Role of Non-Exponential Reversal times in Aggregation Models of Bacterial Populations

TL;DR

This paper investigates how non-exponential reversal times, modeled by Gamma distributions, influence bacterial aggregation, showing they promote tighter aggregates and improve alignment with experimental observations.

Contribution

It introduces the use of Gamma-distributed reversal times in aggregation models, highlighting their impact on aggregation dynamics and model accuracy.

Findings

Non-exponential reversal times enhance aggregation.

Models with Gamma-distributed reversal times produce tighter aggregates.

Incorporating realistic reversal time distributions improves experimental data fit.

Abstract

In this paper, we consider 1D agent-based and kinetic models of aggregation with reversals. In particular, we fit a Gamma distribution to represent the run times in myxobacteria and analyze numerically the importance of non-exponential reversal times. We demonstrate that non-exponential reversal times aid aggregation and result in tighter aggregates. We compare and contrast the behavior of agent-based and kinetic models, and also consider kinetic models with aggregation driven by chemotaxis. Thus, incorporating non-exponential reversal times into models of aggregation can be particularly important for reproducing experimental data, such as aggregate persistence and dispersal.