Agniva Datta, Jan Albrecht
First Presentation
Speaker: Agniva Datta
Topic: The mysterious motion of swimming bacteria: a bridge connecting biology and non-equilibrium physics
Abstract:
From the mesmerizing patterns formed by flocking birds to the remarkable ability of lizards to regenerate their tails, the intricate phenomena observed in living systems at different length scales beckon physicists to unravel their mysteries. These captivating puzzles often find their answers at the interface of nonlinear dynamics and non-equilibrium statistical mechanics. Not only do these solutions shed light on the underlying biology, but they also unveil previously uncharted territories within the black-box of physics far from equilibrium.
In this presentation, I will delve into a case study from my doctoral research: the motion of swimming bacteria. Our study incorporates a combination of experimental analysis utilizing the soil bacterium
Pseudomonas putida and active particle modeling. Specifically, we investigate how the disordered environment (agar) guides the migration patterns of these bacteria, resulting in remarkable motility characteristics like ergodicity breaking. In contrast to E. coli, our research reveals the transient sub-diffusion of bacteria in agar, primarily attributed to intermittent trapping. These findings underscore a dynamic run-and-trap mechanism, with trap times following a power-law distribution. I will discuss the implications of these findings and establish an intricate correlation between the micro-scale navigation of bacteria and their largescale movement in diverse and heterogeneous environments.
Second Presentation
Speaker: Jan Albrecht
Topic: Bayesian Parameter Inference for Biological Tracking Data
Abstract:
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 and the ensuing quasi-random motion, stochastic models like SDEs are well suited to describe their movements.
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, can be addressed.
