A Bayesian model predicts the response of axons to molecular gradients

Mortimer, Duncan, Feldner, Julia, Vaughan, Timothy, Vetter, Irina, Pujic, Zac, Rosoff, William J., Burrage, Kevin, Dayan, Peter, Richards, Linda J. and Goodhill, Geoffrey J. (2009) A Bayesian model predicts the response of axons to molecular gradients. Proceedings of the National Academy of Sciences of the United States of America, 106 25: 10296-10301. doi:10.1073/pnas.0900715106


Author Mortimer, Duncan
Feldner, Julia
Vaughan, Timothy
Vetter, Irina
Pujic, Zac
Rosoff, William J.
Burrage, Kevin
Dayan, Peter
Richards, Linda J.
Goodhill, Geoffrey J.
Title A Bayesian model predicts the response of axons to molecular gradients
Journal name Proceedings of the National Academy of Sciences of the United States of America   Check publisher's open access policy
ISSN 0027-8424
1091-6490
Publication date 2009-06-23
Year available 2009
Sub-type Article (original research)
DOI 10.1073/pnas.0900715106
Volume 106
Issue 25
Start page 10296
End page 10301
Total pages 6
Editor Nicholas R. Cozzarelli
Place of publication Washington , D.C.
Publisher National Academy of Science
Collection year 2010
Language eng
Subject C1
110903 Central Nervous System
110902 Cellular Nervous System
920111 Nervous System and Disorders
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.
Keyword axon guidance
chemotaxis
Growth Cone
Nerve Growth Factor
nerve regeneration
Q-Index Code C1
Q-Index Status Confirmed Code
Additional Notes Published online before print June 18, 2009

 
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Created: Tue, 14 Jul 2009, 15:13:48 EST by Debra McMurtrie on behalf of Queensland Brain Institute