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Computational models of migration modes improve our understanding of metastasis
Tumor cells migrate through changing microenvironments of diseased and healthy tissue, making their migration particularly challenging to describe. To better understand this process, computational models have been developed for both the ameboid and mesenchymal modes of cell migration. Here, we revie...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AIP Publishing LLC
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647620/ https://www.ncbi.nlm.nih.gov/pubmed/33195959 http://dx.doi.org/10.1063/5.0023748 |
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author | Shatkin, Gabriel Yeoman, Benjamin Birmingham, Katherine Katira, Parag Engler, Adam J. |
author_facet | Shatkin, Gabriel Yeoman, Benjamin Birmingham, Katherine Katira, Parag Engler, Adam J. |
author_sort | Shatkin, Gabriel |
collection | PubMed |
description | Tumor cells migrate through changing microenvironments of diseased and healthy tissue, making their migration particularly challenging to describe. To better understand this process, computational models have been developed for both the ameboid and mesenchymal modes of cell migration. Here, we review various approaches that have been used to account for the physical environment's effect on cell migration in computational models, with a focus on their application to understanding cancer metastasis and the related phenomenon of durotaxis. We then discuss how mesenchymal migration models typically simulate complex cell–extracellular matrix (ECM) interactions, while ameboid migration models use a cell-focused approach that largely ignores ECM when not acting as a physical barrier. This approach greatly simplifies or ignores the mechanosensing ability of ameboid migrating cells and should be reevaluated in future models. We conclude by describing future model elements that have not been included to date but would enhance model accuracy. |
format | Online Article Text |
id | pubmed-7647620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AIP Publishing LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-76476202020-11-13 Computational models of migration modes improve our understanding of metastasis Shatkin, Gabriel Yeoman, Benjamin Birmingham, Katherine Katira, Parag Engler, Adam J. APL Bioeng Reviews Tumor cells migrate through changing microenvironments of diseased and healthy tissue, making their migration particularly challenging to describe. To better understand this process, computational models have been developed for both the ameboid and mesenchymal modes of cell migration. Here, we review various approaches that have been used to account for the physical environment's effect on cell migration in computational models, with a focus on their application to understanding cancer metastasis and the related phenomenon of durotaxis. We then discuss how mesenchymal migration models typically simulate complex cell–extracellular matrix (ECM) interactions, while ameboid migration models use a cell-focused approach that largely ignores ECM when not acting as a physical barrier. This approach greatly simplifies or ignores the mechanosensing ability of ameboid migrating cells and should be reevaluated in future models. We conclude by describing future model elements that have not been included to date but would enhance model accuracy. AIP Publishing LLC 2020-11-05 /pmc/articles/PMC7647620/ /pubmed/33195959 http://dx.doi.org/10.1063/5.0023748 Text en © Author(s). 2473-2877/2020/4(4)/041505/9 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Reviews Shatkin, Gabriel Yeoman, Benjamin Birmingham, Katherine Katira, Parag Engler, Adam J. Computational models of migration modes improve our understanding of metastasis |
title | Computational models of migration modes improve our understanding of metastasis |
title_full | Computational models of migration modes improve our understanding of metastasis |
title_fullStr | Computational models of migration modes improve our understanding of metastasis |
title_full_unstemmed | Computational models of migration modes improve our understanding of metastasis |
title_short | Computational models of migration modes improve our understanding of metastasis |
title_sort | computational models of migration modes improve our understanding of metastasis |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647620/ https://www.ncbi.nlm.nih.gov/pubmed/33195959 http://dx.doi.org/10.1063/5.0023748 |
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