Title: Cell-cell adhesion models: mean-field limits and parameter estimation 

Abstract: This work presents a mathematical and computational framework for estimating parameters in cell–cell adhesion models. The study builds upon a macroscopic, nonlocal PDE model involving two interacting cell populations influenced by attractive and repulsive forces, incorporating both adhesion dynamics and volume exclusion effects. Starting from a stochastic particle system, the nonlinear PDE system for the cell densities is formally derived through mean-field limits.  The parameter estimation problem is addressed by  minimising an error functional that combines macroscopic densities and individual trajectories. The model parameters are estimated using a Bayesian approach, which involves sampling from the posterior distribution of the parameters given observed data. To efficiently sample from this distribution, the pre-conditioned Crank–Nicolson (pCN) Markov Chain Monte Carlo method is used. This algorithm is derivative-free, well-suited for high-dimensional spaces, and has a single tunable parameter to optimize performance. 

About the speaker: https://sites.google.com/view/gissell-estrada-rodriguez