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Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model
The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactio...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529768/ https://www.ncbi.nlm.nih.gov/pubmed/33004880 http://dx.doi.org/10.1038/s41598-020-73228-4 |
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author | Wang, Minhong Tsanas, Athanasios Blin, Guillaume Robertson, Dave |
author_facet | Wang, Minhong Tsanas, Athanasios Blin, Guillaume Robertson, Dave |
author_sort | Wang, Minhong |
collection | PubMed |
description | The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach in order to identify plausible biological rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours with or without biases due to neighbour interactions. We introduced a new metric, termed stem cell aggregate pattern distance (SCAPD) to probabilistically assess the fitness of our models with empirical data. The best of our models improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement respectively. Collectively, our findings show that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Our work also defines a region of the parameter space that is compatible with patterning. We hope that our approach will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments. |
format | Online Article Text |
id | pubmed-7529768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75297682020-10-02 Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model Wang, Minhong Tsanas, Athanasios Blin, Guillaume Robertson, Dave Sci Rep Article The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach in order to identify plausible biological rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours with or without biases due to neighbour interactions. We introduced a new metric, termed stem cell aggregate pattern distance (SCAPD) to probabilistically assess the fitness of our models with empirical data. The best of our models improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement respectively. Collectively, our findings show that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Our work also defines a region of the parameter space that is compatible with patterning. We hope that our approach will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments. Nature Publishing Group UK 2020-10-01 /pmc/articles/PMC7529768/ /pubmed/33004880 http://dx.doi.org/10.1038/s41598-020-73228-4 Text en © The Author(s) 2020 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/. |
spellingShingle | Article Wang, Minhong Tsanas, Athanasios Blin, Guillaume Robertson, Dave Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model |
title | Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model |
title_full | Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model |
title_fullStr | Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model |
title_full_unstemmed | Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model |
title_short | Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model |
title_sort | predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529768/ https://www.ncbi.nlm.nih.gov/pubmed/33004880 http://dx.doi.org/10.1038/s41598-020-73228-4 |
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