A mathematical model explains saturating axon guidance responses to molecular gradients

Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molec...

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Veröffentlicht in:eLife 2016-02, Vol.5, p.e12248-e12248
Hauptverfasser: Nguyen, Huyen, Dayan, Peter, Pujic, Zac, Cooper-White, Justin, Goodhill, Geoffrey J
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creator Nguyen, Huyen
Dayan, Peter
Pujic, Zac
Cooper-White, Justin
Goodhill, Geoffrey J
description Correct wiring is crucial for the proper functioning of the nervous system. Molecular gradients provide critical signals to guide growth cones, which are the motile tips of developing axons, to their targets. However, in vitro, growth cones trace highly stochastic trajectories, and exactly how molecular gradients bias their movement is unclear. Here, we introduce a mathematical model based on persistence, bias, and noise to describe this behaviour, constrained directly by measurements of the detailed statistics of growth cone movements in both attractive and repulsive gradients in a microfluidic device. This model provides a mathematical explanation for why average axon turning angles in gradients in vitro saturate very rapidly with time at relatively small values. This work introduces the most accurate predictive model of growth cone trajectories to date, and deepens our understanding of axon guidance events both in vitro and in vivo.
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subjects Animal behavior
Animals
Axon guidance
Axons
Axons - drug effects
Axons - physiology
Cells, Cultured
Chemotaxis
Growth cones
Growth Cones - drug effects
Growth Cones - physiology
Invertebrates
Lab-On-A-Chip Devices
Mathematical models
Microfluidics
Models, Theoretical
Nervous system
Neural circuitry
Neuroscience
Noise
Physiological aspects
Rats, Wistar
Stochasticity
title A mathematical model explains saturating axon guidance responses to molecular gradients
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