B10 - Bayesian deep learning to study non-Gaussianity, correlations, and change-points in cell-driven transport

Colloidal particles transported by a “living carpet” of amoeboid cells exhibit strongly non-Gaussian displacement distributions, appearing to converge towards an exponential (Laplace) distribution in the long-time limit. Concurrently, the mean-squared displacement is superdiffusive at shorter times and normal-diffusive at longer times. 

In this project, we will garner new data covering different cell densities and monitoring the dynamics of cell-cargo contacts. We will develop a theoretical description based on stochastic fractional Laplace motion, aided by Bayesian Deep Learning strategies and extend it to analyse change-points in the particle motion.