Talk by Ivo F. Sbalzarini, TU Dresden.

Numerical solution and data-driven inference of active material models in biology

07.03.2024,  12:15-13:15 Room Y27-H12

Abstract: Living materials, like cells and tissues, are mechanically active. They are able to move and deform by themselves, generating internal mechanical stresses by consuming a chemical fuel. The theory of active matter describes the space-time dynamics of such non-equilibrium, nonlinear materials. It thus holds the key to understanding biological morphogenesis across scales, from cell motility to embryo development, and its misregulation in disease. We present a numerical method for solving active material models in three-dimensional complex-shaped geometries. Specifically, we develop geometry-adaptive meshfree particle methods and a scalable high-performance computing software framework to predict emergent behaviors of active materials across scales. We characterize novel physical phenomena, including active turbulence and spontaneous flow transitions, and hypothesize their biological function. Finally, we present a machine-learning algorithm based on high-dimensional sparse regression to infer reduced-order active material models from data. This allows identifying dominant physical mechanisms in specific biological processes.