  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.
