A model of strongly-biased chemotaxis reveals the tradeoffs of different bacterial migration strategies.

Bearon, R ORCID: 0000-0001-8461-0823 and Durham, william (2020) A model of strongly-biased chemotaxis reveals the tradeoffs of different bacterial migration strategies. Mathematical Medicine and Biology: A Journal of the IMA, 37 (1). pp. 83-116.

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Abstract

Many bacteria actively bias their motility towards more favourable nutrient environments. In liquid, cells rotate their corkscrew-shaped flagella to swim, but in surface attached biofilms cells instead use grappling hook-like appendages called pili to pull themselves along. In both forms of motility, cells selectively alternate between relatively straight 'runs' and sharp reorientations to generate biased random walks up chemoattractant gradients. However, recent experiments suggest that swimming and biofilm cells employ fundamentally different strategies to generate chemotaxis: swimming cells typically suppress reorientations when moving up a chemoattractant gradient, whereas biofilm cells increase reorientations when moving down a chemoattractant gradient. The reason for this difference remains unknown. Here we develop a mathematical framework to understand how these different chemotactic strategies affect the distribution of cells at the population level. Current continuum models typically assume a weak bias in the reorientation rate and are not able to distinguish between these two strategies, so we derive a model for strong chemotaxis that resolves how both the drift and diffusive components depend on the underlying chemotactic strategy. We then test predictions from our continuum model against individual-based simulations and identify further refinements that allow our continuum model to resolve boundary effects. Our analyses reveal that the strategy employed by swimming cells yields a larger chemotactic drift, but the strategy used by biofilm cells allows them to more tightly aggregate where the chemoattractant is most abundant. This new modelling framework provides new quantitative insights into how the different chemical landscapes experienced by swimming and biofilm cells might select for divergent ways of generating chemotaxis.

Item Type:	Article
Uncontrolled Keywords:	bacteria, chemotaxis, biofilms, PDEs, stochastic simulations
Depositing User:	Symplectic Admin
Date Deposited:	14 Mar 2019 09:07
Last Modified:	19 Jan 2023 00:57
DOI:	10.1093/imammb/dqz007
Open Access URL:	https://doi.org/10.1093/imammb/dqz007
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3034168