Pattern formation within phenotype-structured chemotactic populations

TL;DR

This paper extends classical chemotaxis models to include phenotypic heterogeneity, analyzing how trait variability and switching influence pattern formation in chemotactic populations.

Contribution

It introduces a non-local structured population model that accounts for heterogeneity in chemotactic sensitivity and attractant secretion, and analyzes its impact on pattern formation.

Findings

Phenotypic heterogeneity affects the critical conditions for pattern formation.

Switching rates between phenotypes influence pattern dynamics.

Correlation between traits alters the stability and pattern outcomes.

Abstract

Populations can become spatially organised through chemotaxis autoattraction, wherein population members release their own chemoattractant. Standard models of this process usually assume phenotypic homogeneity, but recent studies have shed illumination on the inherent heterogeneity within populations: in terms of chemotactic behaviour, trait heterogeneity can range from the sensitivity to attractant gradients to the rate at which attractants are produced. We propose a framework that accounts for this heterogeneity, extending the standard Keller-Segel model to a non-local formulation in which the population is continuously structured across some phenotype state space. Focussing on autoattraction, we allow both the chemotactic sensitivity and the rate of attractant secretion to vary across the population and suppose members can switch between different phenotype states. We extend classical Turing-type linear stability analyses to determine the impact of phenotypic structuring on pattern formation, showing that the rate of switching influences both the critical condition for self-organisation and subsequent pattern dynamics. Scenarios in which the chemotactic sensitivity and attractant secretion are positively or negatively correlated are used to highlight the significance of these results.