Statistical Physics of Biological Simplicity

Abstract: Theory in biological physics has firmly established itself in cases where it’s been possible to reduce a system to a relatively small number of constitutive parts (population dynamics with a few species, or small networks of neurons, or small molecular circuits). In contrast, modern experiments often characterize “high-dimensional biology”, measuring activity of hundreds of neurons, frequencies of hundreds of pathogenic genomes, and so on. 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 experiments, in domains as different as genomics and neuroscience, hint that high-dimensional biological systems may exhibit unexpected statistical regularities. I will show that simple random-matrix based interaction models explain these seemingly surprising results. Success of these models signals emergence of simpler, collective dynamics in complex biological systems. The goal now is to develop systematic approaches to detect such collective degrees of freedom and to model their interactions.