eLife· 2016Q1
RETRACTED: A mathematical model explains saturating axon guidance responses to molecular gradients
- 1,258citations
- Q1SCImago
- 2016year
Short summary
A new mathematical model, based on persistence, bias, and noise, accurately predicts growth cone trajectories and explains why their turning angles in molecular gradients saturate rapidly at low values.
AI-generated from the title and abstract; the full text is not read.
Key points
- A mathematical model incorporating persistence, bias, and noise is proposed to describe growth cone movement in molecular gradients.
- The model is constrained by detailed statistical measurements of growth cone trajectories in attractive and repulsive gradients.
- It provides a mathematical explanation for the rapid saturation of average axon turning angles at low values in response to gradients.
- This model is the most accurate predictive tool for growth cone trajectories developed to date.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. eLife, 2016 · DOI ↗
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Field: Cellular and Molecular Neuroscience
Cellular and Molecular NeuroscienceNeuroscience