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BP: Fachverband Biologische Physik
BP 23: Focus Session: Inference Methods and Biological Data (German-French Focus Session) (joint session BP/DY)
BP 23.2: 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 Opper2,3, 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