Abstract

Understanding how fluctuations propagate across spatial scales is central to our understanding of inanimate matter, from turbulence to critical phenomena. In contrast to physical systems, biological systems are organized into a hierarchy of processes on a discrete set of spatial scales: they are compartmentalized. In this talk, I will first show that dynamic compartmentalization of stochastic systems leads to emergent, quasi-particle-like kinetics, which cells use to perform key biological functions. Specifically, we derive a general theory that predicts the emergence of a single degree of freedom irrespective of system specifics. We obtain equations of motion and response characterising its unique kinetic properties. We experimentally demonstrate the biological relevance of quasi-particle kinetics in the decision of cells to commit suicide (apoptosis). In the second part of my talk, I will discuss how ideas from theoretical biophysics can contribute to our understanding of emergent phenomena in deep learning.