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Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics

We consider the cell population dynamics with n different phenotypes. Both the Markovian branching process model (stochastic model) and the ordinary differential equation (ODE) system model (deterministic model) are presented, and exploited to investigate the dynamics of the phenotypic proportions....

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Detalles Bibliográficos
Autores principales: Jiang, Da-Quan, Wang, Yue, Zhou, Da
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300154/
https://www.ncbi.nlm.nih.gov/pubmed/28182672
http://dx.doi.org/10.1371/journal.pone.0170916
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author Jiang, Da-Quan
Wang, Yue
Zhou, Da
author_facet Jiang, Da-Quan
Wang, Yue
Zhou, Da
author_sort Jiang, Da-Quan
collection PubMed
description We consider the cell population dynamics with n different phenotypes. Both the Markovian branching process model (stochastic model) and the ordinary differential equation (ODE) system model (deterministic model) are presented, and exploited to investigate the dynamics of the phenotypic proportions. We will prove that in both models, these proportions will tend to constants regardless of initial population states (“phenotypic equilibrium”) under weak conditions, which explains the experimental phenomenon in Gupta et al.’s paper. We also prove that Gupta et al.’s explanation is the ODE model under a special assumption. As an application, we will give sufficient and necessary conditions under which the proportion of one phenotype tends to 0 (die out) or 1 (dominate). We also extend our results to non-Markovian cases.
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spelling pubmed-53001542017-02-28 Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics Jiang, Da-Quan Wang, Yue Zhou, Da PLoS One Research Article We consider the cell population dynamics with n different phenotypes. Both the Markovian branching process model (stochastic model) and the ordinary differential equation (ODE) system model (deterministic model) are presented, and exploited to investigate the dynamics of the phenotypic proportions. We will prove that in both models, these proportions will tend to constants regardless of initial population states (“phenotypic equilibrium”) under weak conditions, which explains the experimental phenomenon in Gupta et al.’s paper. We also prove that Gupta et al.’s explanation is the ODE model under a special assumption. As an application, we will give sufficient and necessary conditions under which the proportion of one phenotype tends to 0 (die out) or 1 (dominate). We also extend our results to non-Markovian cases. Public Library of Science 2017-02-09 /pmc/articles/PMC5300154/ /pubmed/28182672 http://dx.doi.org/10.1371/journal.pone.0170916 Text en © 2017 Jiang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jiang, Da-Quan
Wang, Yue
Zhou, Da
Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics
title Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics
title_full Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics
title_fullStr Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics
title_full_unstemmed Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics
title_short Phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics
title_sort phenotypic equilibrium as probabilistic convergence in multi-phenotype cell population dynamics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5300154/
https://www.ncbi.nlm.nih.gov/pubmed/28182672
http://dx.doi.org/10.1371/journal.pone.0170916
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