Bayes-Optimal chemotaxis

Duncan Mortimer, Peter Dayan, Kevin Burrage, Geoffrey J. Goodhill

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Chemotaxis plays a crucial role in many biological processes, including nervous system development. However, fundamental physical constraints limit the ability of a small sensing device such as a cell or growth cone to detect an external chemical gradient. One of these is the stochastic nature of receptor binding, leading to a constantly fluctuating binding pattern across the cell's array of receptors. This is analogous to the uncertainty in sensory information often encountered by the brain at the systems level. Here we derive analytically the Bayes-optimal strategy for combining information from a spatial array of receptors in both one and two dimensions to determine gradient direction. We also show how information from more than one receptor species can be optimally integrated, derive the gradient shapes that are optimal for guiding cells or growth cones over the longest possible distances, and illustrate that polarized cell behavior might arise as an adaptation to slowly varying environments. Together our results provide closed-form predictions for variations in chemotactic performance over a wide range of gradient conditions.

Original languageEnglish
Pages (from-to)336-373
Number of pages38
JournalNeural Computation
Volume23
Issue number2
DOIs
StatePublished - Feb 2011

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