Physicists in biology, inverse problems and other quirks of the genomic age

Nobel Laureate Sydney Brenner has 
criticized systems biology at the same time that a grandiose 
attempt to solve inverse problems in biology

Leo Szilard – effulgent, peripatetic Hungarian physicist, habitué of tavern lobbies, soothsayer without peer – primeval grasped the implications of a nuclear shackle reaction in 1933 while stepping done the curb at a traffic light in London. Szilard has numerous company distinctions to his name; not solitary did he file a patent notwithstanding the first nuclear reactor with Enrico Fermi, mete he was the one who urged his of advanced age friend Albert Einstein to write a famous letter to Franklin Roosevelt, and furthermore the one who tried to realize another kind of letter signed at the same time that the war was ending in 1945; a verbal expression urging the United States to demonstrate a nuclear weapon in front of the Japanese prior to irrevocably stepping across the line. Szilard was lucky in getting the first letter signed if it were not that failed in his second goal.

After the enmity ended, partly disgusted by the ruthless use to which his beloved science of energy had been put, Szilard left professional science of nature to explore new pastures – in his case, biology. But apart from the good abhorrence which led him to switch fields, in that place was a more pragmatic reason. As Szilard place it, this was an age when you took a year to reveal something new in physics but solely took a day to discover a person of consequence new in biology.

This sentiment herd many physicists into biology, and the second book of the pentateuch benefited biological science spectacularly. Compared to science of energy whose basic theoretical foundations had matured through the end of the war, biology was uncharted district. The situation in biology was like to the situation during the frolic of physics right after the inventing of quantum theory when, as Paul Dirac quipped, “likewise second-rate physicists could make at the outset-rate discoveries”. And physicists took filled advantage of this situation. Since Szilard, biology in whole and molecular biology in particularly be the subject of been greatly enriched by the presence of physicists. Today, any physics close examiner who wants to mull doing biology stands in successi~ the shoulders of illustrious forebears including Szilard, Erwin Schrodinger, Francis Crick, Walter Gilbert and greatest in number recently, Venki Ramakrishnan.

What is it that draws physicists to biology and wherefore have they been unusually successful in make contributions to it? The allure of knowledge life which attracts other kinds of scientists is certainly undivided motivating factor. Erwin Schrodinger whose weak book “What is Life?” propelled divers including Jim Watson and Francis Crick into genetics is individual example. Then there is the opportunity to simplify an enormously complex classification into its constituent parts, an ~ifice which physicists have excelled at since the time of the Greeks. Biology and especially the brain is the conclude complex system, and physicists are tempted to put their reductionist approaches to deconvolute this complication. Thirdly there is the practical favorable opportunity that physicists have; a capacity to apply experimental tools like x-ray diffraction and quantitative ratiocination including mathematical and statistical tools to answer for sense of biological data.

The a~ of the data scientists

It it this third part reason that has led to a significative influx of not just physicists on the contrary other quantitative scientists, including statisticians and computer scientists, into biology. The quick development of the fields of bioinformatics and computational biology has led to a bulky demand for scientists with the quantitative skills to decompound large amounts of data. A rigid background brings valuable skills to this essay and quantitative, data-driven scientists improve in genomics. Eric Lander during the term of instance got his PhD in mathematics at Oxford in the sight of – driven by the tantalizing goal of intellect the brain – he switched to biology. Cancer geneticist Bert Vogelstein too has a background in mathematics. All of us are free with names like Craig Venter, Francis Collins and James Watson whenever it comes to appreciating the cracking of the human genome, moreover we need to pay equal heed to the computer scientists without whom crunching and combining the very great amounts of data arising from sequencing would obtain been impossible. There is no waver that, after the essentially chemically driven whirling in genetics of the 70s, the take part with revolution in the field has been engineered ~ means of data crunching.

So what does the that will be hold? The rise of the “data scientists” has led to the burgeoning tract of land of systems biology, a buzzword which seems to proliferate more than its actual understanding. Systems biology seeks to integrate dissimilar kinds of biological data into a extended picture using tools like graph scheme and network analysis. It promises to potentially cater us with a big-picture eye of biology like no other. Perhaps, physicists look upon, we will have a theoretical frame for biology that does what quantum science did for, say, chemistry.

Emergence and systems biology: A agreeable pairing

And yet even as we gust the fruits of these higher-take aim approaches to biology, we must subsist keenly aware of their pitfalls. One of the radical truths about the physicists’ view of biology is that it is steeped in reductionism. Reductionism is the burdensome legacy of modern science which saw its culmination in the two twentieth-centenary scientific revolutions of quantum mechanics and corpuscular biology. It is hard to overcharge the practical ramifications of reductionism. And still as we tackle the salient problems in twenty-first century biology, we are become aware of the limits of reductionism. The chivalrous antidote to reductionism is emergence, a property that renders mingled systems irreducible to the sum of their districts. In 1972 the Nobel Prize pleasing physicist Philip Anderson penned a remarkably far-reaching article named “More is Different” what one. explored the inability of “diminish-level” phenomena to predict their “higher-level” manifestations.

The brain is every outstanding example of emergent phenomena. Many scientists ween that neuroscience is going to have existence to the twenty-first century which molecular biology was to the twentieth. For the in the beginning time in history, partly through recombinant DNA technology and in some degree due to state-of-the-sagacity imaging techniques like functional MRI, we are poised on the brink of making major discoveries hind part before the brain; no wonder that Francis Crick moved into neuroscience for the period of his later years. But the brain presents a remarkably different kind of challenge than that posed by, say, a superconductor or a crystal of DNA. The brain is a very much hierarchical and modular structure, with multiple unable to exist without and yet distinct layers of construction. From the basic level of the neuron we excite onto collections of neurons and glial cells which behave very differently, onward to specialized centers according to speech, memory and other tasks in successi~ to the whole brain. As we awaken up this ladder of complexity, emergent features arise at each level whose behavior cannot be gleaned purely from the behavior of individual neurons.

The absolutism of inverse problems

The problem thwarts systems biology in inaccurate. In recent years, some of the chiefly insightful criticism of systems biology has get to from Sydney Brenner, a founding fore~ of molecular biology whose 2010 work in Philosophical Transactions of the Royal Society titled “Sequences and Consequences” should have ~ing required reading for those who deem that systems biology’s triumph is blameless around the corner. In his trial, Brenner strikes at what he sees for the re~on that the heart of the goal of systems biology. After reminding us that the systems approach seeks to generate viable models of benefice systems, Brenner goes on to affirmation that:

“Even though the proponents look to be unconscious of it, the claim of systems biology is that it be able to solve the inverse problem of science of the functions of animals and vegetables by deriving models of how systems toil from observations of their behavior. It is known that indirect problems can only be solved when exposed to very specific conditions. A good prototype of an inverse problem is the deducing of the structure of a molecule from the X-ray diffraction shape of a crystal…The universe of in posse models for any complex system like the dependent of a cell has very great dimensions and, in the absence of any theory of the system, there is ~t one guide to constrain the choice of mould.”

What Brenner is saying that each systems biology project essentially results in a pattern, a model that tries to solve the problem of divining reality from from experience data. However, a model is not reality; it is an imperfect picture of substantialness constructed from bits and pieces of data. It is therefore – and this has to have existence emphasized – only one representation of matter of fact. Other models might satisfy the same from experience constraints and for systems with thousands of moving parts like cells and brains, the calculate of models is astronomically large. In etc., data in biological measurements is repeatedly noisy with large error bars, farther complicating its use. This puts systems biology into the classic conundrum of the inverse problem that Brenner points wanting, and like other inverse problems, the re~ you find is likely to have existence one among an expanding universe of solutions, multiplied of which might be better than the one you have. This means that during the time that models derived from systems biology potency be useful – and often this is a adequate requirement for using them – they force likely leave out some important characteristic of the system.

There has been some very interesting recent work in addressing similar conundrums. One of the major challenges in the reversed problem universe is to find a minimal determine of parameters that can describe a plan. Ideally the parameters should be sentient to variation so that one constrains the parameter capacity describing the given system and avoids the “anything goes” springe. A particularly promising example is the employment of ‘sloppy models’ developed by Cornell physicist James Sethna and others in which parameter combinations rather than individual parameters are varied and those combinations that are most tightly constrained are at another time picked as the ‘right’ ones.

But quite apart from these theoretical fixes, Brenner’s redress for avoiding the fallout from incomplete systems modeling is to simply conversion to an act the techniques garnered from classical biochemistry and genetics c~ing the last century or so. In unit sense systems biology is nothing newly come; as Brenner tartly puts it, “there is a watered-down version of systems biology which does nothing more than give a starting a~ name to physiology, the study of form and the practice of which, in a modern experimental form, has been going up~ at least since the beginning of the Royal Society in the seventeenth century”. Careful inspection of mutant strains of organisms, measurement of the interactions of proteins through small molecules like hormones, neurotransmitters and drugs, and note of phenotypic changes caused by known genotypic perturbations stay tried-and-tested ways of drawing conclusions about the behavior of benefice systems on a molecular scale.

Genomics and put ~s into discovery: Tread softly

This viewpoint is likewise echoed by those who take a carping view of what they say is an overly genomics-based approach to the usage of diseases. A particularly clear-headed view comes from Gerry Higgs who in 2004 presciently wrote a fire-arm titled “Molecular Genetics: The Emperor’s Clothes of Drug Discovery”. Higgs criticizes the uninjured gamut of genomic tools used to show new therapies, from the “eminently-volume, low-quality sequence data” to the genetically engineered confined apartment lines which can give a misleading impression of molecular interactions under normal physiological stipulations. Higgs points to many successful drugs discovered in the utmost fifty years which have been establish using the tools of classical pharmacology and biochemistry; these would take in the best-selling, Nobel Prize winning drugs developed through Gertrude Elion and James Black based forward simple physiological assays. Higgs’s cape is that the genomics approach to drugs runs the peril of becoming too reductionist and contract-minded, often relying on isolated systems and feigned constructs that are uncoupled from unimpaired systems. His prescription is not to reject these tools which can undoubtedly make provision important insights, but supplement them by older and proven physiological experiments.

Does all this mean that systems biology and genomics would be useless in leading us to commencing drugs? Not at all. There is not at all doubt that genomic approaches can have ~ing remarkably useful in enabling controlled experiments. The systems biologist Leroy Hood beneficial to instance has pointed out how selective gene silencing can allow us to tease apart vergeeffects of drugs from beneficial ones. But which Higgs, Brenner and others are impressing about us is that we shouldn’t give permission to genomics to become the end-the whole of and be-all of drug making known. Genomics should only be employed being of the kind which part of a judiciously chosen cocktail of techniques including classical ones toward interrogating the function of living systems. And this applies greater degree generally to physics-based and systems biology approaches. 

Perhaps the actual problem from which we need to weanling ourselves is “physics envy”; at the same time that the physicist-turned-financial modeler Emanuel Derman reminds us, “Just like  physicists, we would like to make manifest three laws that govern ninety-nine percent of our system’s intricacies. But we are to a greater degree likely to discover ninety-nine laws that make intelligible three percent of our system”. And that’s as good a starting point as a single one .

When you wear makeup at toil, you should stick to neutral flag, when it comes to your eyes.

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