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Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns

Neuronal networks are complex systems of interconnected neurons responsible for transmitting and processing information throughout the nervous system. The building blocks of neuronal networks consist of individual neurons, specialized cells that receive, process, and transmit electrical and chemical...

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Autor principal: Staii, Cristian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296521/
https://www.ncbi.nlm.nih.gov/pubmed/37366862
http://dx.doi.org/10.3390/biomimetics8020267
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author Staii, Cristian
author_facet Staii, Cristian
author_sort Staii, Cristian
collection PubMed
description Neuronal networks are complex systems of interconnected neurons responsible for transmitting and processing information throughout the nervous system. The building blocks of neuronal networks consist of individual neurons, specialized cells that receive, process, and transmit electrical and chemical signals throughout the body. The formation of neuronal networks in the developing nervous system is a process of fundamental importance for understanding brain activity, including perception, memory, and cognition. To form networks, neuronal cells extend long processes called axons, which navigate toward other target neurons guided by both intrinsic and extrinsic factors, including genetic programming, chemical signaling, intercellular interactions, and mechanical and geometrical cues. Despite important recent advances, the basic mechanisms underlying collective neuron behavior and the formation of functional neuronal networks are not entirely understood. In this paper, we present a combined experimental and theoretical analysis of neuronal growth on surfaces with micropatterned periodic geometrical features. We demonstrate that the extension of axons on these surfaces is described by a biased random walk model, in which the surface geometry imparts a constant drift term to the axon, and the stochastic cues produce a random walk around the average growth direction. We show that the model predicts key parameters that describe axonal dynamics: diffusion (cell motility) coefficient, average growth velocity, and axonal mean squared length, and we compare these parameters with the results of experimental measurements. Our findings indicate that neuronal growth is governed by a contact-guidance mechanism, in which the axons respond to external geometrical cues by aligning their motion along the surface micropatterns. These results have a significant impact on developing novel neural network models, as well as biomimetic substrates, to stimulate nerve regeneration and repair after injury.
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spelling pubmed-102965212023-06-28 Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns Staii, Cristian Biomimetics (Basel) Article Neuronal networks are complex systems of interconnected neurons responsible for transmitting and processing information throughout the nervous system. The building blocks of neuronal networks consist of individual neurons, specialized cells that receive, process, and transmit electrical and chemical signals throughout the body. The formation of neuronal networks in the developing nervous system is a process of fundamental importance for understanding brain activity, including perception, memory, and cognition. To form networks, neuronal cells extend long processes called axons, which navigate toward other target neurons guided by both intrinsic and extrinsic factors, including genetic programming, chemical signaling, intercellular interactions, and mechanical and geometrical cues. Despite important recent advances, the basic mechanisms underlying collective neuron behavior and the formation of functional neuronal networks are not entirely understood. In this paper, we present a combined experimental and theoretical analysis of neuronal growth on surfaces with micropatterned periodic geometrical features. We demonstrate that the extension of axons on these surfaces is described by a biased random walk model, in which the surface geometry imparts a constant drift term to the axon, and the stochastic cues produce a random walk around the average growth direction. We show that the model predicts key parameters that describe axonal dynamics: diffusion (cell motility) coefficient, average growth velocity, and axonal mean squared length, and we compare these parameters with the results of experimental measurements. Our findings indicate that neuronal growth is governed by a contact-guidance mechanism, in which the axons respond to external geometrical cues by aligning their motion along the surface micropatterns. These results have a significant impact on developing novel neural network models, as well as biomimetic substrates, to stimulate nerve regeneration and repair after injury. MDPI 2023-06-20 /pmc/articles/PMC10296521/ /pubmed/37366862 http://dx.doi.org/10.3390/biomimetics8020267 Text en © 2023 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Staii, Cristian
Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns
title Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns
title_full Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns
title_fullStr Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns
title_full_unstemmed Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns
title_short Biased Random Walk Model of Neuronal Dynamics on Substrates with Periodic Geometrical Patterns
title_sort biased random walk model of neuronal dynamics on substrates with periodic geometrical patterns
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296521/
https://www.ncbi.nlm.nih.gov/pubmed/37366862
http://dx.doi.org/10.3390/biomimetics8020267
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