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DY: Fachverband Dynamik und Statistische Physik
DY 14: Focus Session: Nonlinear Dynamics in Biological Systems I (joint session DY/BP)
DY 14.4: Vortrag
Dienstag, 18. März 2025, 10:30–10:45, H43
Likelihood-based inference for heterogeneous motile particle ensembles — •Jan Albrecht1, Cristina M. Torres1, Carsten Beta1, Manfred Opper2,3,4, and Robert Großmann1 — 1Institute of Physics and Astronomy, University of Potsdam, 14476 Potsdam, Germany — 2Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany — 3Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, B15 2TT, United Kingdom — 4Institute of Mathematics, University of Potsdam, 14476 Potsdam, Germany
The inherent complexity of biological agents often leads to motility behavior that appears to have random components. Robust stochastic inference methods are therefore required to understand and predict the motion patterns from time discrete trajectory data provided by experiments. In many cases second-order Langevin models are needed to adequately capture the motility. Additionally, population heterogeneity needs to be taken into account when analyzing data from multiple individual organisms. We present a maximum likelihood approach to infer stochastic models and, simultaneously, estimate the heterogeneity in a population of motile active particles from discretely sampled trajectories. To this end we propose a new method to approximate the likelihood for nonlinear second order Langevin models. We demonstrate that our approach outperforms alternative methods for heterogeneity estimation, especially for short trajectories, while also providing a measure of uncertainty for the estimates. We use the approach to investigate population heterogeneity in systems of ameboid cells.
Keywords: Stochastic Modelling; Bayesian Inference; Active Matter; Cell Motility