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A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch
Experiments show that when a monolayer of cells cultured on an elastic substratum is subject to a cyclic stretch, cells tend to reorient either perpendicularly or at an oblique angle with respect to the main stretching direction. Due to stochastic effects, however, the distribution of angles achieve...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229726/ https://www.ncbi.nlm.nih.gov/pubmed/37249663 http://dx.doi.org/10.1007/s11538-023-01161-4 |
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author | Loy, N. Preziosi, L. |
author_facet | Loy, N. Preziosi, L. |
author_sort | Loy, N. |
collection | PubMed |
description | Experiments show that when a monolayer of cells cultured on an elastic substratum is subject to a cyclic stretch, cells tend to reorient either perpendicularly or at an oblique angle with respect to the main stretching direction. Due to stochastic effects, however, the distribution of angles achieved by the cells is broader and, experimentally, histograms over the interval [Formula: see text] are usually reported. Here we will determine the evolution and the stationary state of probability density functions describing the statistical distribution of the orientations of the cells using Fokker–Planck equations derived from microscopic rules for describing the reorientation process of the cell. As a first attempt, we shall use a stochastic differential equation related to a very general elastic energy that the cell tries to minimize and, we will show that the results of the time integration and of the stationary state of the related forward Fokker–Planck equation compare very well with experimental results obtained by different researchers. Then, in order to model more accurately the microscopic process of cell reorientation and to shed light on the mechanisms performed by cells that are subject to cyclic stretch, we consider discrete in time random processes that allow to recover Fokker–Planck equations through classical tools of kinetic theory. In particular, we shall introduce a model of reorientation as a function of the rotation angle as a result of an optimal control problem. Also in this latter case the results match very well with experiments. |
format | Online Article Text |
id | pubmed-10229726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-102297262023-06-01 A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch Loy, N. Preziosi, L. Bull Math Biol Original Article Experiments show that when a monolayer of cells cultured on an elastic substratum is subject to a cyclic stretch, cells tend to reorient either perpendicularly or at an oblique angle with respect to the main stretching direction. Due to stochastic effects, however, the distribution of angles achieved by the cells is broader and, experimentally, histograms over the interval [Formula: see text] are usually reported. Here we will determine the evolution and the stationary state of probability density functions describing the statistical distribution of the orientations of the cells using Fokker–Planck equations derived from microscopic rules for describing the reorientation process of the cell. As a first attempt, we shall use a stochastic differential equation related to a very general elastic energy that the cell tries to minimize and, we will show that the results of the time integration and of the stationary state of the related forward Fokker–Planck equation compare very well with experimental results obtained by different researchers. Then, in order to model more accurately the microscopic process of cell reorientation and to shed light on the mechanisms performed by cells that are subject to cyclic stretch, we consider discrete in time random processes that allow to recover Fokker–Planck equations through classical tools of kinetic theory. In particular, we shall introduce a model of reorientation as a function of the rotation angle as a result of an optimal control problem. Also in this latter case the results match very well with experiments. Springer US 2023-05-30 2023 /pmc/articles/PMC10229726/ /pubmed/37249663 http://dx.doi.org/10.1007/s11538-023-01161-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Loy, N. Preziosi, L. A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch |
title | A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch |
title_full | A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch |
title_fullStr | A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch |
title_full_unstemmed | A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch |
title_short | A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch |
title_sort | statistical mechanics approach to describe cell reorientation under stretch |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229726/ https://www.ncbi.nlm.nih.gov/pubmed/37249663 http://dx.doi.org/10.1007/s11538-023-01161-4 |
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