Optimality and saturation in axonal chemotaxis

Yuan, Jiajia, Chan, Stanley, Mortimer, Duncan, Nguyen, Huyen and Goodhill, Geoffrey J. (2013) Optimality and saturation in axonal chemotaxis. Neural Computation, 25 4: 833-853. doi:10.1162/NECO_a_00426

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Author Yuan, Jiajia
Chan, Stanley
Mortimer, Duncan
Nguyen, Huyen
Goodhill, Geoffrey J.
Title Optimality and saturation in axonal chemotaxis
Journal name Neural Computation   Check publisher's open access policy
ISSN 0899-7667
1530-888X
Publication date 2013-04
Sub-type Article (original research)
DOI 10.1162/NECO_a_00426
Open Access Status File (Publisher version)
Volume 25
Issue 4
Start page 833
End page 853
Total pages 21
Place of publication Cambridge, MA, United States
Publisher M I T Press
Collection year 2014
Language eng
Abstract Chemotaxis (detecting and following chemical gradients) plays a crucial role in the function of many biological systems. In particular, gradient following by neuronal growth cones is important for the correct wiring of the nervous system. There is increasing interest in the constraints that determine how small chemotacting devices respond to gradients, but little quantitative information is available in this regard for neuronal growth cones. Mortimer et al. (2009) and Mortimer, Dayan, Burrage, and Goodhill (2011) proposed a Bayesian ideal observer modelthat predicts chemotactic performance for shallow gradients.Herewe investigated two importantaspects of this model. First, we found by numerical simulation that although the analytical predictions of the model assume shallow gradients, these predictions remain remarkably robust tolarge deviations in gradient steepness. Second, we found experimentally that the chemotactic response increased linearly with gradient steepness for very shallow gradients as predicted by the model; however, the response saturated for steeper gradients. This saturation could be reproduced in simulations of a growth rate modulation response mechanism. Together these results illuminate the domain of validity of the Bayesian model and give further insight into the biological mechanisms of axonal chemotaxis.
Keyword Chemotaxis
Chemotacting devices
Gradients
Neuronal growth cones
Axonal chemotaxis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Queensland Brain Institute Publications
Official 2014 Collection
 
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