even when the original cause has vanished. It is this rather shocking long-term influence that makes epigenetics one of most alluring—and terrifying—shifts in how we think about our genes. Epigenetic changes can occur in adulthood, in childhood, even in utero (a phenomenon explained in Origins by Annie Murphy Paul), with the consequence that an event you experienced as a child could dictate the ways your genes behave in a different situation as an adult. It may have been simple-minded to assume that we are programmed by our genes, but there was a weird egalitarianism in that: Even if we get different genes to begin with, we are under their sway in the same way. Epigenetic change means that not only do we start out as unwitting participants in a genetic lottery, but environmental forces we cannot see or control can mess with our genetic hardware and change our destiny. At the level of DNA, epigenetic change occurs when particular chemicals become attached to the gene, and stay there, altering how the gene behaves. The first of these attachments to be discovered, and still the best known, is from the methyl group. In 1980, it was shown that different degrees of methylation can alter gene expression in different ways. Demethylation can cause problems, too. Depending on the genes involved, one consequence can be unconstrained cell division, otherwise known as cancer.
The causes of epigenetic attachments are various, and the evidence so far indicates they range from pollution to stressful social interactions. Studies on the long-term effects of a pregnant woman's poor nutrition suggest that the food our mothers eat while we are in the womb can shape our gene expression. So, too, the food they don't eat. The best data on long-term genetic change come from the terrible Dutch famine of 1944, when the Nazis blockaded food supplies, disrupted transport, and flooded farmlands in western Holland. It has emerged as the classic case study in the field, thanks to the exemplary record-keeping of the Dutch, which gives researchers solid longitudinal data on the famine's many far-reaching effects. For children who were in utero at the time of the famine, the consequences include a higher risk of schizophrenia, antisocial personality disorder and other psychological disturbances, and even 50 years down the road, a greater likelihood of becoming obese. At first glance it may seem that the legacy is poor health in general. But that's not how it works. The impact depends on exactly when the fetus was exposed to the famine, Francis reports. Women whose mothers suffered the famine in the first trimester have a higher risk of breast cancer. Those whose mothers suffered in the second trimester have problems with lungs and kidneys.
The first person to realize what a data trove the Dutch records were was Clement Smith, a U.S. doctor who was flown to the Netherlands in 1945 to help. He found that children born during the famine were much smaller than those born before. Numerous teams have revisited the data, which have been updated during the decades since, and they have discovered many ways the famine is still playing out in the lives of Dutch people, even those who weren't born at the time. The studies became epigenetic 20 years ago, when scientists began to look for altered genes in famine survivors to see whether changed DNA explains the ways in which the survivors differ.
In 2009, one team unearthed a tantalizing result: Examining the blood cells of adults who were in the womb at the time of the famine, researchers discovered unusual epigenetic attachments on the gene that codes for a hormone called insulin-like growth factor 2. The hormone is crucial for growth, particularly in fetuses. It turns out the IGF2 gene of the famine group is methylated to a different degree than the same gene in a non-famine group. Even though scientists haven't yet traced the specific causal chain between the epigenetic attachments, the genes, and people's lives, those attachments are a smoking gun for epigenetic change in the womb, and health issues many decades later.
Even more fascinating, and unnerving, it appears that the consequences of epigenetic change may stretch over several lifetimes. In one Swedish village, which also has records of crop harvests that go back hundreds of years, the paternal grandsons of men who experienced famine were less likely to have cardiovascular disease than their peers whose paternal grandfathers did not experience famine. But, wait, conventional wisdom says only genes are supposed to be passed on to the next generation. Most epigenetic attachments are stripped away from genes in the creation of sperm and egg cells. Yet it seems that a record of some epigenetic attachments is passed on and then recreated in the genome of the embryo, too. That means that an event in your parent's life that occurred before you were conceived could affect how your genes work today. In other words, the sins of the fathers may be visited on the deoxyribose nucleic acids of the sons. How malleable are our sons and daughters? The mechanisms involved are extraordinarily subtle. Researchers are now only beginning to understand how and why this happens.
It's almost enough to make one nostalgic for the simplicity of old-style genetic determinism, which at least offered the sense that the genetic hand you were dealt at birth was the same one you would play your whole life—except that epigeneticists hold out the promise that the blessings of a single life, too, can be passed on. Disease researchers, Francis reports, have hopes that the effects of abnormal epigenesis may be reversed. For example, it's possible that the damage caused by many cancers is epigenetic. If those epigenetic attachments can be altered, then it's possible the cancer can be stopped. Still, even if we are discovering that an extraordinary range of conditions may be epigenetic, not all of them are. There are still specific diseases that follow a deterministic path. If you are unlucky enough to draw the Huntington's mutation in the genetic shuffle, you will develop the disease. Francis rightly emphasizes the wonder of epigenetics and the molecular rigor it brings to the idea that life is a creative process not preordained by our genome any more than it is preordained by God. Yet even as epigenetic research invites dreams of mastery—self-creation through environmental manipulation—it also underscores our malleability. There is no easy metaphor for this combination. But if we must have one, we should at least start with the cell, not the gene. The genome is no blueprint, but maybe the cell is a construction site, dynamic, changeable, and complicated. Genes are building materials that are shaped by the cell, and they in turn create materials used in the cell. Because the action at the site is ongoing, a small aberration can have a small effect, or it can cascade through the system, which may get stuck. Recall that your body is a moving collection of these building sites, piled in a relatively orderly way on top of another. Malleability? It's an ongoing dance with chaos, but, incredibly, it works.
[Epigenomics, working together with Genomics [hence called HoloGenomics] was exposed in the peer-reviewed science paper The Principle of Recursive Genome Function , invoking "fractal iterative recursion" that is called by Francis "cascading through the system", "dance with chaos" - which nonlinear dynamics (chaos and fractals being the two sides of the same coin) are demonstrably working in complex systems of biology. The concept by reversing two mistaken axioms that blocked progress for over half a Century were also popularized in Google Tech Talk YouTube, both in 2008. - This entry may be discussed in the FaceBook page of Andras Pellionisz]
Cells may stray from 'central dogma'
Published online 19 May 2011 | Nature | doi:10.1038/news.2011.304
Erika Check Hayden
The ability to edit RNA to produce 'new' protein-coding sequences could be widespread in human cells.
[Crick's own handwritten "Dogma" ruled 1956-2008 - see website, peer-reviewed science paper, Google Tech Talk YouTube - AJP]
All science students learn the 'central dogma' of molecular biology: that the sequence of bases encoded in DNA determines the sequence of amino acids that makes up the corresponding proteins. But now researchers suggest that human cells may complicate this tidy picture by making many proteins that do not match their underlying DNA sequences.
In work published today in Science1, Vivian Cheung at the University of Pennsylvania in Philadelphia and her team report that they have found more than 10,000 places where the base (A, C, G or U) in a cell's RNA messages is not the one expected from the DNA sequences used to make the RNA read-out. When some of these 'mismatched' RNAs were subsequently translated into proteins, the latter reflected the 'incorrect' RNA sequences rather than that of the underlying DNA.
It was already known that some cells 'edit' RNA after it has been produced to give a new coding sequence, but the new work suggests that such editing occurs much more often in human cells than anyone had realized, and that hitherto unknown editing mechanisms must be involved to produce some of the changes observed. If the finding is confirmed by other investigators — and some scientists already say they see the same phenomenon in their own data — it could change biologists' understanding of the cell and alter the way researchers study genetic contribution to disease.
Editing the central dogma
"The central dogma says that there is faithful transcription of DNA into RNA. This challenges that idea on a much larger scale than was known," says Chris Gunter, director of research affairs at the HudsonAlpha Institute for Biotechnology in Huntsville, Alabama.
The work suggests that RNA editing is providing a previously unappreciated source of human genetic diversity that could affect, for instance, how vulnerable different people are to disease.
Cheung does not know whether there are heritable changes, passed down from parent to child, that affect how much RNA editing occurs in different people. But scientists already know of a handful of RNA editing proteins that play a role in human health, such as the APOBEC enzymes, some of which have antiviral activity. Researchers investigating the connection between genetics and disease have been stymied by their inability to find strong connections between genetic variation and risk for most common diseases, leading researchers to wonder where the 'missing heritability' is hiding. The new study at least provides one place to look.
"These events could explain some of the 'missing heritability' because they are not present in everyone and therefore introduce a source of genetic variation which was previously unaccounted for," says Gunter.
Living with error
But because they do not know what mechanism might be responsible, most scientists contacted by Nature remained cautious about the significance of the finding and its possible impact on biology. Some say it is possible that technical errors could have caused the results. For instance, high-throughput sequencing machines can make systematic errors in DNA and RNA sequencing experiments.
And even if the findings hold up, it is still too early to know whether 'mismatching' plays an important role in human biology or not.
"The devil is in the details — to determine if the results are caused by some unintended technical or computational flaw or are correctly describing a biological phenomenon," says Thomas Gingeras at the Cold Spring Harbor Laboratory in New York. "Assuming the latter, I would be encouraged to look at our own large data sets to see if we see similar phenomenona."
Other researchers, such as Manolis Dermitzakis at the University of Geneva in Switzerland, say they are seeing the phenomenon in their data. Indeed, Cheung's team drew in part on data generated by the 1000 Genomes project, of which Dermitzakis is a member. However, Dermitzakis says it is still unclear how important the phenomenon is for disease susceptibility.
Cheung's group attempts to address many of these concerns, some of which were raised when the preliminary work was presented last November (see 'DNA sequence may be lost in translation') at the annual meeting of the American Society for Human Genetics, in Washington DC. Since then, the team has been looking for possible errors that could have caused the results.
For example, the researchers first observed DNA–RNA 'mismatches' in data generated by next-generation sequencing technologies in the International HapMap Project and the 1000 Genomes project. They have now confirmed some of the putative DNA-to-RNA changes using traditional Sanger sequencing, and have found the same changes in different people, across different cell types, and reflected in proteins.
Cheung says that at first "we truly did not believe it". But after performing the additional experiments "we cannot explain this by any obvious technical errors, so we are pretty convinced that this is real," she says.
Researchers who study RNA editing, which up to now was known mostly from plants and some unicellular human parasites, are intrigued by the new finding.
Kazuko Nishikura of the Wistar institute in Philadelphia says she was sceptical at first, because some of the base changes could not be explained by previously identified mechanisms. But she was convinced once she saw Cheung's data.
"It's really exciting, because this study reports a different variety of RNA editing that is much more widespread than existing mechanisms," Nishikura says.
Comment: (Andras Pellionisz)
It is a pitiful fact that “All science students learn the ‘central dogma’ of molecular biology” as if science were based on “Dogma”. To the contrary, science is based on facts explained not by negative pontification but by predictive theories that can be experimentally verified or refuted, and updated as required by new facts. Certain particularly crass pseudo-scientists (hiding their heads in the sandbox of 𠇍ogmatic Ideologies”), when confronted with a new theory, like “The Principle of Recursive Genome Function” (peer-reviewed science paper, popularized by the “Google Tech Talk YouTube”, both in 2008) claim ignorance of the cardinal issues, behind their shield of incompetence in information theory. It is too bad (for them), since the “Battelle Study”: identified old school biochemistry-based Genomics turned into new school information-theory based Genomics. Indeed, an $796 Bn economical impact just in the USA is unsustainable without software-enabling algorithmic approaches to genome function, particularly of genome (mis)regulation. Regarding both “Junk DNA” the historical challenge is not the endurance-game of some provincial ideologies but the vital need to stop ignoring the informatics of “Junk DNA diseases”, most notably cancers. Regarding “Central Dogma” it must be superseded by the science of e.g. fractal iterative recursion, where every event is, by definition, a function of preceding events. As Ms. Hayden so aptly pointed out elsewhere, the result appears complex, but its underlying mathematics (when understood) is lucid; “There is nothing simpler than a problem solved” – Faraday. The Battelle Study also identifies our times as the most important scientific-technological paradigm-shifts, ever. Therefore, those whom the Study refers to as “retards”, in any “lucid heresy” they only note the latter part – new understanding does not illuminate them, rather, it irritates their die-hard habits.
The fractal globule as a model of chromatin architecture in the cell
Leonid A. Mirny
Harvard-MIT Division of Health Sciences and Technology, and Department of Physics, Massachusetts Institute of Technology, Cambridge, MA USA
Chromosome Res. 2011 January; 19(1): 37-51.
Published online 2011 January 28. doi: 10.1007/s10577-010-9177-0.
The fractal globule is a compact polymer state that emerges during polymer condensation as a result of topological constraints which prevent one region of the chain from passing across another one. This long-lived intermediate state was introduced in 1988 (Grosberg et al. 1988) and has not been observed in experiments or simulations until recently (Lieberman-Aiden et al. 2009). Recent characterization of human chromatin using a novel chromosome conformational capture technique brought the fractal globule into the spotlight as a structural model of human chromosome on the scale of up to 10 Mb (Lieberman-Aiden et al. 2009). Here, we present the concept of the fractal globule, comparing it to other states of a polymer and focusing on its properties relevant for the biophysics of chromatin. We then discuss properties of the fractal globule that make it an attractive model for chromatin organization inside a cell. Next, we connect the fractal globule to recent studies that emphasize topological constraints as a primary factor driving formation of chromosomal territories. We discuss how theoretical predictions, made on the basis of the fractal globule model, can be tested experimentally. Finally, we discuss whether fractal globule architecture can be relevant for chromatin packing in other organisms such as yeast and bacteria.
The Principle of Recursive Genome Function: Quantum Biophysical Semeiotics clinical and experimental evidences
Sergio Stagnaro and Simone Caramel
May 8th, 2011
Conclusions: In this paper we have shown that Quantum Biophysical Semeiotics clinical and experimental evidences are consistent with and fully confirm the Principle of Recursive Genome Function. We can argue that the genetic alteration of the mit-DNA is reversible, generally not for a lack or impairment of genes, but for qualitative information imperfections in genes networking which lead to the activation of inappropriate genes or to inefficient configurations, defective or missing in some cases. Similarly, in microvessels there are communication obstructions which slow down the communication itself (blood flow) from structural and functional point of view. In parallel, it may be assumed that the alteration of the mit-DNA is reversible, during lifetime, and not just in overlapping generations, not for the fact that we create new genes from scratch, or because we are able to repair single genes in some way in a patient (as in genetic determinism), but because we intervene holistically on the whole, .. so that a proper and customized release of 'information' gives resonance to a virtuous feedback mechanism between DNA, RNA and downstream structures (tissues, cells, proteins, mithocondria,..) and vice versa, restoring physiological DNA dynamics...
The Myth of Junk DNA - an issue fallen from science in 2006 to a rejected ideology for the masses to chew on as an Amazon bestseller
This review is from: The Myth of Junk DNA (Paperback)
In recent years, a number of leading boosters of Neo-Darwinism have claimed that much of our genome is filled with functionless "junk DNA… But what if so-called "junk DNA" is not junk at all? In this meticulously documented book, molecular biologist Jonathan Wells … presents a growing flood of research that is showing the widespread functionality of DNA previously dismissed by leading Darwinists as little more than genetic garbage. For non-scientists, some of the detail provided here may be tough-sledding. But Wells does his best to make things clear even for those who may not have a detailed understanding of modern genetics, and his book includes a helpful glossary of key terms as well as 17 black and white illustrations. Chapter 9 provides a useful (and understandable!) summary of the case for functionality in "junk DNA." For scientifically-inclined readers, Wells' careful distillation in Chapters 3-8 of hundreds of recent journal articles documenting various functions for so-called "junk DNA" will be extremely valuable.
This review is from: The Myth of Junk DNA (Paperback)
… this book is a gold mine for anyone interested in the junk DNA issue and, indeed, the whole genetics field. The references (page 114 to page 159) alone are worth the price of the 173 page book (I paid 9.95 from Amazon). This readable well-documented tome included a glossary for those not in the field to insure that the book was assessable to every educated adult. In the last decade more and more research has supported the conclusion that much, if not most, of the so-called junk DNA has an important, if not a critical, function and I predict that this trend will continue for some time. Darwinism turned out to be a science stopper in this case. Instead of concluding the DNA that has no known function is junk, as was common, researchers should have been asking what does it do, the same question that would have saved us a lot of grief when Darwinists labeled organs that it was not known what their function was as vestigial (meaning then that they were evolutionary leftovers from the past without a function)
This review is from: The Myth of Junk DNA (Paperback)
As exploration began to reveal the full extent of the human genome in the 1970s, it was discovered, much to the shock of early researchers, that only a small portion of our DNA served explicitly to fold proteins. What did the rest of it do? More than a few researchers proposed that most of our genome was "junk," relics of an evolutionary past that served no current function. Despite the caution of some geneticists, who wisely suggested that junk DNA was merely a category representing the limits of our understanding, several self proclaimed spokesmen for science pounced on the idea that junk DNA was in fact proof that popular alternatives to Darwinian evolution were simply wrong… Much of what was originally thought to be "junk" in fact serves a purpose.
The bulk of Well's latest book is a summation of hundreds of articles dealing with the non protein coding portions of DNA. Indeed, the small print endnotes to the book make up more than a quarter of its total pages. But in general, Wells finds two lines of evidence that these DNA sequences are indeed functional. The first is that these sequences are ultra conserved, both in humans and other animals. If they were truly non-functional, we would expect that they would gradually deteriorate and be subject to a greater number of mutations than the DNA which we know to be functional. But in fact, this is not the case. This line of evidence, of course, is indirect. But a more direct line of evidence is found in the number of positive functions that have been uncovered. Apparently repetitive sequences of DNA, for example, serve to deactivate one of the two X chromosomes in female mammals to promote healthy development. Other functions, including RNA coding, are common.
Taken as a whole, this book is a useful summary of the current literature…. But there is another sense in which the story of "Junk DNA" (or rather, the story of how it was widely accepted and then gradually rejected) is in fact devastating to the Darwinian paradigm. Junk DNA was an important "proof" for many defenders of "science" that evolution was "true." And yet, one of the main arguments against the whole concept of junk DNA is that it is not compatible with what we know of natural selection. In other words, Darwinian evolution is at once proved both by the presence and absence of junk DNA. And given that is the case, falsifying Darwinian evolution is nearly inconceivable. In the final analysis, real science can be falsified, but ideologies cannot.
This review is from: The Myth of Junk DNA (Paperback)
This is a scholarly and impeccable book that takes a cogent case against the contemporary Darwinian story of intergenic sequences and conveys it on a level for the general peruser.
["So much 'Junk DNA' in our Genome" (see full facsimile of the 4.5 page science talk by Dr. Ohno, 1972) intended not as a metphor but a fully serious science issue was branded as "a suspect way to arrive at 1% figures of structural utility to 99% junk" (see first person Boyer to rise immediately after the utterance of the nonsense - ibid). The misnomer was discarded as a science issue by the first international organization of scientists, International PostGenetics Society in 2006, October 16, eight months before the US government released the devastating ENCODE results in 2007). Simply put, those who know anything about informatics realize that 1.3% of "about half the information of Windows 7" (indeed, a fraction of it, as amino-acid-coding exons are a small part of what used to be "genes") is simply an insufficient amount of information to build e.g. a human (a stamp-sized gif picture contains about that tiny amount of information). View Google Tech Talk YouTube of the triple Ph.D. Pellionisz or read his peer-reviewed science paper The Principle of Recursive Genome Function (both in 2008, laying down fractal iterative recursion as truly cracking the compressed code of full DNA, once proponents of "Junk DNA" and "Central Dogma" passed away, and even the US Government released the public admission ENCODE results in 2007). Contrary to earlier quotes, as of today all leading scientists, (Let us just list 7) Drs. Watson, Venter, Hood, Lander, Collins, Church, Schadt all agree that the classic notions are not only "frighteningly unsophisticated" (Venter), but have been outright "wrong" (see Lander telling NIH with all leaders present at the 10th Anniversary of the Human Genome Project, the Science Advisor to the President endorsing fractal approach). Others, notably name-caller ideologues, of course, can carry on with their pseudo-scientific propaganda forever, one full of junk just having been congratulated on his birthday of retirement-age as an epitome of a generation of ideologues "who led science nowhere". - This entry can be discussed on the FaceBook page of Andras Pellionisz]
Eric Schadt Joins Mount Sinai Medical School [Dir. of Inst. of Genomics AND Multiscale Biology]
May 16, 2011, 10:03 AM
Schadt Joins Mount Sinai Medical School
By ANDREW POLLACK
[Schadt' "The New Biology" is fractal-like "Multiscale" - AJP]
With his boundless energy and unvarying outfit of shorts, sandals and a white polo shirt, Eric E. Schadt, the chief scientific officer of DNA sequencer manufacturer Pacific Biosciences, is considered a brilliant rebel in the field of genomics.
Now Dr. Schadt is about to take on a new role – as chairman of genetics at the Mount Sinai School of Medicine in New York as well as chief of an institute there designed to study complex biology and apply it to medical treatments.
Dr. Schadt will retain his position at Pacific Biosciences, which is based in Menlo Park, Calif., but will clearly be spending less time there now.
Pacific Biosciences said it would collaborate with Mt. Sinai on the institute that Dr. Schadt will run, known as the Institute for Genomics and Multiscale Biology.
Dr. Dennis S. Charney, dean of the school of medicine, said in an interview that Dr. Schadt could make Mt. Sinai, “among the leaders in making genetics a key part of the way medicine is practiced.’’
The collaboration with Pacific Biosciences is “a value added,’’ Dr. Charney said. He said the genomics institute would receive about $100 million over five to six years.
Under the collaboration, Pacific Biosciences will supply the institute with two prototype machines that can be used for DNA sequencing as well as for analysis of other biological molecules, such as RNA. Mt. Sinai is also expected to buy at least one of the standard $695,000 DNA sequencers that Pacific Biosciences has just started shipping.
Dr. Schadt said the new effort will allow him and Pacific Biosciences to be “embedded’’ in a medical institution to apply sequencing findings to medicine. “They have clinical data that can be provided instantly’’ from patient medical records, he said.
Hugh Martin, chief executive of Pacific Biosciences, said the company had been looking for a big academic collaboration.
Dr. Schadt is a leading proponent of the view that focusing on individual genes might not be the way to treat diseases or discover drugs. Rather, researchers should focus on complex networks of interactions between genes and other parts of an organism. Such complex networks can be understood best by simulating them on computers.
He spent much of his career at Rosetta Inpharmatics, a genomics company acquired by Merck. He is also co-founder of Sage Bionetworks, a nonprofit organization that seeks to apply the complex network philosophy to understanding diseases. He has been profiled, among other places, in The New York Times and in Esquire.
May 16, 2011
Genomeweb
As our sister publication GenomeWeb Daily News reports, Pacific Biosciences' Chief Scientific Officer Eric Schadt will lead the Mount Sinai Institute of Genomics and Multiscale Biology. As part of the collaboration between PacBio and Mount Sinai, GWDN continues, "a Single Molecule Real Time Biology User Facility will be established within the institute, which is the hub of genomics research at Mount Sinai and collaborates with 13 other disease-oriented and core technology-based institutes at Mount Sinai." Luke Timmerman at Xconomy San Francisco adds that the "union of PacBio and [Mount] Sinai is a high-profile effort to bridge the traditional divide between lab research and clinical treatment of patients," saying that both parties are likely to benefit:
For Mt. Sinai, hiring a star like Schadt means it will likely attract more donations, and be able to recruit many more bright young physicians and scientists interested in genomic-based personalized medicine. For PacBio, it hopes to learn how to best position its instrument with customers around the world, after hearing from Schadt how it works in the trenches.
Timmerman also says that, by joining Mount Sinai, "Schadt and PacBio are walking away from a potential partnership with UC-San Francisco, which had been wooing Schadt for months," and that this move raises questions as to the future of the New York Genome Center, "a fledgling effort to bring together a number of New York's top biomedical research centers to create a shared world-class genomics research facility," which, he adds, is still its planning phases.
COMMENTS:
Submitted by andras on Tue, 05/17/2011 - 01:24.
Kudos to Eric, who is now also the Chairman of the Mount Sinai Institute of Genomics AND Multiscale Biology. The CSO of sequencer PacBio, he is a brilliant rebel, a proponent that focusing on individual genes is not the way to treat diseases or discover drugs. In the paradigm shift of the "genome revolution" documented by the Battelle Study as the most disruptive economic singularity of science and technology ever, already the size of beyond the GDP of Brazil, about the same as the GDP of Russia and just slightly smaller than the GDP of India, rebels are becoming acknowledged leaders. "Multiscale" is a quantum leap towards "scale free" fractals. It is particularly nice to see that "New York's gain is NOT Silicon Valley's loss! - Andras Pellionisz
[It is remarkable that though Eric's "The New Biology" video, from which the illustration is clipped, was uploaded by PacBio as "Systems Biology" (that is clearly not new, Ludwig von Bertalanffy used the term first in 1928). In order to better define the unquestionable mathematical system lurking behind the "complexity" ("complexity is in the eye of the bewildered") Eric Schadt's new $100 M institute is called "Multiscale Biology" - a quantum leep towards the Fractal Approach such as FractoGene (2002), identifying the intrinsic mathematics of the genome-epigenome (hologenome) system, and thus emerging as algorithmic genome interpretation, where e.g. the "before and after" cancer therapy structural variants can be neatly separated to (harmless) "human diversity" parametric differences, and (harmful) "fractal defects" that are syntax-errors, the genome violating its own mathematical rules, leading to disregulated (cancerous) growth; see Principle of Recursive Genome Function science paper and its popularization Google Tech Talk YouTube, 2008. This entry can be commented in the FaceBook page of Andras Pellionisz]
Battelle Study: The $796 Bn Economic Impact of the Human Genome Project
[.pdf of full 58-page Battelle Study - AJP]
[First an idea what $796 Bn is. It is larger than the 2010 GDP - Gross Domestic Product - of the Continent-size Brazil. It is about the same as the GDP of Russia, and just a little bit smaller than the full GDP of India - AJP]
Excerpts [AJP]
Introduction
The sequencing of the human genome represented the largest single undertaking in the history of biological science and stands as a signature scientific achievement. All of history in the making, human DNA took just 13 years to sequence under the Human Genome Project (HGP), an international public project led by the United States, and a complementary private program.[Myth #1 may be, that "the government did it all" - as we know, it was an official "tie" between the government and public sector; by now at both ends of Sequencing Companies and Genome Computers the USA private sector rules - AJP]. Sequencing the human genome—determining the complete sequence of the 3 billion DNA base pairs and identifying each human gene—required advanced technology development and the assembly of an interdisciplinary team of biologists, physicists, chemists, computer scientists, mathematicians and engineers... the knowledge of genome structure, and the data resulting from the HGP as the foundation for fundamental advancements in medicine and science with the goals of preventing, diagnosing, and treating human disease. Also, while foundational to the understanding of human biological systems, the knowledge and advancements embodied in the human genome sequencing, and the sequencing of model organisms, are useful beyond human biomedical sciences.
The resulting “genomic revolution” is influencing renewable energy development, industrial biotechnology, agricultural biosciences, veterinary sciences, environmental science, forensic science and homeland security, and advanced studies in zoology, ecology, anthropology and other disciplines.
In the ten years since the first sequences were published, much has been written about the scientific consequences of mapping the genome but little analysis has been done of the economic significance of the achievement. [Myth #2 may be, that the Battelle Study is about the Science of PostGenetics. It is NOT. It fills the void of a comprehensive ECONOMIC study of its impact, already a historical turning-point, and "just the beginning" - AJP]
The technologies that have empowered genome sequencing range from the gene sequencers themselves, to sample preparation technologies, sample amplification technologies, and a range of analytical tools and technologies. An industry has grown up to supply the scientific research community in the private sector, government and academia with the equipment, supplies and services required to conduct genomics research and development (R&D) and associated product development. This industry, of course, generates additional economic impacts.
To evaluate these genomics-enabled industry impacts in the U.S., Battelle constructed a “from the ground-up” database of individual companies engaged within the sector. The employment of this industry base was used as the foundation for an input/output analysis to quantify the total impacts of these firms (in terms of direct and indirect output and employment and their multiplier effect). The results of the analysis show that—spurred by the original investment and technological development impetus of the human genome sequencing projects—a substantial economic sector has developed thereby benefiting the U.S. economy in terms of business volume, jobs, and personal income supporting American families. [Myth #3 may be, that the Battelle Study introduced "Genome-Based Economy". As the website of Genome Based Economy and the 2009 Churchill Club YouTube indicate, the First Chapter opened by the Nobel Prize in 1970 to Norman Borlaug for his "Green Revolution" - AJP]...
• Non-coding DNA, previously termed “junk DNA” because it was thought to be a relic of evolution with little biological function, was instead confirmed to have specific functionality in transcription and translational regulation of protein-coding—i.e., most of it is not junk at all, it is central to life functions. This finding alone supports the vision of undertaking whole genome sequencing, since prior to the HGP some detractors argued that the budget would be better spent simply studying known protein coding genes and ignoring the rest. Eric Lander points out that “it has reshaped our view of genome physiology, including the role of proteins-coding genes, non-coding RNAs and regulatory sequences. [Myth #4 may be, that the Battelle Study proved that "Junk DNA is anything but Junk". No, the "Junk DNA" misnomer and "Central Dogma" obsolete axioms have long died of a thousand wounds, and only lingered because "facts don't kill theories, only a better theory kills an obsolete one"; as the Gene/Junk "frighteningly unsophisticated" notion was superseded by The Principle of Recursive Genome Function, 2008. What the Batelle Study did prove, however, that "detractors" not only inflicted half a Century of delay in the progress of Genomics/Epigenomics (HoloGenomics) science (and documentable harm to scientists) - but also were directly responsible to huge economic losses; e.g. pursuing for decades "gene discovery" (of non-existent genes) at horrendous expense. The end result is a continuous decrease of the "predicted" 4 million, 140,000, 40,000, 30,000, 19,000 "genes", concluding that the very old-fashioned definition of "gene" became extinct - yielding room for FractoGene (2002) - AJP]
A. Basic Science and Knowledge Expansion Impacts
The sequencing of the human genome has resulted in a distinct paradigm shift in our understanding of the biology of humans, and indeed all organisms. As such, ipso facto the decoding of the human genome stands among the preeminent findings in the history of science.
[Myth #5 may be, that the Battelle Study is impeccable from a science viewpoint. It is NOT. Few leading scientists would disagree with the rock-solid statement that "by revealing - sequencing - of the genome, it was NOT "DECODED". Indeed, the Battelle Study is a rock-solid basis of a horrific scenario; that without an actual (mathematical) decoding of the meaning of revealed sequences a Russia-sized economic Titanic may hit an iceberg. The Battelle Study does provide a very strong hint, as follows: - AJP]
Sequencing the human genome made clear information sciences, mathematics and biological investigation are now inexorably intertwined. The sequencing of the genome was as much a mathematical and computational achievement as it was a biological one and has helped to give rise to new fields of biological science in “computational biology” and “systems biology”. It has been noted that “The revolutions that have been generated by the first draft of the Human Genome Project have barely been felt, but there is one profound change that has already occurred, and that is the realization that biology is fundamentally an information science.”
[Myth #6 may be, that the Battelle Study equates the new axiom that "genomics (not biology in general) is fundamentally an information science" with the improper statement that sequencing of the genome gave rise to "systems biology". The term Systems Biology was first used by Ludwig von Bertalanffy in 1928 and even his General System Theory book appeared in 1936, decades before HGP. While there is no question in anyone's mind that the Genome/Epigenome (HoloGenome) complex interactions constitute a "system", the absolutely critical challenge is the mathematical identification of what "system" do we face; how its complexity is reduced to the size of about half of Microsoft Windows 7. The Principle of Recursive Genome Function peer-reviewed paper and its popularization as Google Tech Talk YouTube (2008) defines the "system" mathematically as fractal iterative recursion; the name of the game is to come up with a more befitting algorithmic (software enabling) system-definition - AJP]
... Most common diseases, such as cancers, heart disease and psychiatric disorders are not homogeneous diseases, but differ dramatically across individual genomes from patient to patient...
[The profound implications of this simple sentence (on cancers, to be analyzed in detail separately), can be best documented by the Battelle Study directly quoting Thomas Kuhn, see below - AJP]
Thomas Kuhn first coined the term “paradigm shift” which is, by definition, a rather radical upheaval—a major movement of scientific knowledge and understanding to a new platform. Such movement occurs rarely and research leading to a paradigm shift should, therefore, be viewed as a momentous scientific achievement.
Thomas S. Kuhn. 1962. “The Structure of Scientific Revolutions.”
[Since this is my third paradigm-shift, I can make some comparisons of the Battelle Study with watershed events of the publication of two earlier Studies. I took part in pioneering the transition from ill-defined "Artificial Intelligence" (mistakenly claiming that we can create machine intelligence without the benefit of understanding the real, biological one). In that breakthrough the US defense-agency DARPA made the crucial difference by their DARPA-Study assessing the potential in Neural Nets - the science and technology providing "reverse engineering" e.g. how flying of birds with their cerebellum can be used to automatically land an F15 fighter on one wing. In my second paradigm-shift (when the tiny R&D defense project of connecting computers by email of system administrators became a Private Industry in 1994 by Jim Clark's Netscape and the ensuing Internet-boom exploded bigger and quicker than anyone imagined, the US Government's "Blue Book on Internet future" ("High Performance Computing & Communications: Toward a National Information Infrastructure" multi-agency report, completed in 1994) served as the key Study for the breakthrough. Now, with the Battelle Study on $796 Bn Genomics of the USA (developed from the seed of $3.8 Bn government investment with competing private investments) is the "eye opener" that literally overnight generates now a ripple-effect through all media. Genome-based Economy is the Next Big Thing already. This entry can be commented in the FaceBook page of Andras Pellionisz]
In an improbable corner of China, young scientists are rewriting the book on genome research [Newsweek]
Newsweek 2011/04/24
high-quality-dna
The world’s largest genome-mapping facility is in an unlikely corner of China. Hidden away in a gritty neighborhood in Shenzhen’s Yantian district, surrounded by truck-repair shops and scrap yards prowled by chickens, Beijing’s most ambitious biomedical project is housed in a former shoe factory.
But the modest gray exterior belies the state-of-the-art research inside. In immaculate, glass-walled and neon-lit rooms resembling intensive care units, rows of identical machines emit a busy hum. The Illumina HiSeq 2000 is a top-of-the-line genome-sequencing machine that carries a price tag of $500,000. There are 128 of them here, flanked by rows of similar high-tech equipment, making it possible for the Beijing Genomics Institute (BGI) to churn out more high quality DNA-sequence data than all U.S. academic facilities put together.
“Genes build the future,” announces a poster on the wall, and there is no doubt that China has set its eye on that future. This year, Forbes magazine estimated that the genomics market will reach $100 billion over the next decade, with scientists analyzing vast quantities of data to offer new ways to fight disease, feed the world, and harness microbes for industrial purposes. “The situation in genomics resembles the early days of the Internet,” says Harvard geneticist George Church, who advises BGI and a number of American genomics companies. “No one knows what will turn out to be the killer apps.” Companies such as Microsoft, Google, IBM, and Intel have already invested in genomics, seeing the field as an extension of their own businesses—data handling and management. “The big realization is that biology has become an information science,” says Dr. Yang Huanming, cofounder and president of BGI. “If we accept that [genomics] builds on the digitalization of life, then all kinds of genetic information potentially holds value.”
BGI didn’t always seem destined for success—or even survival. “The crazy guys” was how Chinese colleagues initially referred to the two founders, Huanming and director Wang Jian. Refused government support, they muscled their way into the international Human Genome Project, mapping out 1 percent of that celebrated first full sequence before tackling the rice-plant genome on their own, beating a well-funded international consortium, and suddenly finding political leverage. Yang and Wang used it to set up the research center, which is nominally nonprofit but carries out commercial activities in support of the research. With an annual grant of $3 million from the local government in exchange for moving to the shoe factory in 2007, BGI first grew modestly, generating income from fee-for-service sequencing and conducting molecular diagnostic tests for hospitals. A $1.5 billion loan from the Chinese Development Bank in 2009 allowed the company to catapult into a different league, and its combination of sequencing power and advanced DNA data-management solutions for the pharma industry are now drawing international attention. Last year, pharmaceutical giant Merck announced plans for a research collaboration with BGI, as the Chinese company’s revenue hit $150 million—revenue projected to triple this year. “I admire their passion and the willingness to take risks,” says Steven Hsu, a physicist at the University of Oregon, adding that “it permeates the organization.”
Others would like to see deeper scientific reflection tempering the monumental ambition. “A more philosophical and conceptual rather than just a technical approach to the genome is needed to foster great discovery,” says long-time collaborator Oluf Borbye Pedersen of the University of Copenhagen.
While other well-known genomics centers such as Boston’s Broad Institute concentrate more narrowly on human health, the Shenzhen scientists cover a broad biological spectrum. In one shiny lab, thousands of microbes are being scanned for genes that might serve useful industrial purposes, while in another human stem cells are being developed for clinical applications. Scientists have mapped the genomes of everything from cucumbers and 40 different strains of silk worms to the giant panda. They have also cataloged tens of thousands of genes of bacteria living in the human gut, and pieced together the genomic puzzle of an ancient human—an extinct paleo-Eskimo who lived in Greenland 4,500 years ago. While such academic prestige projects are geared toward publication in scientific journals, real-world experimentation is going on at a nearby farm where pigs are cloned to serve as disease models. And in Laos, scientists are testing genetically enhanced plants to feed China’s growing population. The institute has already amassed almost 250 potentially lucrative patents covering agricultural, industrial, and medical applications.
Satellite research centers have been set up or are underway in the U.S., Europe, Hong Kong, and four other locations in China, and the number of researchers at the main headquarters in Shenzhen has more than doubled during the past year and a half. The institute now employs almost 4,000 scientists and technicians—and is still expanding.
“I’ve seen it happen but sometimes even I can’t believe how fast we are moving,” says Luo Ruibang, a bioinformaticist, who at 23, fits perfectly within the company’s core demographic. The average age of the research staff is 26.
Li Yingrui, 24, directs the bioinformatics department and its 1,500 computer scientists. Having dropped out of college because it didn’t present enough of an intellectual challenge, he firmly believes in motivating young employees with wide-ranging freedom and responsibility. “They grow with the task and develop faster,” he says. One of his researchers is 18-year-old Zhao Bowen. While still in high school, Zhao joined the bioinformatics team for a summer project and blew everyone away with his problem-solving skills. After consulting with his parents, he took a full-time job as a researcher and finished school during his downtime. Fittingly, he now manages a project on the genetic basis of high IQ. His team is sampling 1,000 Chinese adults with an IQ higher than 145, comparing their genomes with those of an equal number of randomly picked control subjects. Zhao acknowledges that such projects linking intelligence with genes may be controversial but “more so elsewhere than in China,” he says, adding that several U.S. research groups have contacted him for collaboration. “Everybody is interested in intelligence,” he says.
A shoe factory becoming a genomics center, scientists replacing blue-collar workers—the Shenzhen research facility embodies the country’s economic and social ambitions. According to a 2010 report from Monitor Group, a management consulting firm based in Boston, China is “poised to become the global leader in life-science discovery and innovation within the next decade.”
The Chinese government will, by next year, have spent $124 billion since 2009 building hospitals and health-care centers. Such strategic investments have lured Chinese scientists back to China. So far, at least 80,000 Western-trained Ph.D.s have returned, the vast majority in the past five years. With the country on track to become the second-largest pharmaceuticals market next year, and the U.S. failing behind, afflicted by weak—and declining—government funding of basic science as well as anemic collaboration between private and public sectors, China could take the lead. As George Baeder, vice president of Monitor Group Asia, says, China “has the potential to create a more efficient model for discovering and developing new drugs,” a prediction echoed by Caroline Wagner, a science-policy specialist and professor at Pennsylvania State University, who argues in a forthcoming paper that the days of American leadership will soon be gone. “After more than half a century at the top spot, the U.S. will become one big player among several,” she says.
But, Wagner adds, “science is not a zero-sum game,” and as the pie gets bigger, so will the opportunities for collaboration. Yang, for his part, puts it simply: “Genomics is international,” he says. “We must collaborate to survive and develop.” Certainly, the scientists at his Shenzhen headquarters have their view on the world. The latest shipment of high-tech toys sits, still unpacked, on the floor; the stamp on the sides of the crates proclaiming: Made in the USA.
[This posting can be debated at the FaceBook site of Dr. Pellionisz. Suffice to note here, that the same Newsweek that in 2007 elected to publish in all its editions - except in the American edition - a brilliant article on the "Genome Revolution" ensuing publication of US-lead ENCODE, now laments on the fact that 80,000 returned to China with the best ideas and equipment and the US 4 years later is about to slip into the traditional role of "followership" that formerly China practiced, not us. I posted the article, and took steps to secure precious intellectual property. It may well be that the manpower will be provided by China, India, Korea and Japan. It is hard to resist meetings like Bio-IT in Shenzen, China, timed for former US "Independence Day", 4th of July, 2011]
Systems Biology 'Makes Sense of Life'
Genomeweb,
April 27, 2011
Systems Biology needs System Identification [e.g. Fractal System - AJP]
Researchers have made great strides in understanding biology by taking a reductionist approach and studying little bits of the "complex tapestry of life" at a time, says Scientific American's Christine Gorman. And until now, that's worked just fine, she says. But researchers increasingly find themselves butting up against the limits of this traditional approach, eventually realizing that they must embrace the complexity of life in order to really understand it, Gorman adds. Institute for Systems Biology co-founder Alan Aderem says a systems biology approach could allow researchers to produce vaccines that have been otherwise unachievable to date. In this video from Virginia Commonwealth University, researchers explain the systems biology approach and how it can be used to study disease.
Submitted by lermanmi on Wed, 04/27/2011 - 15:09.
Systems Biology was invented following the "omics revolution" and is a tool in the war for monies. No more. Like the omics it's totally devoid of ideas and is illusory and mystical. It is so "new" that I have not seen a textbook describing its principles and fundamental findings for students. However there are a lot of new paper and digital magazines titled with the word Systems...... (e.g. Systems Urology etc) Michael Lerman
Submitted by andras on Thu, 04/28/2011 - 03:27.
Nature can only be understood as interactive systems. Trajectories of planets, from a reductionist view focusing on the Earth, look needlessly overcomplicated - while a Galilean view of the planetary system (planets elliptically orbiting around the Sun) instantly reduces a seemingly maddening "complexity". We know by now that Genomics just would not work if viewed from a "frighteningly unsophisticated" perspective of "Genes/Junk". Rather, a holistic approach of uniting Genomics with Epigenomics, expressed in Informatics; HoloGenomics is needed to mathematically identify the (fractal) "system" that we face. FractoGene and The Principle of Recursive Genome Function, identifying the system as fractal iteration is not a vague "systems biology" approach, but is an algorithmic (and thus software-enabling) specification of fractal genome function, arising from the demonstrably fractal nature of the genomic code. All this was laid out in peer-reviwed science paper and popularized by Google Tech Talk YouTube in 2008 - and within the very short window of a mere three years have become the algorithmic inroad to beat - Pellionisz
["Systems theory is the transdisciplinary study of systems in general, with the goal of elucidating principles that can be applied to all types of systems in all fields of research. The term does not yet have a well-established, precise meaning, but systems theory can reasonably be considered a specialization of systems thinking and a generalization of systems science. The term originates from Bertalanffy's General System Theory (GST), 1968." (Wikipedia). As I commented to Genomeweb (above) the fashionable "New Frontier; Systems Biology" is highly welcome to the club of holistic approaches (see HoloGenomics, once the mistaken half a Century of old reductionalist Dogmas of JunkDNA/Genes/Central Dogma is finally written off). Dr. LeRoy Hood would probably not consider Systems Biology all that new, however, as he has pursued it at least since 2002 - about the same time when FractoGene arose in 2002, and we might add that "The term systems biology is thought to have been created by (my fellow Hungarian nobleman) Ludwig von Bertalanffy in 1928" (Wiki). With Systems Theory, the axiomatic challenge has always been the mathematical identification of what system do we face. From the viewpoint of Academics, one can argue back and forth, forever, but the crucial need to identify the system will not vanish. From the viewpoint of those suffering from genomic diseases (e.g. The Genome Disease, a.k.a. Cancer) this is question of extreme urgency - just as it was for another Hungarian, John von Neuman, to architect computers for the computing needs of World War II. For the present, War on Cancer, II. (the first waged by Nixon, $30 Bn - without the modern weaponry), now we have both practically any number of fully sequenced genomes (in repeat customer mode...) - and can leverage defense-validated High-Performance-Computers - assuming that e.g. a Fractal System Identification (or competing approaches) are algorithmic, i.e. software-enabling. - Pellionisz]
Virginia Tech partners with NVIDIA to “Compute the Cure” for Cancer
Virginia Tech professors have received research backing from the NVIDIA Foundation in the Silicon Valley to develop an analysis program that will help researchers identify cancer mutations. Here is a press release from Tech:
Virginia Tech researchers Wu Feng and David Mittelman have won the first worldwide research award from the NVIDIA Foundation, as part of its “Compute the Cure” program.
The award will enable them to develop a faster genome analysis platform that will make it easier for genomics researchers to identify mutations that are relevant to cancer.
This program is a pilot effort started by the NVIDIA Foundation, the philanthropic arm of the Silicon Valley-based technology firm NVIDIA. The company specializes in programmable graphics processing units (GPUs), which are used in everything from super phones to supercomputers. The Compute the Cure program has a strategic mission to leverage GPUs to support cancer researchers in the search for a cure, as well as promote cancer awareness and prevention initiatives for the greater good.
“Compute the Cure seeks to revolutionize the way that cancer biologists conduct their science by delivering a framework and toolkit of personal desktop supercomputing solutions for the analysis of genome changes from next-generation sequencing data, as a first step toward seeking a cure for cancer,” said Feng, associate professor in the Department of Computer Science and Bradley Department of Electrical and Computer Engineering in the College of Engineering at Virginia Tech. He is principal investigator on the project.
Co-investigator David Mittelman is an associate professor with the Virginia Bioinformatics Institute and the Department of Biological Sciences, part of the College of Science at Virginia Tech. Mittelman previously worked at the Human Genome Sequencing Center at Baylor College of Medicine in Houston. As the domain expert, Mittelman’s research program has explored the molecular basis for genome instability in mammalian systems and its role in diseases such as cancer. His lab also has developed sensitive methods for characterizing genome instability using next-generation whole-genome sequencing.
Using GPU-accelerated alignment and mapping software in combination with sensitive mutation detection methods, Feng and Mittelman will use the $100,000 Compute the Cure award to deliver an optimized and powerful solution for genome analysis to the research community that other investigators can build upon, to collaboratively advance the field of cancer genomics.
Feng first worked with NVIDIA in 2009, when he was one of 38 recipients worldwide to receive an NVIDIA Professor Partnership Award, designed to accelerate exploration at the frontiers of visual, parallel, and mobile computing. Feng’s Professor Partnership Award spurred his research in the parallelization and optimization of different algorithms in pairwise sequence alignment and short-read mapping onto the GPU. These are critical tasks towards understanding how genomes change during cancer.
Tonie Hansen, the director of the NVIDIA Foundation, said the tech firm’s interest in medical research always has been strong.
“NVIDIA’s Compute the Cure program combines our company’s technical focus and our people’s personal priorities,” she said. “Medical researchers the world over use GPU technology to accelerate the pace of their research. And NVIDIA employees donate generously each year to cancer research organizations, in support of a friend or family member who is fighting the disease. Compute the Cure combines these objectives into one comprehensive program.”
Breast cancer prognosis goes high tech [Fractal - AJP]
[Fractal lilly - even laypeople are aware of the fractality of cancer tumors - AJP]
Published: Monday, April 18, 2011 - 09:04 in Health & Medicine
Cancer researchers at the University of Calgary are investigating a new tool to use for the prognosis of breast cancer in patients. This new digital tool will help give patients a more accurate assessment of how abnormal and aggressive their cancer is and help doctors recommend the best treatment options. Currently, a useful factor for deciding the best treatment strategy for early-stage breast cancer is tumour grade, a score assigned by a pathologist based on how abnormal cancer cells from a patient tissue sample look under the microscope. However, tumour grade is somewhat subjective and can vary between pathologists. Hence, there is a need for more objective methods to assess cancer tissue, which could improve risk assessment and therapeutic decisions.
Using a mathematical computer program developed at the U of C , Mauro Tamabsco, PhD, and his team used fractal dimension analysis to quantitatively assess the degree of abnormality and aggressiveness of breast cancer tumours obtained through biopsy. Fractal analysis of images of breast tissue specimens provides a numeric description of tumour growth patterns as a continuous number between 1 and 2. This number, the fractal dimension, is an objective and reproducible measure of the complexity of the tissue architecture of the biopsy specimen. The higher the number, the more abnormal the tissue is.
According to the team's published study, this novel method of analysis is more accurate and objective than pathological grade. "This new technology is not meant to replace pathologists, but is just a new digital tool for them to use" says Tambasco, a medical physicist at the University of Calgary Faculty of Medicine and the Tom Baker Cancer Center.
Researchers say they will continue to study this new digital method and hope in the next few years that it could become another tool used in the clinical setting.
The retrospective study analysed tissue specimens from 379 breast cancer patients and the findings were published in the January 2011 edition of the Journal of Translational Medicine.
FractoGene (2002) and Fractal Frenzy set off by The Principle of Recursive Genome Function, YouTube (2008) [AJP]
[FractoGene 2002, (also USPTO, 2002 Aug. 1.) Pellionisz in CSHL 2009 September, Lander et al. 2009 October, Stein 2011]
“There is a growing gap between the generation of massively parallel sequencing output and the ability to process and analyze the resulting data” McPherson [Ontario Institute of Cancer Research]
“Chanock [See quotation from Article below, National Cancer Institute], also a medical doctor, cautions that the community should be realistic with regard to ... all this structural variation data to facilitate improvements in the clinic… "The plausibility and the meaning of this discovery is complex: each one of these regions requires its own study and it's still a work in progress to reach the level of confidence and validity that's needed to incorporate that into our clinical workflow. We have to be careful with all the ballyhooing about 'The genomic age is going to turn everything into Star Trek medicine,' because I find this dangerously naïve”
As seen above, YouTube Is IT Ready for the Dreaded DNA Data Deluge? 2008 clearly predicted both that "Information Technology will be all right" as well as that Information Theory of Genome Function needed not a cosmetic, but profound reform.
Now, three years later - there is a crisis in Cancer Research without Algorithmic breakthrough.
Conventional medicine has lost ground without algorithmic, software-enabling theory
It is also increasingly recognized by leaders, that Brute Force must be augmented by Genome Theory to resolve crisis:
Francis Collins; Scientists have to re-think long-held beliefs
Craig Venter: Our level of understanding the genome is frighteningly unsophisticated
George Church: Zero dollar sequencing and one million dollar interpretation
Eric Lander: Fundamental assumptions were all wrong
John Mattick: Dogmas are obsolete
Eric Schadt: Considers the fractal approach of AP [HolGenTech] “truly revolutionary”
From the Figs. quoted above, a "Fractal Frenzy" is evident since 2008, especially afer the Fractal Approach was also presented in Cold Spring Harbor (2009 September). Lander et al. published their Science cover article in 2009 October - and now a CSHL leader (though not on record with fractal papers) placates fractals as the ZeitGeist. The acceleration is also evident from the escalation of viewership of the 2008 YouTube, see above.
The Structural Struggle - [vs. Fractal Algorithmic Elegance - AJP]
Genomeweb
April 2011
By Matthew Dublin
The diverse world of structural genomic variation research — which includes investigations into copy number variation and mapping myriad inserted, deleted, inverted, and translocated genes — is undoubtedly providing investigators with an exciting and promising source of data on human diversity and disease susceptibility. [But the Ten Million Dollar question is the algorithm that sets the "human diversity" and "disease susceptibility" structural variations apart - AJP]. But if a Nature paper published by the 1,000 Genomes Project's Structural Variant group in February is any indication, eureka moments in this field may be a bit further off than researchers originally hoped. The report — which represents the culmination of roughly two years' work involving more than 50 investigators from across the world — describes the group's construction of a CNV map based on whole-genome sequencing data from 185 human genomes. It encompasses roughly 22,000 deletions and 6,000 insertions and tandem duplications. Using a genotyping approach that examined several partial- and whole-gene deletions, the researchers reported a depletion of gene disruptions among high-frequency deletions as well as differences in the size spectra of structural variants.
While the team produced a robust resource for future sequencing-based association studies, Charles Lee, the group's co-chair and director of Harvard Medical School's Molecular Genetic Research Unit, says the take-home message is that considerable barriers must still be overcome before the field can move forward. "We found that we needed new algorithms to identify structural variants and we ended up creating 19 different computer programs. No one program was sufficient — we had to combine multiple programs to maximize the amount of structural variation we are picking up," Lee says. "But at the end of the day ... even at high coverage, we are picking up probably about 82 percent of known deletions, about 15 to 18 percent of known duplications, and essentially no inversions or translocations that we can verify at this stage — so we have a long way to go. If that's where we're at with over 50 investigators, 19 algorithms, and two years of work, we have a long ways to go."
Stephen Chanock, chief of translational genomics at the National Cancer Institute, says that while the generation of resources like the 1,000 Genomes Project are important to better explore genomic structural variation, the need for analytical accuracy will be the pinch that wakes the dreamers up to face reality. "The excitement of having more and more tools always bring us back to the very important question of having to validate or replicate, and I worry that that's getting lost as everyone gets so excited about the next really cool tool. Those are all in silico observations; you still have to go back and make sure that variant is stable and matches what you think you've seen when you actually sequence a genotype," Chanock says. "CNVs, I think, are very interesting for rare or less-common diseases, although the common disease, common variant hypothesis for CNVs has been not quite as exciting as everyone had hoped. It didn't have the drama that everyone thought was there, unlike [in] the common SNP world. ... Ultimately, the technologies are making it easier and we may be going after uncommon and rare variants if whole-genome sequencing kicks in, or at least much denser chips become available."
Better tools
While there are many tools available to identify structural variants, the question of determining which reported variants are actually valid remains a large challenge that bioinformatics tools alone cannot deal with. "I'm not saying any one study is bad, but there is an under-appreciation for the amount of false-positives in the structural variation data that we're generating as a scientific community from next-generation sequencing data," Harvard's Lee says. "My advice to people who are analyzing next-generation sequence data in structural variants — especially for whole genome analyses — is to use as many technologies to complement their analysis as possible. For example, if you're whole-genome sequencing a given individual, maybe use different insert-sized libraries complemented with arrayCGH data. And, by all means, perform a significant amount of validation so you can minimize the amount of false-positive data."
The limitations to productivity Lee and his colleagues face when using multi-color probes to look at the structure of repeated genes using the fiber FISH technique is just one area in need of improvement. "It's just not high-throughput enough, so if someone could come up with a high-throughput method, that would be an excellent way to genotype some of the more copy number variable regions," he says. "I think also the arrays themselves are continually being improved in terms of what probes are being placed on there to genotype specific CNVs, but there needs to be more effort put into the technology for accurate genotyping of CNVs." For now, Lee says, the only work-around is putting in hours of labor to get the job done. [This is called "the brute force approach" in industry - AJP]
The most significant development with genotyping and CNVs over the last few years is the development of high-resolution array comparative genomic hybridization. This technique enabled the very first studies that mapped structural variation genome-wide in 2003 and 2004. Since then, advancements in high-throughput paired-end mapping, read depth of coverage analysis, split read analysis, and assembly have all seriously ramped up research efforts. "We consider massive paired-end mapping a key technique to identify structural variation and genomic rearrangements," says Jan Korbel, group leader at the European Molecular Biology Laboratory. Korbel and his colleagues at Yale University and 454 Life Sciences developed an approach for massively parallel paired-end sequencing that is helping the team to identify germ-line structural rearrangements in connection with the 1,000 Genomes Project and the International Cancer Genome Consortium. "The key advantage of paired-end mapping [is that] it allows a fairly deep and quick and cheap sequencing structural aberrations in the genome by recognizing ends of long fragments and mapping them," Korbel says.
Some newer genotyping tools show particular promise, he adds. These include the SUN genotyping method, developed by Evan Eichler's group at the University of Washington, which identifies "singly unique nucleotide" positions to genotype the copy and content of specific paralogs within gene families that are highly duplicated, and the analytical software framework Genome-STRiP, developed by Harvard University's Steve McCarroll for characterizing genome structural polymorphisms using multiple types of next-generation sequencing data including read depth, read pairs, and split reads.
Korbel's own group has designed a novel computational method to analyze the depth of coverage of high-throughput DNA sequencing reads, called CopySeq. This tool can infer locus copy number genotypes by integrating paired-end and break point junction analyses based on CNV-analysis approaches such as arrayCGH and FISH. In November, Korbel demonstrated CopySeq in a PLoS Computational Biology paper in which the team used it to genotype 500 chromosome 1 CNV regions in 150 genomes sequenced at low-coverage and to analyze gene regions enriched for segmental duplications by comprehensively inferring copy number genotypes in the CNV-enriched olfactory receptor human gene and pseudogene -loci. Using CopySeq, they found that for several olfactory receptor loci, the reference genome appears to represent a minor-frequency variant — a finding that could inform future functional studies.
As far as discovery methods are concerned, Korbel says he is waiting for a technique that can identify unique CNVs, irrespective of their sizes, as well as those in segmental duplications. "There are still regions in the genome that are very poorly understood and are hard to compare between individuals and with current technologies. We are unable to correctly resolve for these regions. ... Some of them are relevant for medicine, so that's a huge challenge," he says. "The data is good and so much is being generating by newer techniques, but we're still not fully exploring all the benefits of this data yet because we're still developing suitable methodologies that combine all types of signature signals in the data. We're obviously trying to improve this, but there's still a challenge there."
Recently, a team of researchers from Yale and Stanford University developed a method for genotyping and CNV discovery from read-depth analysis of personal genome sequencing. In February, they published a paper in Genome Research describing a method called CNVnator, which is based on a combination of the established mean-shifting approach with multiple-bandwidth partitioning and GC correction. The team used 1,000 Genomes Project validation data sets to calibrate CNVnator so it could be applied to CNV discovery, population-based genotyping, and the characterization of de novo and multi-allelic events. The team also reported its identification of six de novo CNVs in two family trios.
"The technology has sort of changed incrementally over the last decade, but the large data sets that we accumulated really made all the difference and allowed groups to start definitively identifying genetic factors that contribute to autism and schizophrenia," says Jonathan Sebat, an assistant professor at the University of California, San Diego.
"In 2011, the biggest game-changer is the short read sequence data, and shortly on its heels, the long-read, third-generation sequence data. The methods for detection of variants and the spectrum of potential disease alleles that you can find now is enormous, so that's a complete game-changer there."
'Game-changer'
In February, Sebat published a paper in Nature describing a large, two-stage genome-wide scan of rare CNVs that associated copy number gains at chromosome 7q36.3 with schizophrenia. Their findings implicate altered vasoactive intestinal peptide signaling receptor gene VIPR2 in the pathogenesis of schizophrenia and indicate the VPAC2 receptor as a potential target for future antipsychotic drug development. "What's new and interesting about that is that the structural variants that we're finding contrast [with] the large microdeletion syndromes that we knew about from the early CNV studies. We're now honing in on the smaller CNVs, not the big, non-allelic homologous recombination-mediated deletions that we used to see," Sebat says. "We're now seeing structural variants that are mediated by other types of mutational mechanisms. The break points are not the same in different patients — they're overlapping, but very different risk alleles. When we get our disease association, we end up finding many different rare mutations in the same gene, often with the same functional impact." He adds that down the road, new CNV findings will not only be used to pinpoint specific genes but identify neurobiological processes in diseases as well.
The University of Washington's Joshua Akey and his colleagues are refining approaches to explore patterns of genomic variation using exome sequencing, as it allows them to use data from thousands of individuals rather than from the mere handful they'd afford using whole-genome sequencing. "It's really striking to be able to look at a data set of 2,000 individuals because you have such deep insight into patterns of variation and you get a real appreciation for the structure of rare variation that you can't get when you only have 20 or 40 individuals," Akey says. "One of the most interesting things that we'll be able to do with thousands of individuals is make very detailed inferences into recent human history. You can't do that unless you have thousands of individuals. For the first time, we can see these dramatic expansions in human population sizes that have occurred in the [past] couple thousand years."
Akey is involved in several structural variation research projects, including one study that looks at the genetic basis of adverse drug responses across dog breeds. He is working, in collaboration with his colleague Evan Eichler and Washington State University's Katrina Mealy, to characterize the distribution of segmental duplications and CNVs across 20 dog breeds with arrayCGH, as it functions at a higher resolution than chromosome-based comparative genomic hybridization.
Coming up
Later this year, Akey and his colleagues plan to publish what he describes as one of the largest and most comprehensive studies into patterns of human genetic variations using high-quality data from roughly 2,000 exomes. Although the rise of exome sequencing has undoubtedly caused excitement and heightened expectations within the structural variation research community, he cautions that the real insights are only going to come from taking a step back and determining how to interpret and compare those sequences from that many individuals. "There's a critical need for further methodological development to be able to fully extract all of the information in these complex data sets and the challenge is that there are so many challenges," he says. "Let's assume that the genotypes we have are accurate: what do we do with that data in terms of making inferences about human history and about disease susceptibility? What's the best way to test for association between rare variants and disease? What's the best way to look for natural selection? There are challenges from the very beginning of the process to the very end of the process. A lot of theoretical work needs to be developed to fully exploit the information that CNVs have. [Lest anyone think that "theoretical work" is free, let us correct the perception. Superior theory is the most expensive part of research - except for all those funds wasted on "garbage in - garbage out"; done with "frighteningly unsophistaced" (most often disarmingly naive) "theoretical background" - AJP]
While the literature contains a growing number of studies that demonstrate associations of common simple CNVs with specific disease susceptibilities, forming a substantial collection of common CNVs, the issue of resolution still hinders researchers who aim to study rare CNVs. "I think we have a very nice catalog of common copy number variants and we have methodologies to pick up the rare CNVs, although not as high resolution as I'd like to see, but it's the cost effective way of doing it," Harvard's Lee says. "We have 18 to 20 of these very clear associations — these are deletions that increase your susceptibility with more common disease — and I think there are more to come. But the issue we have right now is that we don't have a catalog for the rare variants and the smaller ones. Once we start to develop those catalogs, we can start to improve on our arrays, or whatever method we use to detect CNVs in the disease association studies, to see if any of those rare, smaller CNVs are associated with other diseases."
NCI's Chanock, who is also a physician, cautions that the community should be realistic with regard to the potential for all this structural variation data to facilitate improvements in the clinic. "We've started to make very important steps, and when we look at the age of CNVs and a good part of the sequencing that's going on, the discovery element is spectacular — -almost unprecedented," he says. "The plausibility and the meaning of this discovery is complex: each one of these regions requires its own study and it's still a work in progress to reach the level of confidence and validity that's needed to incorporate that into our clinical workflow. We have to be careful with all the ballyhooing about 'The genomic age is going to turn everything into Star Trek medicine,' because I find this dangerously naïve."
[Would anybody with a sane mind fund a nuclear accelerator to smash an atom into myriads of pieces - if nuclear physics was not enough developed to see where the predicted trajectories differ from the actually measued data? More or less the same is happening now, when the "frighteningly naive" (and totally discredited "Gene/Junk" notion, see Dr. Mattick's article in this column) is far too often the only "theory" that experimentalist can use as an alibi. Let's us face it - the non-targeted hunt for "structural variants" could cost cancer patients (literally) and arm and a leg (let alone other even more precious body parts to surgery) - yeat an amassed "Library" of "structural variants", will yield only an extraordinary knowledge at an exorbitant price of what "structural variants" there are - but (as Thomas Kuhn predicted, the knowledge would never automatically translate into "understanding"). The better way is to go for a Fractal Recursive Genome Function Algorithic Approach, that is software enabling. The algorithmic fractal approach would tell you immediately that changing the "c" constant in a Mandelbrot Set would retain the fractality (such structural variants only affecting human diversity), while the ways how the Genome does not obey its own fractal rules would pinpoint fractal defects that violate the fractal rules of the Genome - thereby associated with pathology of phenotypes. This entry can be commented on the FaceBook page of Andras Pellionisz]
Cancer center builds Texas-sized cloud [Private cloud!]
Computerworld
Beth Schultz
04.04.2011 kl 04:32 | Network World (US)
As researchers at The University of Texas MD Anderson Cancer Center work at "making cancer history," they're doing so with the help of compute power and storage capacity from a private cloud.
But this is no ordinary cloud.
After all, when you're researching something as complex as the human genome you tend to think big, and MD Anderson's cloud reflects that type of ambition and scale. We're talking 8,000 processors and a half-dozen shared "large memory machines" with hundreds of terabytes of data storage attached, says Lynn Vogel, vice president and CIO of MD Anderson, in Houston.
A different path
And while MD Anderson's general server infrastructure uses virtualization, the typical foundational technology for cloud, this specialized research environment doesn't. Rather, the organization uses an AMD-based HP high-performance computing (HPC) cluster to underpin the research cloud.
"We're currently implementing the largest high-performance computing environment in the world devoted exclusively to cancer," says Vogel, who was recently named Premier 100 IT Leader honoree by our sister publication, Computerworld.
The data and processing capacity are available to the MD Anderson cancer researchers as needed, whether they're sequencing human genomes or investigating radiation physics, epidemiology, dosing calculations for radiation therapy or running simulations for clinical trial activities. About three dozen principal investigators, who each have anywhere from two to 10 assistants, regularly tap into the research cloud, Vogel says.
To access the cloud, they use a service oriented architecture-based Web portal called ResearchStation.
"When you look at the classic definition of cloud computing as enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released, that's in fact how we're approaching our environment," Vogel says.
Enterprise Cloud Services: The agenda
However, he notes, the MD Anderson cloud doesn't currently have a chargeback mechanism - an oft-cited but, at this point, little used cloud attribute. "We don't require a chargeback mechanism because we manage demand largely by a peer review process. The actual determination of priority for using resources is driven by clinicians and researchers themselves, not by IT people," Vogel says.
What this means, he adds, is that he never needs to plead a case for, say, more storage. "They're the ones going to executive management, saying, 'You really have to increase the capacity of this capability or that capability for us to continue to do our work and maintain our rating as one of the top cancer centers in the world," Vogel explains.
More, more, more
In addition, MD Anderson doesn't experience the typical up and down spikes in usage that other enterprises might encounter.
"We find that both the clinicians and researchers in the field of medicine have what I would label 'an insatiable demand' for computing resources, and the demand curve just keeps going up," Vogel says.
He notes that the 8,000-processor HPC sitting at the heart of the private cloud already operates at 80% to 90% capacity, as did its predecessor, a mere 1,100-processor machine. Memory-intensive applications rely on six 512-GHz, 32-CPU servers.
The cloud build-out at MD Anderson dovetails with the organization's expansion into a third data center, due to open this summer.
This will be the second new data center the organization has opened in a four-year period - and these are good-sized operations, with 12,000 to 15,000 square feet of raised floor in each, Vogel says. "We thought our second data center would last us four to five years, but it was full within 18 to 20 months. We had to turn our disaster recovery site into a production data center as we built another one," he adds.
The MD Anderson data centers house roughly 3 petabytes of data, a "somewhat surprising amount," Vogel says, since the cancer center is primarily a 500-bed hospital. But the volume of research data, at about 1.4 PB, now exceeds the amount of clinical data at MD Anderson.
"Anybody who looks at genomic medicine and the sequencing of human genomes begins to realize that there's a tsunami of data coming out of those processes," he notes. "So, ironically, today at MD Anderson we have more data storage capacity devoted to research than we do for clinical care, and that includes all of our images. We're being hit by extraordinary amounts of data that needs to be managed and stored." [See YouTube below, that predicted today's conditions 3 years ago - AJP]
To handle the cloud's storage requirements, MD Anderson uses an HP-Ibrix system that supports extreme scale-out. It chose the Ibrix system because of its reliability and its ability to present storage seamlessly over Ethernet or InfiniBand, using CICS, FTP, HTTP, the Linux client, NFS and other technologies, Vogel says. "This capability also enables us to do data tiering through the cluster," he adds.
Manageability also has been a boon. "Having HP as the end-to-end vendor ensures that all parts will fit together and fit into our monitoring system without any clashes," Vogel says.
While MD Anderson uses HP Storage Essentials and CIM to manage each storage unit, it relies on the Ibrix management server, Fusion Manager, for a top-level view. Each server also reports into Fusion Manager, Vogel says.
"As an added bonus, and very much a consideration in a constrained healthcare personnel environment, is the ability to operate our entire cloud configuration with minimal personnel involvement - just two people," Vogel says.
Public cloud: Not on your life
Vogel says he's talked to some public cloud providers who would love to host those MRIs, CT Scans and other clinical images - more than 1 billion of them - within their infrastructures. But no can do, he says.
"We've looked into this, but quite honestly, we've found on performance, access and in the management of that data, going to a public cloud is more risky than we're willing to entertain," Vogel says. "This goes directly to the point that this is identifiable patient data ... and we're just not comfortable with the cloud given the actionable capability of a patient should there be a breach."
What's more, public cloud providers simply can't provide the level of business knowledge that MD Anderson's IT staffers can, some of whom are PhD scientists themselves, Vogel says.
"When you're in the business of biology, which we are, it's a different ballgame in terms of understanding the structures of data, the kinds of access and models used, and the applications that need to be available," Vogel says. "As much as public cloud providers would like us all to believe, this is not just about dumping data into a big bucket and letting somebody else manage it."
Cancer as Defective Fractal Recursive Genome Function (Pellionisz and Lander et. al trigger escalation of fractal approach)
[Google Tech Talk YouTube, The Principle of Recursive Genome Function, 2008]
[The "double lucid heresy" of brain cell growth programmed by an iterative fractal recursive genome function from proteins to DNA - surpassing both mistaken axioms ruling for half a Century; the "Central Dogma" AND the "Junk DNA" misnomer - are traced back to the fractal model of a Purkinje brain cell (Pellionisz, 1989, see page 461 Fig. and point 3.1.3. "repeated access to genetic code", see also acknowledgement on pp. 462 reference to the NIHM grant application - denied because of double heresy, and renewal of ongoing NIH grant to AJP also denied). Based on the core-concept, FractoGene (fractal DNA governing growth of fractal organelles, organs and organisms, Pellionisz, 2002) could only be established in a stealth-mode (USPTO, 2002), till the results of NIH "ENCODE" concluded (2007) that "the community of scientists would have to re-think long-held beliefs (Collins, 2007). Upon this clearance, both the peer-reviewed The Principle of Recursive Genome Function and the Google Tech Talk YouTube could be swiftly disseminated (click above, 2008).
The "fractal approach to DNA" ("The Principle") received a major impetus (mark B) when, within 6 months of its publications was explicitely cited by about 15 authors (see e.g. Shapshak et al, 2008 and Chiappelli et al, 2008, and the fractal approach to recursive genome function was also presented (invited by Prof. George Church) in Cold Spring Harbor, Personal Genomes, Pellionisz, 2009, September (see escalation from Mark A).
As the manuscript was handed over to Dr. Lander prior to its publication, along with a dozen of co-workers a Science cover article appeared (Lieberman et al. 2009, October), showing a fractal folding structure of the DNA. The article with Eric Lander, Science Advisor, amounted to convey the message "Mr. President, the DNA is fractal! Although the Lander et al. paper (2009) reached back by direct citation to the seminal idea of fractal folding by Grosberg et al. (1993) - similarly as Pellionisz' The Principle of Recursive Genome Function (2008) reached back by direct citation to the seminal idea of fractal neural growth by Pellionisz (1989), the mutually reinfocing papers by Pellionisz (2008) and Lieberman et al. (2009) created the flood of viewership of Pellionisz' YouTube (2008).
Thus, with Pellionisz (2002), Collins (2007), Church (2009), Lander (2009), Schadt (2010), Mattick (see article below, prized in 2011) there is a slew of leaders calling for new axioms, some implicitely or explicitely referring to the fractal nature of DNA, setting of a new school in Genomics (HoloGenomics). Some further representatives of the followership include:
Jean-Claude Perez (2010) Codon Populations in Single-stranded Whole Human Genome DNA Are Fractal and Fine-tuned by the Golden Ratio 1.618 Interdiscip Sci Comput Life Sci 2: 228–240. DOI: 10.1007/s12539-010-0022-0
“Since the PHYSICAL structure was found fractal (providing enormous amount of untangled compression), it is reasonable that the LOGICAL sequence and function of the genome are also fractal.” (Pellionisz, A., 2009, personal communication: From the Principle of Recursive Genome Function to Interpretation of HoloGenome Regulation by Personal Genome Computers.Cold Spring Harbor Laboratory. Personal Genomes Conference, Sept. 14–17, 2009).
For several years, ...researchers like A. Pellionisz advocated ways to analyze and detect fractal defects within whole genomes. This is based on recursive fractal exploration methods and artificial neural network technologies (Pellionisz, 2008).
Simone Caramel and Sergio Stagnaro (2011) in their "Quantum Biophysical Semeiotics and mit-Genome's fractal dimension" attempt make generalizations from fractal dimension to chaotic, thermodynamical and quantum-states of the genome.
Alexei Kurakin (2011) "The self-organizing fractal theory as a universal discovery method: the phenomenon of life" attempts to make generalizations from fractal theory to non-equilibrium thermodynamics in genomics, expanding the horizon to life sciences.
The PostModern era of Genomics, HoloGenomics that unites Genomics and Epigenomics in terms of Informatics is upon us. It is a new science- and since it is at the same time a new industry to save lifes as urgently as possible, science results can be immediately verified (or falsified) - and if they are algorithmic (software enabling) can be put into use at once.
The Trouble with Genes [Article Gets prize - Mattick joins leaders to admit that basic premises were all wrong - AJP]
Cosmos
by Elizabeth Finkel
'Junk DNA' research inspires Higher Education Journalist of the Year
3 March 2011
The article, "The Trouble with Genes", was published in Issue 31 of Cosmos in February 2010 and details Professor's Mattick theory that so-called 'junk DNA' actually helps regulate our development. [Why does a year-old article get Prize? Because the message looms greater and greater as the "Big Genome Letdown" sours - AJP]
This research runs counter to the traditional scientific dogma that the only useful genetic material is genes, which contain the instructions to make proteins, and everything else in the genome is 'junk'. Dr Finkel, who herself has training as a geneticist, said Professor Mattick's view "puts him way ahead of the curve".
"We know he is leading the scientific world with his ideas and we really ought to be writing about them but it is difficult to understand what he has discovered - one seems to require degrees in both computing and genetics," Dr Finkel said. [This is a reason why AJP, originator of FractoGene, can excel - with Ph.D. in computer technology, Ph.D. in biology and Ph.D. in physics - AJP]
The Higher Edcuation Awards, conducted in conjunction with the National Press Club, are judged in two categories, each with awards for print and broadcast. The Journalist of the Year is selected from the category winners and is awarded a $10,000 study tour funded by Universities Australia. Dr Finkel's category win was for excellence in communicating research and innovation, teaching and learning, equity and access, social inclusion or Indigenous education issues in print.
National Press Club President and Chair of the judging panel, Laurie Wilson, said, "Elizabeth's story displayed all the hallmarks of outstanding journalism - she tackled an extremely complex topic, translated it into layman's language and turned it into a great story."
The other judges were: Dr Matthew Ricketson, author and former journalist who is now Professor of Journalism at the University of Canberra; Mischa Schubert, NPC Vice-President and political reporter for The Age; and Malcolm Colless, journalist, media consultant and former Director of News Ltd.
---
Junk DNA was once thought to be little more than gibberish. But it may actually be the software that controls a complex organism.
"WHAT'S A GENE, DAD?" I'd like to be there when the nine-year-old son of iconoclastic geneticist John Mattick pops the question. It used to be simple - a gene coded for a protein.
But when I put that question to Mattick, based at the University of Queensland, his response was as disturbing as it was confusing.
"Genetic information is multilayered and a gene can convey lots of different information into the system. It's almost like we've moved into hyperspace in terms of information coding and transfer."
Mattick's cutting-edge theories about gene regulation have been published in the British journal Nature and even appeared in the New York Times. Yet, even though I was once a geneticist, I couldn't fathom his answer.
It seemed my fears had been realised and I'd been left behind by the genetics revolution. In a desperate ploy to catch up, I asked how he would explain a gene to his young son.
"I would just tell him, 'it's an old-fashioned concept', and then explain about information networks. He's a child of the digital generation - he won't have any trouble with it." [This is a reminder that presently "gene" has ZERO universally accepted definition. You get as many fragmented definitions, as may experts you ask - the algorithmic (software enabling) definition is FractoGene - AJP]
IT'S NOT JUST ME who's confused. I checked the 2008 edition of my favourite text book, Molecular Biology of the Cell.
The traditional definition is still there in the opening chapter. But as you read on, you sense the textbook struggling, trying to wrestle the gene back into the box of a definition.
Mattick prefers not to try. And a lot of other geneticists are starting to think this way too. As Ed Weiss at the University of Pennsylvania told me, "the concept of a gene is shredding". [It is not really shredding - it is becoming fractal - AJP].
The genomics revolution is largely to blame. Scientists were shocked when they found out how few 'old-fashioned' genes we actually have - about the same number as the humble nematode worm (Caenorhabditis elegans).
In fact, almost all multicellular creatures with the complexity of a worm or greater have about 20,000 genes. But for Mattick, the death knell of the traditional concept of the gene was triggered by another revolution altogether - that of the digital information age.
Scientists have always understood biology in terms of the technology of the day. The brain, for instance, was considered by the Ancient Greeks and Romans to be an aqueduct for pumping blood; inhabitants of the 19th century likened it to a telephone exchange; those of the 20th century likened it a personal computer. Now scientists compare the brain to chaos and distributed functions of the Internet. [Indeed, those who actually know nonlinear dynamics, with chaos/fractals the two sides of the same coin, declared FractoGene as soon as ENCODE invalidated the premises of the old school; see peer-reviewed science paper (about 10,000 downloads) and Google Tech YouTube (now at some 10,300 views) in 2008 - AJP].
WHEN IT COMES to the gene, Mattick likes to point out that scientists cracked its code in the 1950s, when the world was purely analogue [This perhaps the only major science mistake of the brilliant science writer. The code was never "cracked" - only "revealed". Those in WWII intercepting coded messages of the Japanese and German forces know exactly the difference between "intercepting" the exact transmission - and "cracking the code, to reveal its meaning - AJP].
We had vinyl records, slide rules and mechanical cars. We were primed to recognise the gene as a recipe for an analogue device - such as a protein, for instance.
Proteins are the analogue devices that operate the chemistry of life: the enzymes that metabolise food; the mortar and bricks of tissues; the motors of muscles; the hormones that transmit signals; and the ferries that carry oxygen through blood. We recognised a gene as being the recipe for a protein.
Today, iPods store the equivalent of many thousands of vinyl records. Microprocessors in cars can control everything from the engine to the stereo.
The digital revolution has succeeded in taking vast amounts of information and compressing it. Mattick believes something very similar happened to the gene. In the course of evolution, it went digital. [Dr. Mattick may not be on firms grounds when it comes to information theory - or the science writer may have misunderstood his statement. The DNA has always been digital throughout the evolution - only human interpretation did not seem to recognize this fact for far too long - AJP]
In 1953 we got our first inkling of how genes work. Scientists knew that genetic information was carried by the threadlike molecule DNA - a polymer of four repeating molecules adenine, thymine, cytosine and guanine or A, T, C and G. But how did this thread carry genetic information? Perhaps a picture would reveal its secret.
BRITISH CRYSTALLOGRAPHERS Rosalind Franklin and Maurice Wilkins bombarded crystals of DNA with X-rays and observed an enigmatic regular structure.
The University of Cambridge's James Watson and Francis Crick figured out what it was. Like the elegant spiral staircase of the Louvre in Paris, it was a double helix. And the moment they figured out the structure, the secret of life was revealed. [No, it wasn not. Reproduction (copy mechanisms) of the DNA was, indeed, discovered, but how the fractal DNA governs the growth of fractal organelles, organs and organisms were not at all revealed. Certainly never understood by Dr. Crick (because of his adherence to his "Central Dogma" till the end of his life, and the jury is still out on Dr. Watson - AJP]
Life copied itself by splitting the helical ladder down the middle. Each half then became a template for generating a new copy because each DNA letter on the split rung specified what its partner must be: A only paired with T; C would link only with G.
Watson and Crick had figured out how the code of life copies itself. Some five years later, Marshall Nirenberg, Har Gobind Khorana and Robert Holley in the U.S. figured out what the code means.
The letters of DNA spelt words that coded for amino acids - the building blocks of proteins. Until then scientists had been kids pulling Tinkertoys apart, but now they had the instructions for assembling them. Mankind had discovered the awe-inspiring logic of life.
Genes were made up of a string of DNA, and DNA coded for proteins. DNA happened to have a go-between, a disposable working copy called 'messenger RNA'.
RNA was chemically similar to DNA, but flimsier. Just as an architect will run off copies of a blueprint, so messenger RNA was the working copy used on the protein construction site.
HAVING CRACKED THE SECRET of life, these scientists now started calling themselves molecular biologists (biologists who studied living molecules). And they became rather sanguine, so sanguine they started talking about dogmas.
"We had two central dogmas that were regarded as universal truths in the '60s," said geneticist Bob Williamson, now an emeritus professor at the University of Melbourne.
"The first was 'DNA made RNA made protein'. The second was that the genetic code was universal: what was true for E. coli would be true for an elephant".
The shock to the system came in 1977. Researchers by now were quite au fait with genetic code. Thanks to its universality, they could insert the predicted DNA code for a human gene into a bacterium and out would pop the correct protein.
Yet no-one had ever glimpsed the 'mother code' of a human gene. It was packaged in a chromosome within the dark nucleus of the cell, like a hallowed tome in the crypt of the Vatican library.
IN 1977, RESEARCHERS decided to fish out the mother code for the gene that makes globin (a component of haemoglobin). But no-one was prepared for its size - the globin gene was way larger than it ought to have been.
Williamson, whose group at St Mary's Hospital in London were the first to put the human globin gene into bacteria, remarked in a Nature editorial: "Once again we are surprised".
The explanation was bizarre. The mother gene did indeed carry the predicted code for globin, but it was strangely interspersed with gibberish.
Imagine that the predicted DNA code for globin was written with the English letters: G-L-O-B-I-N.
The mother code appeared as:
G-L-z-z-z-q-q-O-B-s-r-m-b-I-N.
Researchers panicked. What was this gibberish? Was the genetic code not universal after all?
But the panic soon subsided. Whatever gibberish had infiltrated the mother code, it disappeared from the working copy - the messenger RNA - by the time it got to the factory floor.
Like an edited home video, the internal junk had been clipped out and the good bits spliced back together again. Indeed the process was dubbed 'splicing'. The bits that were spliced together were named 'exons'; the internal junk, 'introns'.
With everything neatly named and explained, "the world collectively breathed a sigh of relief," says Mattick. The hallowed central dogma had been saved.
THERE WERE LOTS OF justifications for dismissing junk DNA as 'junk'. Not only did it lack code words for amino acids; it turned out 50% of the junk was comprised of inane repetition.
These repetitious tracts seemed meaningless. But researchers had a good notion of what many of them were. Most of the repeats were 'transposons' or 'jumping genes'.
Jumping genes, which may have originated from invading viruses, have the ability to copy themselves independently of the rest of the genome and then become inserted randomly throughout the genome.
Then there was another reason to suspect that much of the DNA of a species was junk. The total amount of DNA seemed to bear very little relationship to the complexity of the organism.
An amoeba for instance, had a thousand times more DNA than a human. Sometimes it seemed cells multiplied, but forgot to divide, ending up with vast amounts of DNA. It seemed as though DNA just liked to go along for the ride.
NOT EVERYONE DISMISSED junk DNA. Physicists such as Eugene Stanley at Boston University looked for patterns in junk DNA and found long-range interactions more typical of language than gibberish.
Malcolm Simons, a Melbourne immunologist, stumbled upon junk DNA in the course of testing people's tissue types. Tissue compatibility depends on MHC genes, as do some aspects of immunity.
Yet he found the pattern of junk DNA surrounding the genes was a better predictor of the tissue type. For him, junk turned to treasure.
Mattick's departure from the dogma seems to have been driven more by instinct than evidence. Blame it on his genes: "I've got a natural tendency to challenge everything because of my Irish background," he says.
Mattick recalls sitting in a pub in 1977 during his postdoctoral stint at the Baylor College of Medicine in Houston, Texas, and thinking "Maybe this is telling us something?" But for 16 years, while he built his career as a bacterial geneticist, the problem of junk remained an "intellectual hobby".
In 1993, Mattick felt he deserved a break. He'd completed the Herculean task of setting up an entire new institute from scratch - the Institute of Molecular Biosciences at the University of Queensland in Brisbane. What better reward than to spend a sabbatical at the University of Cambridge scratching his intellectual itch?
He had slowly been building a theory in which RNA was central. The current dogma said that most of the RNA made by the genome, the RNA from introns, was bound for the scrap heap. But Mattick thought otherwise.
Simple organisms such as bacteria do not carry introns, but complex creatures do. Mattick wondered if the scrap RNA was part and parcel of that complexity. After all, RNA has amazing versatility: it is a code-carrying molecule that can recognise matching codes on both DNA and other bits of RNA. And it can also form extraordinary three-dimensional structures to mesh with proteins.
IN MATTICK'S THEORY, the scrap RNA or 'non-coding' RNA as it became called, was not flotsam and jetsam floating off a sea of junk DNA. Rather this scrap was more akin to the optical fibres of a modern high-rise building.
An 18th century time traveller, spying these cables, might pass them off as scrap compared to the recognisable analogue components of the building like bathrooms, kitchens and bedrooms. Yet, just as the cables are crucial for the building's communications and controls, so scrap RNA was crucial to the communications and control of a multicellular organism.
The major problem with his theory was that there was no experiment to prove it right or wrong. So Mattick decided to spend his sabbatical in the library looking for "circumstantial evidence".
What he searched for with the most alacrity was evidence to prove him wrong. "The critical observations were the ones that would show it was bunk. Then I could just return to my lab and forget about all this stuff".
TWO BITS OF EVIDENCE threatened to abruptly end to his quest. One was fugu - the pufferfish (Diodon). Fugu is famous for the tetrodotoxin, which kills dozens of Japanese diners each year and for its tiny genome - about an eighth the size of our own.
Nobel Prize winner Sydney Brenner, then at the British Medical Research Council's Laboratory of Molecular Biology in Cambridge, was in the process of reading fugu's DNA sequence. Rumour was the fish had barely any introns, and if a complex vertebrate such as fugu had no introns, then Mattick's theory about regulatory RNA must be wrong.
He paid a visit to Brenner to discover the terrible truth. It turned out that while most of fugu's introns were very small, some were really big. Mattick's theory survived.
The next mortal threat was a publication reporting that introns, once clipped out of the messenger RNA, were destroyed within seconds. If introns were as ephemeral as a puff of smoke, how could they perform any function?
Mattick scrutinised the report closely. It showed that introns were edited out of the main message within seconds. But as to how long they persisted before being shredded, no-one knew. Perhaps, he speculated, it was long enough to do something.
Mattick, of course, was also on the lookout for evidence that would support his theory. He found some. The fruit fly possessed a set of genes that were responsible for its body plan, known as the bithorax complex. It turned out that a crucial stretch of this DNA produced RNA that did not code for protein. What other function might this RNA have?
MATTICK RETURNED to the University of Queensland with his theory intact. He started writing papers articulating his theory that non-coding RNA (shorthand for non-protein coding RNA) was the high-level coding language of complex organisms.
His approach remained one of gathering circumstantial evidence. Together with co-workers in mathematics and computer science, he amassed some compelling observations.
For instance, as more and more species became the darlings of DNA sequencing projects, Mattick noticed a delectable relationship: there was no link between the complexity of the critter and its total amount of DNA.
But there was a clear relationship between the proportions of junk and protein-coding DNA: as the complexity of the organism increased, so did the relative amount of junk.
And then genome sequencing delivered the pièce de résistance: making a human being required no more old-fashioned genes than making a worm or fly.
Clearly complexity was encoded elsewhere, and according to Mattick and a growing number of converts, it was in non-coding RNA.
Mattick's genetic programming theory, outlined in a recent edition of the Annals of the New York Academy of Science, started to assume its current form. In simple terms it goes like this: bacteria could make do with using analogue devices - proteins.
But even these single-celled critters devoted a large portion of their genetic information to the task of control. If organisms were going to get more complex and coordinate decisions between trillions of cells, they needed to develop a more compact regulatory language.
Just as engineers turned to digital coding to move from LPs to iPods, biological systems turned to RNA to evolve from bacteria to people. According to Mattick, RNA, like DNA, carries coded digital information in four letters that can rapidly interact with other parts of the code, much like the self-modifying or feed-forward routines of some computer programs.
As Mattick was building the framework of his model, the rest of the world started providing the bricks and mortar. Big time.
SINCE 1993, THERE has been an avalanche of evidence on the surprising roles of non-coding RNA.
Most of our DNA may well have originated as 'junk' but that junk has been put to work. One of its most common jobs is to produce tiny bits of RNA known as 'microRNA' that targets other RNA for destruction. MicroRNA has been shown to shut down the activity of protein-coding RNA in everything from petunias to people.
Junk DNA also plays another crucial function: it guards the DNA code from invasion by retroviruses or so-called jumping genes, which can hop about in the genome causing dangerous mutations.
Junk DNA is itself largely composed of former interlopers but, like a patriotic immigrant, it does its best to prevent any further invasions.
The RNA transcripts that run off junk DNA are still a close match to live viruses or active jumping genes, and if these junk transcripts meet up with their relatives, they inactivate them.
JUNK DNA MAY play an even more profound role in the workings of multicellular animals. A crucial part of being multicellular is that different cells do different things - they are not all reading from the same page of the genome hymn book.
The first step toward specialisation is folding down those pages that are not to be read, and it seems junk DNA guides the folding process. For instance, females carry two X chromosomes, but only read the contents of one.
During embryonic development, one of the X chromosomes is folded away, a process initiated by a large string of non-coding RNA called 'Xist'.
While most tissues of the body want to keep jumping genes from jumping, the brain might have other ideas.
Fred Gage's lab at the Salk Institute for Biological Studies in La Jolla, California, found evidence that jumping genes known as 'LINE-1' or 'L1', which are permanently deactivated in other cells, become active during development of the human brain.
The L1 genes replicate and insert randomly, sometimes creating as many as 100 extra copies per cell. This variation among neurons in our brains could be the basis for individual differences in neural circuitry and may open up a new way of looking at neurological disorders.
Junk may also have played a crucial role in our evolution. At the DNA level, one of the things that distinguishes primates from other mammals is the invasion of a million copies of a jumping gene that goes by the name of 'alu'. It now occupies 10.5% of the human genome.
JUNK RNA MAY also account for some of the difference between humans and chimps. Our DNA is 99% similar, but one of the regions that differs is the so-called 'HAR1', or human accelerated region 1.
It turns out HAR1 produces a 118-letter non-coding RNA, which is highly active in the brain.
In 2005, Mattick resigned as director of his institute and went back to work in the lab. Tools to explore the function of non-coding RNA had arrived in the form of heavy-duty sequencing machines.
In just one week a 'next generation' sequencing machine can read three billion letters - the equivalent of an entire human genome. Not long ago, that task took the combined forces of the Human Genome Project 14 years to complete.
Mattick and University of Queensland colleague Sean Grimmond have been in collaboration with like-minds at Japan's RIKEN institute. They have been scouring the output of mouse and human genomes, trying to put together a comprehensive catalogue of their RNA output. The database called 'Fantom' (functional annotation of mammalian genomes) now contains millions of transcripts.
THE LATEST DATA is mind boggling. As Grimmond tells me: "Each gene is capable of seven different transcripts, some of these code for proteins and some don't."
Trying to make sense of this deluge is the challenge. "[But] we're getting good at asking questions about ludicrous amounts of data," he says.
For Mattick, the human genome is an RNA machine. But is his theory well and truly vindicated? Not yet. [Why not? One may ask. A possible explanation is that Dr. Mattick's theory is not algorithmic; thus not "software enabling" - AJP] Though it would be hard to find anyone today who blithely dismisses junk DNA, few are willing to go as far as he is and say that the RNA read from junk code is the software that controls a complex organism.
For example, Claude Desplan at New York University has studied fruit fly development for 25 years and argues that complex genomes, in flies or people, are still fundamentally controlled by proteins. While acknowledging that some junk has a role he says, "most of junk DNA is still junk".
Mattick, though, is convinced that our genome is way ahead of anything that IT designers have yet imagined. "The genome is so sophisticated, that there are lessons in information storage and transmission that will be really useful," once we figure it out, he tells me. "The human genome is a similar size to Microsoft Word, but it makes a human that walks and talks."
Notwithstanding the deluge of papers he has authored in top journals, Mattick still seems to be on the fringe. And you get the impression that's just where he likes it.
Adventures in Extreme Science [Eric Schadt and other kooks - AJP]
Esquire
March 22, 2011, 12:24 PM
From Crick and Watson through J. Craig Venter, we had all our eggs in one basket — molecular biology, gene mapping, whatever you want to call it. It failed. And now we're counting on this guy.
By Tom Junod
There may be another scientist in the world as smart as Eric Schadt. After all, scientists are a pretty smart lot, even though you'd be surprised at how few want to change the world, and how many of them have the trudging souls of brilliant, dutiful clerks. There may even be another scientist in the world as popular, as in demand as Eric Schadt, even though Eric works hard at everything he does, including his popularity, and is engaged, at any given time, in at least ten collaborations with other top scientists, not to mention the production — just last year — of a profligate thirty-five scientific papers, not to mention the delivery, year in and year out, of about forty talks and presentations after receiving invitations to deliver two or three hundred. (You'd also be surprised by how social a lot of scientists are, and how many parties they go to.) But if you're looking for a scientist whose great popularity rests in tirelessly writing papers and delivering speeches whose implicit and sometimes explicit message to the most eminent minds in his field is that they're wrong, that they've failed, and that the best way for them to stop wasting their lives is to follow him in a scientific revolution that he admits might not even work: Well, then you'd probably have to narrow your search a little bit. It takes a pretty smart guy to tell the smartest people in the world that all their success, all their hard-won knowledge has led them to a dead end ... that the approach they've taken has been a little, um, simplistic. It takes Eric Schadt to say that — and then to make the damned sale.
What's he selling? Well, the first way to answer that is to say what he's not selling. He's not selling molecular biology. He's not selling the last big revolution in biology, the revolution that made biology the dominant science of our time and was supposed to save us. The Human Genome Project, which at a cost of about $3 billion mapped the twenty-three thousand or so genes that are said to encode all of human existence? That's molecular biology, man — its signal triumph, its apotheosis, the culmination of an effort that began with the elucidation of the structure of the DNA molecule, picked up speed and funding with the War on Cancer, and then, well, figured everything out for us, from the causes of cancer to the roots of belief. (Hint: They're both in the genes, which govern our biology by the proteins they express.) And so we believed. We believed that in our genes was the code for not just our proteins but our fates; we believed what we read in the newspapers and heard on television, that a gene for this had been found, or a gene for that; and we believed above all that if a cause for a certain disease had been discovered, then a cure had to be on the way. Indeed, without quite knowing it, we believed in the logic of molecular biology, its inexorable momentum, which we equated with scientific progress itself. The logic was this: one gene at a time. One gene at a time, we'd triumph over disease, and if we figured out the right gene, the right protein, and the right pathway between genes and proteins, maybe we'd even triumph over death itself. How triumphant was molecular biology? It was so triumphant that we believed in it (and still believe in it) even when it has gone a long way toward bankrupting the pharmaceutical industry with drugs like the painkiller Vioxx and the diabetes medication Avandia — drugs that hit their molecular targets but also cause catastrophic side effects by hitting other unforeseen targets as well — or drugs that never come close to making it to market at all. We still believe in it even when nearly ten years after the mapping of the genome, it has radically increased the cost of drug development while delivering next to nothing in return.
That's right: nothing. Oh, sure, knowledge, yes. Humans know more about the workings of individual genes, proteins, pathways, and kinds of cells than we ever have. We know so much that surely all we need is time. Because one gene at a time takes time. And drug discovery takes time. And FDA approval takes time, gobs of time, epochal engines of time ... and now here comes Eric Schadt saying, Don't hold your breath. Here comes Eric Schadt saying that time isn't the problem with molecular biology — molecular biology is. Reductionism is. Willful oversimplification is. The very idea that humanity can enlist the aid of grunting lab-coated Sherpas and march toward pharmaceutical nirvana one gene at a time is. Here comes Eric Schadt saying, "All right, so the idea was that understanding individual proteins and their missions could open up our understanding of the complexity of living systems. That's failed. That's turned out not to be true. And that was the dream, right? So it's a crisis. We understand simple processes, but we have no idea how simple processes fit into larger processes." You get that? Molecular biology — the great scientific god of our age, not just the answer to but the explanation for our prayers — in crisis! Not true! A failure! Dead wrong! No wonder that a few years ago, Schadt gave one of his talks at Columbia and five minutes into the speech, a gray biological eminence stood up and said (in Eric's telling), "How dare you dismiss all the biology that has made us so successful today? My recommendation to everyone in this audience is not to listen to what this man has to say."
The gentleman then turned and walked out, in front of a few hundred people. But here's the deal: Everybody else stayed. And listened. Because you see, at the time, Eric Schadt was working for Merck and was already getting a reputation as the guy who was remaking Merck from the inside out. And because Schadt's not just (or even) a critic, not some apocalyptic scold. He's funny. He's a real character. He's the life of the party, with a line of bullshit he likes to call bullshit, a mad motormouthed charisma that he combines with a mad cackling awareness of the absurdity of all intellectual endeavor, especially his own. He has a shtick, a pitch, but he also has a vision, and that's what he's selling, with evangelical fervor. And the vision, basically, is this:
Okay, so focusing on one gene at a time doesn't work, doesn't explain what causes disease, indeed falsifies the causes of disease and makes it nearly impossible to develop the drugs we need to cure it. So how about focusing on thousands of genes at a time? How about focusing on thousands of genes and thousands of proteins with some enzymes and environmental factors thrown in for good measure? How about getting bigger instead of getting smaller? How about going for complexity instead of simplicity? How about implicating not single genes and single pathways of proteins in disease but whole vast networks of genes and proteins — networks that have been invisible to us until now? How about taking advantage of the technology and the data that's become available over the past ten years and using it to create models of the living world that are nearly as complex as the living world itself and by God nearly as large? Oh, sure, it sounds impossible. Maybe it is impossible. But that's why Eric Schadt wants not just to remake the underpinnings of biological science but rather to remake science itself — the way it's done. Okay, so the complexity of living systems — and the amount of data they generate — turns out to be too much for even the most heroic of individual scientists to master. All right then: Biologists have to form networks that mimic the biological networks they're studying. The networks between genes and proteins turn out to be organized socially, like human networks, and so human social networks will be required to understand them ... with Eric Schadt at their center/
Basically, most anecdotes about Eric Schadt involve the two things that have enabled him to be both highly connected and a revolutionary — smarts and salesmanship. For example, here's how he met his wife. He was a graduate student at UC Davis, going for his Ph.D. in pure mathematics. Pure math is the hardest, most abstract and conceptually demanding discipline you can find, which is why Eric was studying it, and why a young woman named Jennifer Harkness one night gave him a call. She was a freshman with nothing to do, and she and her friends were making prank phone calls. The phone rang, Eric picked it up, and he heard a voice say, "Hi, this is Jenny." He said instantly, "Hi, Jenny — is this a prank phone call?" Jenny and her friends started screaming; they couldn't figure how he'd figured out what they were doing. He explained that he was an inveterate prank caller himself — he liked calling seismologists and telling them that he was feeling tremors — and when Jennifer stayed on the line, he found out that she was, like him, from Michigan, and they had some things in common... .
He made the sale, in other words, and now he and Jennifer live in Palo Alto, California, with their five blond kids, the big sprawling brood that inevitably causes other scientists to remark, "Oh, you're an optimist!" when he tells them about it and also prompts him to articulate an elemental personal philosophy when he's sitting at his big dining-room table one morning, trying to finish another of his groundbreaking papers while bouncing his five-year-old daughter in his lap and at the same time checking his oldest son's math homework — "I can never do too much of anything. Bring it on, baby." He's forty-six years old, and he has the moonfaced swagger of a former child star, albeit one who grew up to be a football blocking back. He's stocky and strong, with a knobby nose and an imposingly lumpy brow and a disheveled head of brown hair spit-curled to his forehead by the sweat induced by his Herculean labors. Think Jack Black in a white lab coat and you get the picture ... except that he doesn't wear a lab coat. He's developed his own standardized scientific uniform — a white tennis shirt with a dark-blue Polo insignia and a pair of hiking shorts — and he wears it as faithfully as Steve Jobs wears blue jeans and a black mock turtleneck. He wears it when you see him in the morning and he wears it when you see him at night, so that you don't really know if he's ever gone to sleep or ever changed, and he even wears it when he takes his motorcycle to work, though the motorcycle is to motorcycles exactly what Eric Schadt is to biology — a baroque exaggeration of normal capabilities that either promises deliverance or threatens obliteration. But let Eric, who's something of a gearhead in both the civilian and scientific aspects of his life, describe the specifications of the BMW S 1000 RR:
"Four hundred pounds, 200 horsepower, the fastest thing out there, zero to 60 in 2.9 seconds, the first superbike." Well, at least he wears a helmet — and not just a helmet but a big black-visored one with a video camera rigged on top so that he can record the sublime experience of riding his superbike or the inevitably annihilating experience of being run off the road and crashing it. "People don't like being passed," he worries, but of course he passes them anyway on the way to work, hitting 100 miles per hour on the street and 120 miles per hour at the office park and popping the occasional celebratory wheelie in nothing but the white shirt, the short pants, and the mitered black helmet that makes him look like some kind of postmodern grenadier, sporting technological plumage.
And then, when he gets to work, three miles from his house, he gets to ride something that goes even faster.
He's one of those guys with a coveted brain, so he's one of those guys with a lot of gigs. He's cofounder of a nonprofit organization called Sage Bionetworks, which is dedicated to facilitating biological research through an open-source sharing of data. He's been trying to start his own institute — the catchily titled Institute for Multi Scale Biology — at the University of California, San Francisco, though he's constantly getting wooed, and it seems inevitable that he'll wind up with a big academic appointment somewhere, along with what's known as "massive institutional support" — i.e., a lot of money. There's a lot of money in biology, and Schadt, like a lot of other brilliant minds for hire, spends a lot of his time chasing it, making his pitch to the venture capitalists in and around the Bay Area or else going up to Seattle and making his pitch to the Gates Foundation and to Microsoft cofounder Paul Allen. "I think I must amuse Paul," Schadt says. "He keeps on inviting me up there, and he never gives me any money."
He does, however, have a regular job, with somewhat regular hours, and that's his job as chief scientific officer of a seven-year-old biotech company called Pacific Biosciences. It's a pretty interesting job, because basically Pacific Biosciences hired him the way a big-time Nascar team hires a driver — that is, because it has this miraculous machine, and it wants someone to drive it really, really fast. Schadt had just left Merck and could have gone almost anywhere he wanted — Yale wanted him to start a systems-biology department, and Genentech wanted him as its head of genetic research, a story that harkens once again to the constants of smarts and salesmanship: "When I was interviewing for that job, the head of the company's research department said, 'You're either completely full of shit or the smartest person on earth. We're not smart enough to know. But we're willing to bet that you're the smartest person on earth.' "
He wound up going to PacBio instead, even though it was essentially a start-up whose fortunes were and are tied to a machine called the RS, which stands for "real-time sequencer" but which is really an homage to "Rally Sport" and a nod to the fact that the people who run the company are really into California car culture. Schadt had heard of the RS when he was at Merck — he had heard that a scientist at Cornell named Stephen Turner had created a technology that could look at individual molecules of DNA in real time and was trying to take it into production. Schadt never thought he'd pull it off but realized that if he did ... well, the technology would be to some enterprising biologist what the telescope was to Galileo — a chance to corroborate what was a mathematical inference, a chance to see "changes in DNA causing changes in cellular networks causing changes in tissue networks going up to the whole organism." And then, in 2008, PacBio gave him a call. Turner's technology was now the RS, a $260 million triumph of engineering, design, and the kind of precautionary prophylaxis that's usually implemented around Level 4 biohazards. Would Schadt like to take it for a spin? Oh, hell yes — it was a job offer that satisfied the gearhead in him, the daredevil, the biologist who thinks like an astronomer, and, not incidentally, the salesman. Indeed, in his job as PacBio's top scientist, Schadt is a cross between Galileo, a paid thinker at someplace like the Santa Fe Institute, and a guy hawking ultra-high-end copiers. Yes, he's already glimpsed some things with the PacBio RS — he's looking to prove that instead of four bases making up the DNA molecule, there are actually so many modifications of the four that the real number could be more than twenty. (It's a perfect Eric Schadt breakthrough, because not only would it be a "game changer," it would also complicate the practice of biology beyond human capability.) But Schadt's also on the road a lot and on the phone a lot, because PacBio hired him not only to figure out the best experimental applications for the RS but also to "work your collaborations like you've been doing" — because it was his collaborative nature, his connectivity, that was at the heart of his attempt to remake the scientific culture at Merck. And that's where he's happiest. He's not the solitary scientist heroically thinking big thoughts. What he does at PacBio is his "vision of the perfect life" precisely because he's hardly ever alone, precisely because after "thinking of the things I want to think about," he gets to "travel around and talk them over with the most interesting people in the world."
He's all about the network, you see. He's helped identify it as the fundamental organizing principle of biological systems, and he sees very little difference between biological networks and social ones. When you look at biological networks comprised of thousands of genes, you'll see that they are just like social ones, with a few "highly connected" genes showing up again and again as "hub nodes" and others acting as spokes and outliers. Well, Schadt's ambition is to be a hub node. And PacBio allows him to realize his ambition because now not only is he Eric Fricking Schadt, but he's also got the machine that nobody else in the world has — the telescope, the souped-up gene sequencer, the RS. People call him out of the blue. He picks up the phone. And if their interest sounds interesting enough, he says yes, even when — hell, especially when — he already has too many projects to handle. Bring it on, baby. So now he's got collaborations going on with people at Harvard, Stanford, Columbia, Northwestern, UCLA, UC San Diego, UC San Francisco, the University of Washington, the University of Colorado, and the University of God Knows Where Else. He's got collaborations going that are intended to target the relevant biological networks behind cancer, heart disease, aging, diabetes, and sleeping problems. He's even collaborating with a scientist who's trying to extract energy from bacteria. And back in November, he got a call from a team at Harvard that was trying to figure out the strain of cholera that was ripping the guts out of Haiti almost a year after the earthquake. They'd heard about him. Could he help? Moreover, could the PacBio RS help? So here's what happened: They sent him the cholera strain from the cell culture, and he ran it through the RS. And four weeks after he received the samples, The New England Journal of Medicine published his paper with the results. A month, man. That's instantaneous in the world of biology. That's unprecedented. Turns out the strain of cholera originated in South Asia, and that information now makes a mass-vaccination plan feasible.
But this is also part of Schadt's vision — part of his pitch, part of what he's selling. He's worked at two big drug companies, Roche and Merck, and he knows what they're good at and knows what they're bad at. What they're good at: making drugs. What they're bad at: sharing. Unfortunately, what they're bad at sharing is the information that would help them make better drugs. But Eric Schadt is the strange rebel who happens to play well with others. He's the strange outlier who wants nothing more than to be a hub node. If anything, he overshares, and so what he wants to do is convince biologists to share for those who can't. In all his collaborations, he says, "we don't have as clear a path in getting drugs developed as in a pharmaceutical setting," so what he and his collaborators are doing is "publishing papers so that anyone can pursue them for whatever reason" — i.e., so that drug companies can use the ideas in them to make better drugs. And this is the idea that really gets Schadt going. Eric Schadt's the biggest thinker in biology, but meeting him sometimes feels like meeting Einstein and finding out that what he really liked about physics was the parties, like meeting Niels Bohr and having to look at his autograph collection. But that's why this is Schadt's moment: because he is out to erase the distinctions between intellectual force, technological force, and social force. Because as the Age of Information inexorably morphs into the Age of Information Overload, he's figured out that social force is the key to science's survival. And because when you ask him his grandest aim, his most cherished ambition, what he really wants to be, he answers, without hesitation, is a "master of information."
He never mentions the word science at all.
He wasn't supposed to be a scientist, anyway. Literally. It was, like, forbidden. It was ungodly. He grew up in Stevensville, Michigan, a town of a thousand people one mile square. His family was hardcore evangelical. His stepfather was a hard man, a believer, and a beautician, in roughly that order. Was Eric Schadt a believer? "Of course I was. I had no choice." Education was suspect — "I had no education to speak of." And so although he went to high school, what he calls the "greatest compliment I ever received" came when a teacher pronounced him "untamable," and as soon as he graduated, he was gone. He joined the Air Force, only to realize that instead of escaping the social and intellectual poverty of his background, he had planted himself "on the lowest rung of an organization that people in society already regarded as the lowest rung." His answer: step one, "I became profoundly depressed." Step two: He did the hardest thing he could think of doing, which was joining Special Operations. Parachute rescue. But he blew out his shoulder rappelling down a cliff, so he washed out. The Air Force looked to salvage its investment by giving him a battery of aptitude tests. When the results came back, he was asked if math had come easily to him in high school.
"Yeah, I guess so," he said. "Well, look at your scores," the Air Force said, and sent him to Cal Poly on a military scholarship.
He studied computer science and applied math at Cal Poly, and it was like taking a drug. Education itself blew a mind hungry for expansion. It wasn't just math. It was ... enlightenment. He came home, started talking about logic and philosophy, started posing "thought experiments" to his brothers and sisters. His stepfather kicked him out of the house. What he said, in Eric's recollection: "You are of the devil. Leave and never come back." Eric's answer, of course, was to do the hardest thing he could think of doing, even after his mother called a year and a half later and invited him back into the fold. He went to UC Davis to get a doctorate in pure math. Pure math: a purely conceptual exercise that takes place in purely abstract space. That's why they call it pure. But he kept learning real-world things from it. The first was how to sell something intellectually ambitious, even impossible: "People don't understand, but if you can make them think you understand, your story wins." The second was what he wanted to do with his life. He was still a Christian in orientation, if not in practice, and pure math, after a while, started feeling, well, "a little empty," even ungodly. It wasn't going to help anyone. So he passed his Ph.D. candidacy tests but never wrote his dissertation and instead enrolled in UCLA's biomathematics Ph.D. program. He had the math for it, certainly, but since he hadn't taken a biology course since high school, he had to learn Ph.D.-level biology "from scratch." To catch up, he began reading through the basic textbooks in genetics and molecular biology on his own. Sounds hard. It wasn't. Pure math was hard. Biology? "It was so easy, it was like a vacation. After pure math it was so refreshing and conceptually simple that my mind just locked onto it."
Indeed, biology was so simple that he began to suspect that what was in the textbooks was simplified, even simplistic. He began to suspect there was something wrong with it, molecular biology in particular. As a former creationist, he immediately saw the insufficiency of a biology of broken pieces, and as a man of broken faith he wondered whether he could put it back together again.
There's a famous book, written by Thomas S. Kuhn and published in 1962, called The Structure of Scientific Revolutions. Well, maybe it's not so famous — but it's hugely influential. It introduced the term "paradigm shift" into the language. A promiscuous term, as it turns out, used to describe everything from the emergence of smartphones to the omnipresence of the spread offense in college football. But in Kuhn's book it describes something specific to science. According to Kuhn, scientific progress is not a peaceful process, characterized by the gradual accumulation of knowledge. Rather, it's a nearly political one, characterized by acts of intellectual violence. A paradigm is like a king — it's the body of knowledge and practice that coheres around a theory or a discovery, and in periods of stability everybody serves it by practicing what Kuhn calls "normal science." Eventually, though, it becomes insufficient to its own ends and enters a period of crisis, during which it comes under attack by those practicing "extraordinary science." At last, the king is overthrown, and that's a paradigm shift.
Has Schadt read Kuhn's book? "I remember the exact month, almost the exact day I started reading that. It was when I first started graduate school in 1993." And of course, he knew what kind of science he wanted to practice even before he knew what king he wanted to kill. A paradigm shift requires not only scientists practicing extraordinary science; it requires "attackers" and "persuaders" willing to declaim the end of the old order and announce the dawn of the new. Schadt has turned out to be both. He's very aware that biology is in the middle of a paradigm shift and very aware of his role in both the murder of molecular biology — the king is dead! — and the establishment of its successor. He's even produced a documentary film entitled The New Biology, which heralds the arrival of a biology that's "more like physics" and "more quantitative in nature" than biology has ever been. Not incidentally, it's also a whole lot harder.
He was doing New Biology even before he got his Ph.D. from UCLA in 1999. Big Pharma, in the form of Roche, had recruited him. He was thirty-three years old, just another brilliant nobody, but he started improving the algorithms on the "gene chips" Roche used for gene detection and sharing them publicly. That gave him a name; it also got him investigated by the U. S. Attorney's office under suspicion of stealing trade secrets, because nobody could believe that he was picking the lock on proprietary algorithms without resorting to illegal means. He was cleared when investigators found out that, well, as a matter of fact, he could. Still, it was the start of his career, and he'd already seen "the amount of energy devoted to keeping you from breaking out of accepted paradigms. It was an extraordinary amount of energy. But the cool thing about the human spirit is its ability to push and persist if it thinks it's on the right path. And the path I was on was the right path from which to change biology."
Yes, that's right: The guy who wants to change biology now wanted to change biology even then, and eventually his ambition brought him to the attention of Stephen Friend and Leland Hartwell of Rosetta Inpharmatics. They were molecular biologists. To be more precise, they had made history as molecular biologists, Friend becoming the first scientist to clone a human gene associated with inherited tumors and Hartwell winning the damned Nobel Prize. That's all. But they'd thought deeply about molecular biology and had started Rosetta in part to address its inherent limitations, in particular its failure to deliver drugs to the marketplace. They were looking for the future. And so when Stephen Friend met Eric Schadt, he saw a scientist with "almost Mozart-like qualities — insights that are not always logical, but they're correct. You talked to Eric and you said to yourself, Oh, my God, I can see what's going to come."
Schadt went to Rosetta, and then, when Merck bought Rosetta, he went with Friend and a team of fifty nascent New Biologists to Merck. At the time, Merck was a molecular-biology company. It was using the basic techniques of molecular biology to figure what proteins to target and what drugs to develop. The main technique is called a "knockout study." A scientist interested in the function of a specific gene "knocks out" the gene in mice bred for the purpose, to see what happens. Schadt and Friend thought the strategy was hopeless. Not only are there twenty-three thousand genes to be knocked out one gene at a time, there are also catastrophic side effects when the drug you develop to hit a single protein encoded by a single gene instead hits a network of genes and proteins all working together in mysterious and invisible concert.
The idea of networks was not original to Schadt. What was original to Schadt, however, was a method for finding them and proving their existence. How could he find something that was not only invisible but indescribably vast, involving thousands of genes and thousands of proteins? Well, the sky is vast, and astronomers can't see the planets orbiting distant stars, either. But they prove their existence by measuring the changes in starlight and subjecting the data to statistical analysis. They never see the new planets — the whole new solar systems — they're exploring. They just know they're out there, in the numberless flickerings of the stars.
And that's how Schadt proved the existence of biological networks. He developed algorithms to mine Merck's massive troves of biological data, and he began finding genetic networks through statistical correlations. Were the networks merely theoretical? To the contrary: They were "highly predictive" experimentally — that is, they could predict the success or failure of therapeutic interventions. And so in 2003, he started publishing the papers that, in the words of a Merck spokesman, "changed the way people looked at disease," and at the same time became the foundations of the New Biology. What's more, he and his team began using the networks they were finding to figure out which genes Merck should target, until they were responsible for "half the drugs" in Merck's pipeline. What's more, long before GlaxoSmithKline ran into problems with Avandia, Schadt predicted that a similar drug Merck was developing would fail for the same reasons — because it would lower the risk of diabetes but increase the risk for cardiovascular problems — and therefore proved that the New Biology could save pharmaceutical companies billions of dollars. And then, of course, he and Friend tried to turn Merck into a New Biology company, by which they meant a company that would share its data with networks of outside scientists and that would develop drugs that targeted networks instead of single genes. The problem with that: Merck was still an old biology company. The drugs in its pipeline — including the drugs informed by Schadt's networks — targeted single genes. And so when Schadt and Friend made their presentation, this was Merck's response: "We're not an information company." And when, in 2009, Schadt published a paper in Nature entitled "A Network View of Disease and Compound Screening" — a paper that implied that drugs targeting single genes were doomed to failure — "well, that was the paper that got me kicked out of Merck."
He likes to do his supercomputing on planes now, because that's the one place where he's alone. He had access to a supercomputer at Merck, but he and Friend left Merck in 2009 after negotiating an agreement to take the New Biology component with them — including the millions of dollars' worth of data necessary to continue their work — and turn it into Sage Bionetworks. He still needs the capacity of a supercomputer, however, because the amount of data generated by the networks he's exploring is inordinate, overwhelming. There's terabytes of data, petabytes of data. Fortunately, he has the same access to supercomputers that every other American with an Internet connection and a credit card has. He waits till the plane climbs to a cruising altitude, waits for the pilot to allow electronic devices, and then uses the plane's WiFi to get on Amazon. Amazon sells a lot of stuff — books, washing machines, whatever the hell you want. What it sells Schadt is super-computing on the cheap. You see, companies like Amazon have a lot of computing power available, and now it's gotten in the business of selling some of that to guys like Schadt and whoever else might want it. A guy like Schadt doesn't have to work for a company like Merck anymore, because he has as much computing power available to him on an airplane as a scientist at Merck does on the company's multimillion-dollar supercomputer. More even. On cross-country flights he tells Amazon what data to crunch after takeoff, and for a few hundred bucks the job's done by the time he lands.
He likes to talk about this kind of stuff, because it's one of the ways he makes his sale. A lot of people are afraid of the Age of Information. They think things are getting too big and too complicated, and going too fast. You think scientists are immune? You think biologists are immune? No, they're especially anxious, because biology has turned out to be even more complex than they thought, indeed precisely as complex as the world in general. And so what Schadt has done is not only give biologists the tools to deal with the problem of increasing complexity; he's also sold complexity and has gotten biologists to relax and embrace it, in the words of Stephen Friend. "And it's a good thing, because the complexity is just going to get worse. But Eric gets you to understand that it's out of complexity that a pattern derives. That complexity is not the enemy but the vehicle of understanding, and embracing it is how you get there. You talk to him and he makes you think, Oh, this might turn out all right after all."
Schadt has sold the New Biology by making biologists feel that if they change biology, they can change the world. But he also makes it clear that as the world changes, it will change biology, whether biologists like it or not — whether we like it or not. For instance, he has this idea for what he calls a "disease weather map" that will inform people what kind of pathogens are on the handrails of the escalators, say, at the San Francisco Airport, or for that matter in the bathrooms. The idea would have been laughable just a few years ago, but Schadt is not only thinking about it — he's doing it, with the PacBio RS. He's sending out technicians, getting samples, and sequencing them more or less instantly. This is an extension of Schadt's vision to expand the network model of human disease into tracking the forces of infection in the population at large; the network is not just genes, it's also germs. He's able to do the same with sewage outflows, which has led him to a vision of monitoring the pathogens that pour out of individual households — a vision of helpful technicians knowing what's coming out of your toilets, and calling you if they think you need to eat more yogurt.
Does anybody want a world of pathogen surveillance and transparent effluvia? Well, DARPA does, Schadt says — they're very interested. And he's not overly concerned about everybody else. He's a revolutionary, and what he knows about revolutions — scientific and otherwise — is that "it's best to be one of the drivers of the revolution, and then it will work itself out." What he knows about revolutions is that "there's always this outcry, but the revolution marches on. And I would rather be part of the revolution than on the outside figuring out what it all means."
And that's what Eric Schadt's really all about — why he wants to be a "master of information" instead of simply a scientist. The New Biology is the New World, and he wants to be part of both. He wants to be one of the people who help other people figure out that information overload is not the enemy, if you know how to read it (and have supercomputer access). He wants to be part of what he calls "a revolution in human intelligence." He wants to make the sale, even if what he's selling is what so many fear. The world is getting too big? Make it bigger. The world is getting too fast? Make it faster. The world is getting complex to the point of impossibility? Bring it on, baby.
Bring it on.
Read more: http://www.esquire.com/features/eric-schadt-profile-0411-3#ixzz1HpNA1mta
[One left - but all others stayed. Genes/Junk failed Genomics, by now Mattick, Simons, Pellionisz, Collins, Lander and Schadt are in unisone. See my 2008 Google Tech Talk YouTube currently with 10,138 views - some mediocre minds dismissing anybody with a paradigm-shift as a "kook". (Giordano Bruno was torched, and his ashes thrown into the Tiberis - with the Vatican correcting the course some 300 years later. In modern times, Barbara McClintock had to reach the age 83 to get her Nobel, decades after her discovery). This entry can be commented on the FaceBook page of Andras Pellionisz]
Global Scaling Institute of Germany Explores Roots of Fractals with Euler
Leonhard Euler was one of the greatest mathematicians of all times. He developed the basics of the modern theory of numbers and algebra, the topology, the probability calculus and combinatorics, the integral calculus, the theory of the diffenrential equation and the differential geometry, the variational calculus and he discovered the coherence between trigonometrical functions and exponential functions. Leonard Euler developed the hydrodynamics and fluidic, he made the bases for the theory of the gyroscope. He was a brilliant natural scientist, an excellent teacher and mentor.It was on April 24th, 1727 when on invitation of the Russian czarina Katharina I the 19-year-old master Leonhard Euler left his home town of Basel and set off for a brillant scientific career at the Academy of the Sciences of Russia. The brothers Bernoulli (Nikolai and Daniel), Christian Goldbach and other excellent European scientist already worked there.Peter I engaged the philosopher and mathematician Christian Wolf from Marburg shortly before his death to unite the best heads of Europe under the seal of Academy of the Sciences of St. Petersburg.
In May 1771 an enormous blaze raged through St. Petersburg. Hundreds of buildings burned down, among others the house of the graduate Leonard Euler. But the basler craftsman Peter Grimm succeeded in saving the 64-year-old blind mathematician from death by burning. Thanks to his courageous intervention almost all manuscripts of the greatest mathematician of all times remained for the posterity. Among others also the work “About continued fractions” (1737) and “About the vibrations of a string” (1748). In these papers Euler formulated theses whose solution would keep mathematics busy for 200 years to come. Eulers work made it possible 250 years later to air one of the most fundamental secrets of nature – the free vibrations of the universe.
Euler examined already free vibrations of an elastic thread with no mass occupied with pearls. In connection with this task d’Alembert developed his integration method for a system of linear differential equations. Starting out from there Daniel Bernoulli put forward his famous theorem that the solution for the problem of a free vibrating string can be portrayed as a trigonometrical series, which lead to a discussion between Euler, d’Alembert and D. Bernoulli which spread over few decades. Later on Langarne pointed out more correctly, how one can come to a solution of the problem of swinging string of beads to the solution of the problem of the vibrating of a homogeneous string by breaching the limit. Only in 1822 J. B. Fourier in solved this formulation completely for the first time.
Meanwhile, nearly insurmoutable problems still arose with pearls of various mass and irregular distribution. This task leads to functions with gaps. According to a letter of Charles Hermite of May 20th, 1893, which called to “reject the lamentable plague of the functions without derivations in fright and fear “, T. Stieltjes examined functions with discontinuities and found an integration method of such functions, which led to continued fractions.
Meanwhile, Euler already recognized that complicated vibrating systems can contain also such solutions (integrals) which aren't differentiating everywhere and left to the mathematically talented posterity an analytic monster – the so called non-analytic functions (this term was chosen by himself). Non-analytic functions have ensured a lot of work up until the 20th century, even after the identity crisis of the mathematics seemed to be already overcome.
The crisis started, as E. du Bois Reymond 1875 reported for the first time about a steady, but not subtly differentiable function designed by Weierstrass, and lasted approximately till 1925. Their dominant players were Cantor, Peano, Lebesgue and Hausdorff. As result a new branch of the mathematics was given a birth – the fractal geometry.
Fractal comes from the Latin fractus and means as much as “in pieces broken” and “irregular”. [We already know since 2002 that Gene/Junk is scientifically incorrect; thus FractoGene, with its self-similar repetitions - AJP]. So fractal are really incomplete, spiteful mathematical objects. The mathematics of the 19th century took these objects for exceptions and therefore looked at regular, steady and smooth structures or tried to put down fractal phenomenons to such structures.
The theory of the fractal quantities made it possible to examine strictly “not analytic” creased, granulous or incomplete forms qualitatively. Soon it turnes out that fractal structures aren't that rare at all. In nature one discovered more fractal objects than suspected till now. More, it seemed so as if suddenly the universe was fractal by nature.
Especially the works of Mandelbrot placed the geometry finally in a position capable of describing correctly fractal mathematical objects: incomplete crystal lattices, the Brown’s movement of the gas molecules, sinuous polymer giant molecules, irregular star clusters, Cirrus clouds, the saturn rings, the distribution of the lunar craters, turbulences within liquids, bizarre shorelines, winding river courses, folded mountain ranges, branched forms of growth of most different plant sorts, areas of islands and seas, rock formations, geological depositions, the spatial distribution of raw material occurrences and so on and so on.
The Leningrad mathematicians F. R. Gantmacher and M. G. Krein looked 1950 at the deflection line of a vibrating string with pearls as partitioned line. Just this attempt made it possible for them to view the problem in a fractal way without being conscious of it (Mandelbrot’s classic “Le Objets Fractals” appeared 1975, his first works from 50’s fell into the linguistics school). Only the fractal view put them to the position to completely solve (also for the most general case) the 200 years old Euler’s problem of the vibrating string of beads for pearls of various masses and irregular distribution. In their work “Oscillation-Matrixes, Oscillation-Cores and Small Vibrations of Mechanical Systems” they proved, that all free vibrations form a finite string of beads or string a finite or infinite Stieltjes-continued fraction. The masses of the pearls and the separations between them are identical with the part denominators of the continued fraction.
1955 V. P. Terskich generalized the (as regards content fractal) continued fraction method on vibrations of complicated branched chain systems. The classic work of T. N. Thiele, A. A. Markov, A. J. Chintchin, O. Perron, J. A. Murphy, M. R. O'Donohoe, A. N. Chovansky, H. S. Wall, D. I. Bodnar, C. I. Kucminskaja, V. J. Skorobogat'ko and others helped to get the definite breakthrough for the continued fraction method and made the development of efficient algorithms poss
