Associate Professor University of Washington, United States
Introduction:: Biological function is governed by highly integrated networks that exhibit nonlinear dynamics at all biochemical/molecular, cellular, and organismal levels. We believe that engineering principles can be employed to better understand, predict, and control complex biological functions, and that these principles need to be informed by biology. In particular, computational models are essential tools that can be used to simultaneously explain and guide biological intuition.
Materials and Methods:: My lab employs machine learning, dynamical systems, and agent-based modeling strategies to explain biological observations and uncover fundamental principles that drive both individual cellular decisions and cell population dynamics. We are interested in the inherent multiscale nature of biology, with a specific focus on system-level dynamics that emerge from interactions of simpler individual-level modules.
Results, Conclusions, and Discussions:: In this presentation, I introduce multiscale agent-based models of cell populations that are designed to interrogate multilateral regulation among heterogeneous cell agents and their local environments. The modeling framework is flexible and can be adapted to represent, analyze, and control a wide variety of biological systems, including stem cell colony dynamics, tumor microenvironments, and plant root architecture.