Integrating Population, Metabolic, and Reinforcement Learning Models for Evolutionary and Ecological Systems

Nicholas Chia, Mayo Clinic
Cellular mitosis


Abstract:  Key questions within the biological and biomedical sciences require an understanding of complex dynamical systems. This includes physics approaches to modeling collective effects—the science of understanding how seemingly coordinated behaviors emerge from many interacting agents in the absence of a central driving force. Often given the moniker “the madness of crowds,” collective effects implies that biological systems give rise to emergent phenomena that are more than the mere sum of their parts—or at the very least, that the summing is not obviously carried out. My research program focuses on uncovering the broad mechanisms that give rise to collective effects within evolving ecosystems as well as modeling the specific biological entities and actions that can help us engineer desired ecological outcomes.  I will present examples of the broad impact these approaches have across the science continuum ranging from single-species biofilms to microbial ecology to cancer evolution.

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