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Populations of genetic circuits are unable to find the fittest solution in a multilevel genotype-phenotype map

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dc.affiliation.dptoUC3M. Departamento de Matemáticases
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Interdisciplinar de Sistemas Complejos (GISC)es
dc.contributor.authorCatalan, Pablo
dc.contributor.authorManrubia, Susanna
dc.contributor.authorCuesta, José A.
dc.date.accessioned2021-02-15T12:21:22Z
dc.date.available2021-02-15T12:21:22Z
dc.date.issued2020-06-03
dc.description.abstractThe evolution of gene regulatory networks (GRNs) is of great relevance for both evolutionary and synthetic biology. Understanding the relationship between GRN structure and its function can allow us to understand the selective pressures that have shaped a given circuit. This is especially relevant when considering spatio-temporal expression patterns, where GRN models have been shown to be extremely robust and evolvable. However, previous models that studied GRN evolution did not include the evolution of protein and genetic elements that underlie GRN architecture. Here we use toyLIFE, a multilevel genotype-phenotype map, to show that not all GRNs are equally likely in genotype space and that evolution is biased to find the most common GRNs. toyLIFE rules create Boolean GRNs that, embedded in a one-dimensional tissue, develop a variety of spatio-temporal gene expression patterns. Populations of toyLIFE organisms choose the most common GRN out of a set of equally fit alternatives and, most importantly, fail to find a target pattern when it is very rare in genotype space. Indeed, we show that the probability of finding the fittest phenotype increases dramatically with its abundance in genotype space. This phenotypic bias represents a mechanism that can prevent the fixation in the population of the fittest phenotype, one that is inherent to the structure of genotype space and the genotype-phenotype map.en
dc.description.sponsorshipP.C. is supported by a Ramón Areces Postdoctoral Fellowship. This research has been supported by Ministerio de Ciencia, Innovación y Universidades/FEDER (Spain/UE) through grant nos. PGC2018-098186-B-I00 (BASIC) and FIS2017-89773-P (MiMevo)en
dc.description.statusPublicadoes
dc.format.extent9
dc.identifier.bibliographicCitationJournal of the Royal Society Interface, (June 2020), 17(167), pp.: 1-9.en
dc.identifier.doihttps://doi.org/10.1098/rsif.2019.0843
dc.identifier.issn1742-5689
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue167
dc.identifier.publicationlastpage9
dc.identifier.publicationtitleJournal of the Royal Society Interfaceen
dc.identifier.publicationvolume17
dc.identifier.urihttps://hdl.handle.net/10016/31928
dc.identifier.uxxiAR/0000026257
dc.language.isoengen
dc.publisherThe Royal Societyen
dc.relation.projectIDGobierno de España. PGC2018-098186-B-I00/BASICes
dc.relation.projectIDGobierno de España. FIS2017-89773-P/MiMevoes
dc.rights© 2020 The Author(s) Published by the Royal Society. All rights reserved.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaMatemáticases
dc.subject.otherPhenotypic biasen
dc.subject.otherGenetic circuitsen
dc.subject.otherGenotype–phenotype mapen
dc.subject.otherGene regulatory networksen
dc.subject.otherEntropyen
dc.subject.otherToyLIFEen
dc.titlePopulations of genetic circuits are unable to find the fittest solution in a multilevel genotype-phenotype mapen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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