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Evolutionary regime transitions in structured populations

The evolutionary dynamics of a finite population where resident individuals are replaced by mutant ones depends on its spatial structure. Usually, the population adopts the form of an undirected graph where the place occupied by each individual is represented by a vertex and it is bidirectionally li...

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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 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261048/
https://www.ncbi.nlm.nih.gov/pubmed/30475815
http://dx.doi.org/10.1371/journal.pone.0200670
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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 The evolutionary dynamics of a finite population where resident individuals are replaced by mutant ones depends on its spatial structure. Usually, the population adopts the form of an undirected graph where the place occupied by each individual is represented by a vertex and it is bidirectionally linked to the places that can be occupied by its offspring. There are undirected graph structures that act as amplifiers of selection increasing the probability that the offspring of an advantageous mutant spreads through the graph reaching any vertex. But there also are undirected graph structures acting as suppressors of selection where this probability is less than that of the same individual placed in a homogeneous population. Here, firstly, we present the distribution of these evolutionary regimes for all undirected graphs with N ≤ 10 vertices. Some of them exhibit transitions between different regimes when the mutant fitness increases. In particular, as it has been already observed for small-order random graphs, we show that most graphs of order N ≤ 10 are amplifiers of selection. Secondly, we describe examples of amplifiers of order 7 that become suppressors from some critical value. In fact, for graphs of order N ≤ 7, we apply computer-aided techniques to symbolically compute their fixation probability and then their evolutionary regime, as well as the critical values for which they change their regime. Thirdly, the same technique is applied to some families of highly symmetrical graphs as a mean to explore methods of suppressing selection. The existence of suppression mechanisms that reverse an amplification regime when fitness increases could have a great interest in biology and network science. Finally, the analysis of all graphs from order 8 to order 10 reveals a complex and rich evolutionary dynamics, with multiple transitions between different regimes, which have not been examined in detail until now.
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spelling pubmed-62610482018-12-06 Evolutionary regime transitions in structured populations Alcalde Cuesta, Fernando González Sequeiros, Pablo Lozano Rojo, Álvaro PLoS One Research Article The evolutionary dynamics of a finite population where resident individuals are replaced by mutant ones depends on its spatial structure. Usually, the population adopts the form of an undirected graph where the place occupied by each individual is represented by a vertex and it is bidirectionally linked to the places that can be occupied by its offspring. There are undirected graph structures that act as amplifiers of selection increasing the probability that the offspring of an advantageous mutant spreads through the graph reaching any vertex. But there also are undirected graph structures acting as suppressors of selection where this probability is less than that of the same individual placed in a homogeneous population. Here, firstly, we present the distribution of these evolutionary regimes for all undirected graphs with N ≤ 10 vertices. Some of them exhibit transitions between different regimes when the mutant fitness increases. In particular, as it has been already observed for small-order random graphs, we show that most graphs of order N ≤ 10 are amplifiers of selection. Secondly, we describe examples of amplifiers of order 7 that become suppressors from some critical value. In fact, for graphs of order N ≤ 7, we apply computer-aided techniques to symbolically compute their fixation probability and then their evolutionary regime, as well as the critical values for which they change their regime. Thirdly, the same technique is applied to some families of highly symmetrical graphs as a mean to explore methods of suppressing selection. The existence of suppression mechanisms that reverse an amplification regime when fitness increases could have a great interest in biology and network science. Finally, the analysis of all graphs from order 8 to order 10 reveals a complex and rich evolutionary dynamics, with multiple transitions between different regimes, which have not been examined in detail until now. Public Library of Science 2018-11-26 /pmc/articles/PMC6261048/ /pubmed/30475815 http://dx.doi.org/10.1371/journal.pone.0200670 Text en © 2018 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
Evolutionary regime transitions in structured populations
title Evolutionary regime transitions in structured populations
title_full Evolutionary regime transitions in structured populations
title_fullStr Evolutionary regime transitions in structured populations
title_full_unstemmed Evolutionary regime transitions in structured populations
title_short Evolutionary regime transitions in structured populations
title_sort evolutionary regime transitions in structured populations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6261048/
https://www.ncbi.nlm.nih.gov/pubmed/30475815
http://dx.doi.org/10.1371/journal.pone.0200670
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