Pierre Ronceray
CINaM, Turing Centre for Living Systems, CNRS, Université Aix-Marseille
Learning the stochastic dynamics of biological matter
The dynamics of biological systems, from proteins to cells to organisms to ecosystems, is complex and stochastic, and thus often modeled by stochastic differential equations (SDEs). There is currently a strong interest in learning such SDEs from experimental trajectories. This task faces many challenges, such as data imperfection (noisy, sparse time series), partial observations and model interpretability, and currently SDE inference remains a bottleneck to data-driven biophysics. In this talk, I will present a set of tools developed to bridge this gap and permit robust learning of stochastic dynamical models. These methods are rooted in an information-theoretical framework that quantifies how much can be inferred from trajectories that are short, partial and noisy. They permit the efficient inference of dynamical models for overdamped and underdamped Langevin systems, as well as the inference of entropy production rates. I finally present early applications of these techniques in the context of cell migration of patterned substrates and microbial ecology.
