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DY: Fachverband Dynamik und Statistische Physik

DY 26: Focus Session: Inference Methods and Biological Data (German-French Focus Session) (joint session BP/DY)

DY 26.2: Talk

Wednesday, March 20, 2024, 15:30–15:45, H 2032

Bayesian Model Inference for Biological Tracking Data — •Jan Albrecht¹, Lara S. Bort¹, Carsten Beta¹, Manfred Opper^2,3, and Robert Großmann¹ — ¹Institute of Physics and Astronomy, University of Potsdam, 14476 Potsdam, Germany — ²TU Berlin, Fakultät IV-MAR 4-2, Marchstraße 23, 10587 Berlin, Germany — ³Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, B15 2TT, United Kingdom

In order to understand and predict the motion patterns of microorganisms, robust methods to infer motility models from time discrete experimental data are required. Due to the internal complexity of the organisms, their movements appear to have random components which motility models need to account for. Bayesian statistical methods provide a way to efficiently extract information from the trajectory data and provide model parameter estimates together with a measure of uncertainty. We showcase that Bayesian methods are especially well suited when the models contain additional layers of stochasticity, for example population heterogeneity or temporal dependence of parameters. Furthermore, we demonstrate how challenges that arise when multidimensional dynamics is only partially observed, e.g. second order dynamics, colored noise or non-observed internal degrees of freedom, can be addressed.

Keywords: Stochastic Modeling; Bayesian Inference; Active Matter; Cell Motility