Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/61958

Title

A Bayesian model predicts the response of axons to molecular gradients

Author(s)

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

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.

Publication type

Journal article

Source

Proceedings of the National Academy of Sciences of the United States of America, Vol. 106, no. 25 (Jun 2009), pp. 10296-10301

Publication year

2009

Keyword(s)

Axon guidance;  Chemotasix;  Growth cone;  Nerve growth factor;  Nerve regeneration

Publisher

National Academy of Sciences

ISSN

1091-6490

Publisher URL

http://dx.doi.org/10.1073/pnas.0900715106

Copyright

Copyright © 2009 The authors.

Peer reviewed