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Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution

The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have develope...

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Detalles Bibliográficos
Autores principales: Kaneko, Tadamune, Kikuchi, Macoto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8803174/
https://www.ncbi.nlm.nih.gov/pubmed/35045068
http://dx.doi.org/10.1371/journal.pcbi.1009796
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author Kaneko, Tadamune
Kikuchi, Macoto
author_facet Kaneko, Tadamune
Kikuchi, Macoto
author_sort Kaneko, Tadamune
collection PubMed
description The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have developed their functions through evolutionary processes. To understand the particularities of this process theoretically, evolutionary simulation (ES) alone is insufficient because the outcomes of ES depend on evolutionary pathways. We need a reference system for comparison. An appropriate reference system for this purpose is an ensemble of the randomly sampled genotypes. However, generating high-fitness genotypes by simple random sampling is difficult because such genotypes are rare. In this study, we used the multicanonical Monte Carlo method developed in statistical physics to construct a reference ensemble of GRNs and compared it with the outcomes of ES. We obtained the following results. First, mutational robustness was significantly higher in ES than in the reference ensemble at the same fitness level. Second, the emergence of a new phenotype, bistability, was delayed in evolution. Third, the bistable group of GRNs contains many mutationally fragile GRNs compared with those in the non-bistable group. This suggests that the delayed emergence of bistability is a consequence of the mutation-selection mechanism.
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spelling pubmed-88031742022-02-01 Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution Kaneko, Tadamune Kikuchi, Macoto PLoS Comput Biol Research Article The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have developed their functions through evolutionary processes. To understand the particularities of this process theoretically, evolutionary simulation (ES) alone is insufficient because the outcomes of ES depend on evolutionary pathways. We need a reference system for comparison. An appropriate reference system for this purpose is an ensemble of the randomly sampled genotypes. However, generating high-fitness genotypes by simple random sampling is difficult because such genotypes are rare. In this study, we used the multicanonical Monte Carlo method developed in statistical physics to construct a reference ensemble of GRNs and compared it with the outcomes of ES. We obtained the following results. First, mutational robustness was significantly higher in ES than in the reference ensemble at the same fitness level. Second, the emergence of a new phenotype, bistability, was delayed in evolution. Third, the bistable group of GRNs contains many mutationally fragile GRNs compared with those in the non-bistable group. This suggests that the delayed emergence of bistability is a consequence of the mutation-selection mechanism. Public Library of Science 2022-01-19 /pmc/articles/PMC8803174/ /pubmed/35045068 http://dx.doi.org/10.1371/journal.pcbi.1009796 Text en © 2022 Kaneko, Kikuchi 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
Kaneko, Tadamune
Kikuchi, Macoto
Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
title Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
title_full Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
title_fullStr Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
title_full_unstemmed Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
title_short Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
title_sort evolution enhances mutational robustness and suppresses the emergence of a new phenotype: a new computational approach for studying evolution
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8803174/
https://www.ncbi.nlm.nih.gov/pubmed/35045068
http://dx.doi.org/10.1371/journal.pcbi.1009796
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