A modern molecular biologist might paraphrase the poet Pope by saying, The proper study of mankind is the bacterium. — David Koshland
Appropriately, Philippe Cluzel displayed this quote (at least the second half of it) as part of his presentation on biophysics. Before today’s colloquium I had the vague impression that biophysics was something like more mathematical molecular biology. More mathematical, at least, was correct. Cluzel’s work has quite a bit of mathematical modelling. But there are few, if any, physical concepts used, even if some of the mathematical methods are borrowed from physics. (In that sense I suppose it’s rather like econophysics.) Rather, he works on biological systems on the assumption that they are computational systems adapted to maximise evolutionary advantage. Now throw the idea of investigating the causes of evolutionary adaptation into anything and I’m immediately interested (much more interested than if you were to tell me that a problem is important because it could improve the welfare of humans).
The basic idea behind biophysics (or at least the kind of biophysics Cluzel does) is to make simplifying assumptions for biological systems, to the point that it is possible to construct simple models that might explain many general phenomena. For example, his work on neural networks involves assuming that the model of a nano-brain could have implications even for something as complex as the human brain. (Cluzel: “But I should stop using the term nano-brain or my biology colleagues will think I’m not serious — which is true, by the way.”)
A summary, largely for myself:
Homogeneous networks = networks where each node has about the same number of connections as every other network. Rarely or never found in biological systems.
Scale-free network = network where some nodes have much more connections than others. Widespread in biological systems.
By computational models, Cluzel and colleagues found that homogenous networks adapted much more slowly to environmental perturbations than did scale-free networks. They also required much more precise fine-tuning of initial input variables to attain a fitness maximum, whereas scale-free networks would converge to the maximum fitness adaptations regardless of what variables they started with.
Chemotaxis in bacteria is what Cluzel called the “hydrogen atom” of biology. A simple system that is understood in great detail, and a system with analogues in much of biology. It’s also amenable to being explained by computational models, because (at least for E. coli), the movement of bacteria is controlled by a simple binary signal that rotates the flagella either clockwise or anticlockwise. His explanation of his work on this was somewhat long and detailed, and I didn’t have time to stay till the end, so I shall hold off spouting a string of falsehoods here.
Quite simply, I’m interested, and I’m surprised that I’m interested. I like the elegance of using a model of a nano-brain to explain very general phenomena. I’m always curious to know why this or that trait is evolutionarily advantageous. If I could start again I would be tempted to volunteer to work for him.
