Statistical Inference of The Mechanisms Driving Collective Cell Movement

Abstract

SummaryNumerous biological processes, many impacting on human health, rely on collective cell movement. We develop nine candidate models, based on advection–diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm. The best model was selected by using the widely applicable information criterion. We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells.