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Experiments have shown that bacteria can be sensitive to small variations inchemoattractant (CA) concentrations. Motivated by these findings, our focushere is on a regime rarely studied in experiments: bacteria tracking point CAsources (such as food patches or even prey). In tracking point sources, the CAdetected by bacteria may show very large spatiotemporal fluctuations which varywith distance from the source. We present a general statistical model todescribe how bacteria locate point sources of food on the basis of stochasticevent detection, rather than CA gradient information. We show how all modelparameters can be directly inferred from single cell tracking data even in thelimit of high detection noise. Once parameterized, our model recapitulatesbacterial behavior around point sources such as the "volcano effect". Inaddition, while the search by bacteria for point sources such as prey mayappear random, our model identifies key statistical signatures of a targetedsearch for a point source given any arbitrary source configuration.

Inferring models of bacterial dynamics toward point sources