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Suppressors of selection

Inspired by recent works on evolutionary graph theory, an area of growing interest in mathematical and computational biology, we present examples of undirected structures acting as suppressors of selection for any fitness value r > 1. This means that the average fixation probability of an advanta...

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Autores principales: Alcalde Cuesta, Fernando, González Sequeiros, Pablo, Lozano Rojo, Álvaro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5503266/
https://www.ncbi.nlm.nih.gov/pubmed/28700698
http://dx.doi.org/10.1371/journal.pone.0180549
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author Alcalde Cuesta, Fernando
González Sequeiros, Pablo
Lozano Rojo, Álvaro
author_facet Alcalde Cuesta, Fernando
González Sequeiros, Pablo
Lozano Rojo, Álvaro
author_sort Alcalde Cuesta, Fernando
collection PubMed
description Inspired by recent works on evolutionary graph theory, an area of growing interest in mathematical and computational biology, we present examples of undirected structures acting as suppressors of selection for any fitness value r > 1. This means that the average fixation probability of an advantageous mutant or invader individual placed at some node is strictly less than that of this individual placed in a well-mixed population. This leads the way to study more robust structures less prone to invasion, contrary to what happens with the amplifiers of selection where the fixation probability is increased on average for advantageous invader individuals. A few families of amplifiers are known, although some effort was required to prove it. Here, we use computer aided techniques to find an exact analytical expression of the fixation probability for some graphs of small order (equal to 6, 8 and 10) proving that selection is effectively reduced for r > 1. Some numerical experiments using Monte Carlo methods are also performed for larger graphs and some variants.
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spelling pubmed-55032662017-07-25 Suppressors of selection Alcalde Cuesta, Fernando González Sequeiros, Pablo Lozano Rojo, Álvaro PLoS One Research Article Inspired by recent works on evolutionary graph theory, an area of growing interest in mathematical and computational biology, we present examples of undirected structures acting as suppressors of selection for any fitness value r > 1. This means that the average fixation probability of an advantageous mutant or invader individual placed at some node is strictly less than that of this individual placed in a well-mixed population. This leads the way to study more robust structures less prone to invasion, contrary to what happens with the amplifiers of selection where the fixation probability is increased on average for advantageous invader individuals. A few families of amplifiers are known, although some effort was required to prove it. Here, we use computer aided techniques to find an exact analytical expression of the fixation probability for some graphs of small order (equal to 6, 8 and 10) proving that selection is effectively reduced for r > 1. Some numerical experiments using Monte Carlo methods are also performed for larger graphs and some variants. Public Library of Science 2017-07-10 /pmc/articles/PMC5503266/ /pubmed/28700698 http://dx.doi.org/10.1371/journal.pone.0180549 Text en © 2017 Alcalde Cuesta 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
Alcalde Cuesta, Fernando
González Sequeiros, Pablo
Lozano Rojo, Álvaro
Suppressors of selection
title Suppressors of selection
title_full Suppressors of selection
title_fullStr Suppressors of selection
title_full_unstemmed Suppressors of selection
title_short Suppressors of selection
title_sort suppressors of selection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5503266/
https://www.ncbi.nlm.nih.gov/pubmed/28700698
http://dx.doi.org/10.1371/journal.pone.0180549
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