Communications on Applied Mathematics and Computation· 2026Q2
Chemotaxis Guidance of Random Walkers Modeling Self-Wiring of Neural Networks
- 0citations
- Q2SCImago
- 2026year
Short summary
A new stochastic walker model describes how growth cones navigate chemical gradients, coupling their movement to the diffusion of attractive/repulsive cues they emit.
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Key points
- Proposes a stochastic walker model for growth cone chemotaxis.
- Couples growth cone motion (SDEs) with chemical concentration dynamics (reaction-diffusion equations).
- Proves the existence of a unique solution for the coupled system.
- Numerically investigates sensitivity to biological parameters and nonlocal regularization effects.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract A stochastic walker model is proposed to describe the chemotactic guidance of growth cones, which are the tips of developing neurites. The model accounts for the influence of both attractive and repulsive chemical cues, which are emitted by the growth cones and the somas. The system couples stochastic differential equations governing the motion of the growth cones with reaction-diffusion equations that describe the dynamics of the chemical concentrations. The existence of a unique solution to this coupled system is proved. Numerical experiments are performed to investigate the sensitivity of the model to key biological parameters. The impact of the nonlocal regularization of point sources in the reaction-diffusion equations is analyzed in a simplified deterministic setting.
The authors' abstract, as published at the source. Communications on Applied Mathematics and Computation, 2026 · DOI ↗
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Field: Modeling and Simulation
Modeling and SimulationMathematics