Information Processing in Living Organisms: What Does Bifurcation Theory Teach Us?
John Tyson (June 27, 2019)
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One of the basic characteristics of living organisms is their ability to process information about their external environment and internal state and to implement adaptive responses to the challenges they face. At the cellular level, these information processing tasks are carried out by complex networks of interacting genes and proteins; quite differently than the information processing done by digital computers or (analog) central nervous systems. Despite the triumphs of molecular biologists over the past 40 years in identifying and characterizing the components of these networks, their information-processing capabilities are still largely mysterious. Is there a basic theory of information-processing by molecular reaction networks that is biochemically realistic, reasonably accurate and comprehensive, and of predictive value? I will make the case that bifurcation theory of dynamical systems provides a framework for thinking about this problem. Briefly put, a one-parameter bifurcation diagram (dynamical variable as a function of control parameter) is the mathematical analog of the physiologistâ€™s â€œsignal-responseâ€? curve; and a two-parameter bifurcation diagram (e.g., physiological control parameter versus level of gene expression) can provide insight into the translation from genotype to phenotype. I will illustrate these principles with a number of classic examples from the field of network dynamics and cell physiology, and I will relate this particular problem to broader considerations of the â€œRules of Lifeâ€?.