For various reasons, biologists who are willing to accept a living model as a source of insight are unwilling to apply the same criterion to a computational model. Some of the reasons for this are legitimate, others are not. For example, some fields of biology are so swamped with information that new sources of ideas are unwelcome. A Nobel Prize winning molecular biologist said to me recently, "There may be some good ideas there, but we don't really need any more good ideas right now.
A second reason for biologists' general lack of interest in computational models is that they are often expressed in mathematical terms. Because most mathematics is not very useful in biology, biologists have little reason to learn much beyond statistics and calculus. The result is that the time investment required for many biologists to understand what is going on in computational models is not worth the payoff.
A third reason why biologists prefer living models is that all known life is related by common ancestry. Two living organisms may have many things in common that are beyond our ability to observe. Computational models are only similar by construction; life is similar by descent. All three of these reasons are legitimate. But there are also emotional reasons that are less so.
Biologists are biologists because they love living things. A computation is not alive. Most biologists can more easily empathize with someone who is feeding sugar to a cell culture than someone who is feeding programs to a computer.
I once asked John Maddox, the editor of Nature, why he thought computational modeling results in biology received relatively little attention. He was sympathetic, but pointed out that most of the reviewers were experimental biologists: "You can't expect people who have spent years just getting their organism to grow to have much respect for someone who does something on a computer in a few hours. The problem predates computers. Haldane described how his insights into various evolutionary processes came from working with models that were based on exchanging different colored beans from bag to bag.
In reference to his model-based estimates of the intensity selected pressures favoring industrial melanism, he complains: "If biologists had had a little bit more respect for algebra and arithmetic, they would have accepted the existence of such intense selection thirty years before they actually did so. So with all these reasons for not using non-biological models, why fight the consensus?
The best reason is that computational models help in understanding how things might work. When a system is too complex to understand, it often helps to understand a simpler system with analogous behavior.
Just as a physicist can get some insight into electric waves by watching waves of water, a neuro- physiologist can get insight into real neurons by playing with a simple model of a neural network. The "beanbag" model used by Haldane is far simpler than actual biological genetics, yet working with the model led him to the concept of mutation load, the first accurate estimates of human mutation rates, understanding of the mechanisms of stable polymorphism, and a variety of other insights into real biological systems.
Computational models complement real experimental data in several dimensions. The measurements are precise and exactly repeatable. The costs are low and the time scales are short.
It is often possible to perform much larger scale experiments than are practical in a laboratory. For example, a simulation-based computational model of evolution may run for hundreds of thousands of generations in a few hours.
The complete "gene pool" over all the generations is available for inspection and analysis. There is no need to worry about imperfections in the fossil record. The cost of all this largeness, of course, is that the model represents a tremendous simplification over real biological evolution.
Only specific idealized aspects of real biological organisms are included in the model. The model cannot prove anything conclusive about real biological evolution, anymore than the nervous system of a nematode can prove anything about the nervous system of a mammal. Models of this type can only suggest what might be true. It is still up to the experimenter to determine what actually is. E xamples like these give us confidence that biology does have a physics to it.
And one thing we can be fairly sure about is that biology is not like that, because it would simply not work if it was.
It will surely have something to say about how gene networks produce both robustness and adaptability in the face of a changing environment—why, for example, a defective gene need not be fatal and why cells can change their character in stable, reliable ways without altering their genomes. It should reveal why evolution itself is both possible at all and creative.
Saying that physics knows no boundaries is not the same as saying that physicists can solve everything. They too have been brought up inside a discipline, and are as prone as any of us to blunder when they step outside. Physics is not what happens in the Department of Physics. Pigliucci, M. Biology vs. Physics: Two ways of doing science?
Perunov, N. Statistical physics of adaptation. Hoel, E. Quantifying causal emergence shows that macro can beat micro. Proceedings of the National Academy of Sciences , Walker, S. The informational architecture of the cell. Philosophical Transactions of the Royal Society A Retrieved from: DOI: Evolutionary transitions and top-down causation.
The algorithmic origins of life. Journal of the Royal Society Interface 10 Mora, T. Are biological systems poised at criticality? Journal of Statistical Physics , Hildalgo, J. Information-based fitness and the emergence of criticality in living systems. Bialek, W. Yes, physics and biology can sometimes collide and when they do, it can produce something entirely new. More than half a century ago, physics and biology came together in the work of Crick and Watson — working at my own place of work, the Cavendish Laboratory in Cambridge — with Franklin and Wilkins from Kings College, London.
The structure of the DNA double helix was solved by bringing the then relatively new technique of X-ray diffraction to bear on the structure of carefully grown DNA crystals.
Out of this and related work grew the whole field of molecular biology and, although physicists often migrated to work in this field, it became divorced from physics as a discipline. For many years after that mainstream physics concentrated on "simple" systems which could be fully understood and modelled. Biology tended to be seen as messy and complicated, not a fit subject for a physicist as a senior departmental colleague once made very clear to me as I made my initial forays into the field , and few individuals in physics departments braved the untidy world of biological organisms.
As a result, those whose tastes remained biological would more likely be found in some form of biology department calling themselves biophysicists, or perhaps working as medical physicists in a hospital. But times change and so do the attitudes of physicists towards messy systems. Complexity and emergent phenomena are most definitely now seen as proper domains for a physicist. Emergent phenomena cover processes where the outcome is more than the sum of the component parts, and something new emerges from collective behaviour that could not be predicted by looking at any contributing entity in isolation.
This applies to superconductivity in certain complicated inorganic compounds, but also to the synchronised beating of heart cells. Studying biological systems with a physicist's mindset has, if I can put it this way, become respectable, and as a soft matter physicist I no longer feel that colleagues frown upon my research if I mention that I am studying starch, proteins or cellular biophysics.
There was, in the past, one way that did seem acceptable for a physicist to enter the biological arena and that was by providing a service, typically imaging of some sort. Medical imaging has made great strides during the past decades — MRI, CAT scans and ultrasound being familiar to many from hospital visits, often transforming diagnostics and treatment — and much of their development work can be directly attributed to physicists and engineers.
The many new microscopies which are transforming the way we see cells function also owe a lot to physicists' understanding of lenses, noise and image analysis algorithms, coupled with the power of lasers.
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