A Bayesian model predicts the response of axons to molecular gradients

Duncan Mortimer, Julia Feldner, Timothy Vaughan, Irina Vetter, Zac Pujic, William J. Rosoff, Kevin Burrage, Peter Dayan, Linda J. Richards, Geoffrey J. Goodhill

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian "ideal observer" analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings.

Original languageEnglish
Pages (from-to)10296-10301
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume106
Issue number25
DOIs
StatePublished - Jun 23 2009

Keywords

  • Axon guidance
  • Chemotaxis
  • Growth cone
  • Nerve growth factor
  • Nerve regeneration

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