Probabilistic Inference of the Steady-State Distribution of an Age-Size Structured Population

Explore a 37-minute lecture on the probabilistic inference of steady-state distributions in age-size structured populations using single-cell data. Delve into a stochastic individual-based dynamic model for E. coli cells, calibrated with temporal single-cell lineage data from microfluidic techniques. Examine how age structure provides a non-Markovian characterization of growing populations. Learn about the exponential convergence of the stochastic process towards a unique stationary distribution and the criteria for convergence. Compare predicted distributions with empirical data from macroscopic observations to validate micro-to-macro links in healthy and perturbed bacterial populations under various growth conditions. Presented by Ignacio Madrid Canales from Ecole Polytechnique at the Institut Henri Poincaré in Paris, this talk offers insights into advanced probabilistic techniques for studying bacterial population dynamics.