Individual-based models for bacterial chemotaxis in the diffusion asymptotics

TL;DR

This paper analyzes velocity-jump models for bacterial chemotaxis with internal states, proving their convergence to an advection-diffusion process and proposing a discretization method that preserves this limit.

Contribution

It introduces a probabilistic framework for internal state models, proves their diffusion limit, and develops a discretization technique that maintains this limit exactly.

Findings

Models converge to advection-diffusion process in long-time limit

Discretization method preserves diffusion limit without error

Results enable variance reduction in simulations

Abstract

We discuss velocity-jump models for chemotaxis of bacteria with an internal state that allows the velocity jump rate to depend on the memory of the chemoattractant concentration along their path of motion. Using probabilistic techniques, we provide a pathwise result that shows that the considered process converges to an advection-diffusion process in the (long-time) diffusion limit. We also (re-)prove using the same approach that the same limiting equation arises for a related, simpler process with direct sensing of the chemoattractant gradient. Additionally, we propose a time discretization technique that retains these diffusion limits exactly, i.e., without error that depends on the time discretization. In the companion paper \cite{variance}, these results are used to construct a coupling technique that allows numerical simulation of the process with internal state with asymptotic variance reduction, in the sense that the variance vanishes in the diffusion limit.