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.
Highlights
Bacteria sense chemoattractants (CA) or chemorepellents (CR) through a sequence of stochastic detection events at their chemoreceptors [1, 2] and convert temporal variations in the number of detection events into a directional bias [3,4,5]
Berg and Brown’s original single particle tracking analysis of E. coli [4, 16] shed light on E. coli’s run-and-tumble dynamics and directly motivated the types of models proposed in subsequent decades [2, 37,38,39,40,41,42]
The signaling pathway responsible for E. coli’s chemotactic response has been extensively studied [2, 30, 37, 38, 43] and attention has been focused on internal noise sources arising from the stochasticity of the signaling pathway [22, 44, 45]
Summary
Our goal is to build a ‘top-down’ model valid across bacterial species that will describe how bacteria respond to stochastic detection events to locate point sources
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