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Mathematical Models of Organoid Cultures
Organoids are engineered three-dimensional tissue cultures derived from stem cells and capable of self-renewal and self-organization into a variety of progenitors and differentiated cell types. An organoid resembles the cellular structure of an organ and retains some of its functionality, while stil...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6761251/ https://www.ncbi.nlm.nih.gov/pubmed/31592020 http://dx.doi.org/10.3389/fgene.2019.00873 |
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author | Montes-Olivas, Sandra Marucci, Lucia Homer, Martin |
author_facet | Montes-Olivas, Sandra Marucci, Lucia Homer, Martin |
author_sort | Montes-Olivas, Sandra |
collection | PubMed |
description | Organoids are engineered three-dimensional tissue cultures derived from stem cells and capable of self-renewal and self-organization into a variety of progenitors and differentiated cell types. An organoid resembles the cellular structure of an organ and retains some of its functionality, while still being amenable to in vitro experimental study. Compared with two-dimensional cultures, the three-dimensional structure of organoids provides a more realistic environment and structural organization of in vivo organs. Similarly, organoids are better suited to reproduce signaling pathway dynamics in vitro, due to a more realistic physiological environment. As such, organoids are a valuable tool to explore the dynamics of organogenesis and offer routes to personalized preclinical trials of cancer progression, invasion, and drug response. Complementary to experiments, mathematical and computational models are valuable instruments in the description of spatiotemporal dynamics of organoids. Simulations of mathematical models allow the study of multiscale dynamics of organoids, at both the intracellular and intercellular levels. Mathematical models also enable us to understand the underlying mechanisms responsible for phenotypic variation and the response to external stimulation in a cost- and time-effective manner. Many recent studies have developed laboratory protocols to grow organoids resembling different organs such as the intestine, brain, liver, pancreas, and mammary glands. However, the development of mathematical models specific to organoids remains comparatively underdeveloped. Here, we review the mathematical and computational approaches proposed so far to describe and predict organoid dynamics, reporting the simulation frameworks used and the models’ strengths and limitations. |
format | Online Article Text |
id | pubmed-6761251 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67612512019-10-07 Mathematical Models of Organoid Cultures Montes-Olivas, Sandra Marucci, Lucia Homer, Martin Front Genet Genetics Organoids are engineered three-dimensional tissue cultures derived from stem cells and capable of self-renewal and self-organization into a variety of progenitors and differentiated cell types. An organoid resembles the cellular structure of an organ and retains some of its functionality, while still being amenable to in vitro experimental study. Compared with two-dimensional cultures, the three-dimensional structure of organoids provides a more realistic environment and structural organization of in vivo organs. Similarly, organoids are better suited to reproduce signaling pathway dynamics in vitro, due to a more realistic physiological environment. As such, organoids are a valuable tool to explore the dynamics of organogenesis and offer routes to personalized preclinical trials of cancer progression, invasion, and drug response. Complementary to experiments, mathematical and computational models are valuable instruments in the description of spatiotemporal dynamics of organoids. Simulations of mathematical models allow the study of multiscale dynamics of organoids, at both the intracellular and intercellular levels. Mathematical models also enable us to understand the underlying mechanisms responsible for phenotypic variation and the response to external stimulation in a cost- and time-effective manner. Many recent studies have developed laboratory protocols to grow organoids resembling different organs such as the intestine, brain, liver, pancreas, and mammary glands. However, the development of mathematical models specific to organoids remains comparatively underdeveloped. Here, we review the mathematical and computational approaches proposed so far to describe and predict organoid dynamics, reporting the simulation frameworks used and the models’ strengths and limitations. Frontiers Media S.A. 2019-09-19 /pmc/articles/PMC6761251/ /pubmed/31592020 http://dx.doi.org/10.3389/fgene.2019.00873 Text en Copyright © 2019 Montes-Olivas, Marucci and Homer http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Montes-Olivas, Sandra Marucci, Lucia Homer, Martin Mathematical Models of Organoid Cultures |
title | Mathematical Models of Organoid Cultures |
title_full | Mathematical Models of Organoid Cultures |
title_fullStr | Mathematical Models of Organoid Cultures |
title_full_unstemmed | Mathematical Models of Organoid Cultures |
title_short | Mathematical Models of Organoid Cultures |
title_sort | mathematical models of organoid cultures |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6761251/ https://www.ncbi.nlm.nih.gov/pubmed/31592020 http://dx.doi.org/10.3389/fgene.2019.00873 |
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