Physics Colloquium with Ilya Nemenman on Emergent Dynamics in High Throughput Biological Data
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 measure activity of thousands of components, such as activities of hundreds of neurons, or frequency of hundreds of pathogenic genomes. Building computational models at this level of detail has proven difficult (and maybe not useful), and we lack intuition about how to interpret results of our experiments. Put another way: When should we be surprised by what we see in high throughput recordings? I will show that simple models can explain seemingly surprising results of high throughput experiments in fields as diverse as neuroscience and immunology (my primary focus here will be on neuroscience), and I will argue that success of these models signals emergence of simpler, collective descriptions of complex biological systems. The goal now is to identify those collective degrees of freedom, and how they interact with each other. Overall, this progress raises hopes for building predictive models of the nervous system without neurons, tissues without cells, and evolution without individuals or genes.