Understanding Genomic Networks through Statistical Physics and Nonlinear Dynamics
Many natural, technological, and social systems take the form of networks. Examples include metabolic networks, the Internet, and friendship networks. In the last decade or so, the new field of “network science” has emerged, with physicists playing a key role applying methods from statistical mechanics and nonlinear dynamics to understand the behavior of these complex networks. In this talk, I will discuss two research projects that leverage a networks perspective in order to gain insights into biological systems. The first project focuses on how network methods can help us better interpret the vast quantities of raw data that must be integrated for whole-genome sequencing. Our work utilizes insights from the observation that a key element of the genome assembly problem is similar to that of finding the ground state of a spin glass, but with a previously unconsidered connection topology for which a network perspective is applicable. The second project explores how the dynamics of genetic control in living organisms depends upon the elaborate network of interactions between genes. Our work shows how connecting these systems to well studied models in statistical physics can help elucidate the role of network structure in gene regulation.