Inferring models of bacterial dynamics toward point sources

TL;DR

This paper introduces a statistical model for bacterial tracking of point sources based on stochastic detection, capturing behaviors like the volcano effect and identifying signatures of targeted search in noisy environments.

Contribution

The paper presents a novel statistical model that infers bacterial search behavior towards point sources from single-cell data, even under high detection noise.

Findings

Model accurately reproduces bacterial behavior around point sources.

Parameters can be inferred from single-cell tracking data.

Identifies statistical signatures of targeted search behavior.

Abstract

Experiments have shown that bacteria can be sensitive to small variations in chemoattractant (CA) concentrations. Motivated by these findings, our focus here is on a regime rarely studied in experiments: bacteria tracking point CA sources (such as food patches or even prey). In tracking point sources, the CA detected by bacteria may show very large spatiotemporal fluctuations which vary with distance from the source. We present a general statistical model to describe how bacteria locate point sources of food on the basis of stochastic event detection, rather than CA gradient information. We show how all model parameters can be directly inferred from single cell tracking data even in the limit of high detection noise. Once parameterized, our model recapitulates bacterial behavior around point sources such as the "volcano effect". In addition, while the search by bacteria for point sources such as prey may appear random, our model identifies key statistical signatures of a targeted search for a point source given any arbitrary source configuration.