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Sequential mutations in exponentially growing populations

Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing po...

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Detalles Bibliográficos
Autores principales: Nicholson, Michael D., Cheek, David, Antal, Tibor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359018/
https://www.ncbi.nlm.nih.gov/pubmed/37428805
http://dx.doi.org/10.1371/journal.pcbi.1011289
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author Nicholson, Michael D.
Cheek, David
Antal, Tibor
author_facet Nicholson, Michael D.
Cheek, David
Antal, Tibor
author_sort Nicholson, Michael D.
collection PubMed
description Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of n or the mutations’ selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays.
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spelling pubmed-103590182023-07-21 Sequential mutations in exponentially growing populations Nicholson, Michael D. Cheek, David Antal, Tibor PLoS Comput Biol Research Article Stochastic models of sequential mutation acquisition are widely used to quantify cancer and bacterial evolution. Across manifold scenarios, recurrent research questions are: how many cells are there with n alterations, and how long will it take for these cells to appear. For exponentially growing populations, these questions have been tackled only in special cases so far. Here, within a multitype branching process framework, we consider a general mutational path where mutations may be advantageous, neutral or deleterious. In the biologically relevant limiting regimes of large times and small mutation rates, we derive probability distributions for the number, and arrival time, of cells with n mutations. Surprisingly, the two quantities respectively follow Mittag-Leffler and logistic distributions regardless of n or the mutations’ selective effects. Our results provide a rapid method to assess how altering the fundamental division, death, and mutation rates impacts the arrival time, and number, of mutant cells. We highlight consequences for mutation rate inference in fluctuation assays. Public Library of Science 2023-07-10 /pmc/articles/PMC10359018/ /pubmed/37428805 http://dx.doi.org/10.1371/journal.pcbi.1011289 Text en © 2023 Nicholson et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Nicholson, Michael D.
Cheek, David
Antal, Tibor
Sequential mutations in exponentially growing populations
title Sequential mutations in exponentially growing populations
title_full Sequential mutations in exponentially growing populations
title_fullStr Sequential mutations in exponentially growing populations
title_full_unstemmed Sequential mutations in exponentially growing populations
title_short Sequential mutations in exponentially growing populations
title_sort sequential mutations in exponentially growing populations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359018/
https://www.ncbi.nlm.nih.gov/pubmed/37428805
http://dx.doi.org/10.1371/journal.pcbi.1011289
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