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Symmetry in models of natural selection

Symmetry arguments are frequently used—often implicitly—in mathematical modelling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natura...

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Detalles Bibliográficos
Autor principal: Allen, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645516/
https://www.ncbi.nlm.nih.gov/pubmed/37963562
http://dx.doi.org/10.1098/rsif.2023.0306
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author Allen, Benjamin
author_facet Allen, Benjamin
author_sort Allen, Benjamin
collection PubMed
description Symmetry arguments are frequently used—often implicitly—in mathematical modelling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natural selection. This definition applies to a broad class of models that satisfy a minimal set of assumptions, using a framework developed in previous works. In this framework, population structure is represented by a set of sites at which alleles can live, and transitions occur via replacement of some alleles by copies of others. A symmetry is defined as a permutation of sites that preserves probabilities of replacement and mutation. The symmetries of a given selection process form a group, which acts on population states in a way that preserves the Markov chain representing selection. Applying classical results on group actions, I formally characterize the use of symmetry to reduce the states of this Markov chain, and obtain bounds on the number of states in the reduced chain.
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spelling pubmed-106455162023-11-15 Symmetry in models of natural selection Allen, Benjamin J R Soc Interface Life Sciences–Mathematics interface Symmetry arguments are frequently used—often implicitly—in mathematical modelling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natural selection. This definition applies to a broad class of models that satisfy a minimal set of assumptions, using a framework developed in previous works. In this framework, population structure is represented by a set of sites at which alleles can live, and transitions occur via replacement of some alleles by copies of others. A symmetry is defined as a permutation of sites that preserves probabilities of replacement and mutation. The symmetries of a given selection process form a group, which acts on population states in a way that preserves the Markov chain representing selection. Applying classical results on group actions, I formally characterize the use of symmetry to reduce the states of this Markov chain, and obtain bounds on the number of states in the reduced chain. The Royal Society 2023-11-15 /pmc/articles/PMC10645516/ /pubmed/37963562 http://dx.doi.org/10.1098/rsif.2023.0306 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Life Sciences–Mathematics interface
Allen, Benjamin
Symmetry in models of natural selection
title Symmetry in models of natural selection
title_full Symmetry in models of natural selection
title_fullStr Symmetry in models of natural selection
title_full_unstemmed Symmetry in models of natural selection
title_short Symmetry in models of natural selection
title_sort symmetry in models of natural selection
topic Life Sciences–Mathematics interface
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645516/
https://www.ncbi.nlm.nih.gov/pubmed/37963562
http://dx.doi.org/10.1098/rsif.2023.0306
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