Colloquium – Ilya Nemenman
Statistical Physics and Biological Simplicity
Modeling in biology has firmly established itself in cases where it’s been possible to reduce a system to a relatively small number of constitutive parts (think of population dynamics models with a few species, or biophysical neural models with a handful molecular species involved). In contrast, modern experiments often characterize “high-dimensional biology”, measuring activity of thousands of components, such as activities of hundreds of neurons, or frequency of hundreds of pathogenic genomes. Building detailed models that account for this biological complexity has proven to be difficult (and maybe not useful), and we lack intuition about how to interpret results of such experiments. I will argue that many recent experiments, in domains as different as ecology and neuroscience, hint that high-dimensional biological systems are much simpler than they could have been. I will show that simple models based on random interaction networks can explain these seemingly surprising results, and I will argue that success of these models signals emergence of simpler, collective descriptions of complex biological systems. The goal now is to develop systematic approaches to detect such collective degrees of freedom and to model their interactions.
Host: BingKan Xue