Phenotypes to remember: Evolutionary developmental memory capacity and robustness
There is increased awareness of the possibility of developmental memories resulting from evolutionary learning. Genetic regulatory and neural networks can be modelled by analogous formalism raising the important question of productive analogies in principles, processes and performance. We investigat...
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description | There is increased awareness of the possibility of developmental memories resulting from evolutionary learning. Genetic regulatory and neural networks can be modelled by analogous formalism raising the important question of productive analogies in principles, processes and performance. We investigate the formation and persistence of various developmental memories of past phenotypes asking how the number of remembered past phenotypes scales with network size, to what extent memories stored form by Hebbian-like rules, and how robust these developmental "devo-engrams" are against networks perturbations (graceful degradation). The analogy between neural and genetic regulatory networks is not superficial in that it allows knowledge transfer between fields that used to be developed separately from each other. Known examples of spectacular phenotypic radiations could partly be accounted for in such terms. |
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Genetic regulatory and neural networks can be modelled by analogous formalism raising the important question of productive analogies in principles, processes and performance. We investigate the formation and persistence of various developmental memories of past phenotypes asking how the number of remembered past phenotypes scales with network size, to what extent memories stored form by Hebbian-like rules, and how robust these developmental "devo-engrams" are against networks perturbations (graceful degradation). The analogy between neural and genetic regulatory networks is not superficial in that it allows knowledge transfer between fields that used to be developed separately from each other. Known examples of spectacular phenotypic radiations could partly be accounted for in such terms.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1008425</identifier><identifier>PMID: 33253184</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adaptation ; Analysis ; Biological Evolution ; Biology and Life Sciences ; Cichlids ; Computer and Information Sciences ; Developmental biology ; Developmental stages ; Divergence ; Ecological niches ; Embryos ; Gene expression ; Gene Expression Regulation, Developmental ; Gene Regulatory Networks ; Genetic aspects ; Genotype & phenotype ; Genotypes ; Humans ; Lakes ; Memory ; Morphology ; Mutation ; Natural history ; Natural selection ; Neural circuitry ; Neural networks ; Niches ; Ontogeny ; Phenotype ; Phenotypes ; Population ; Radiation ; Technology application</subject><ispartof>PLoS computational biology, 2020-11, Vol.16 (11), p.e1008425-e1008425</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Szilágyi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Szilágyi et al 2020 Szilágyi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c633t-ff43464773d97f1bb58950b28cc5abc37f8e5c83a6dda139262126d1234eaf423</citedby><cites>FETCH-LOGICAL-c633t-ff43464773d97f1bb58950b28cc5abc37f8e5c83a6dda139262126d1234eaf423</cites><orcidid>0000-0002-6894-4652 ; 0000-0002-6478-6570 ; 0000-0001-5227-2997 ; 0000-0002-0521-3094</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703877/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703877/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33253184$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Newman, Stuart A.</contributor><creatorcontrib>Szilágyi, András</creatorcontrib><creatorcontrib>Szabó, Péter</creatorcontrib><creatorcontrib>Santos, Mauro</creatorcontrib><creatorcontrib>Szathmáry, Eörs</creatorcontrib><title>Phenotypes to remember: Evolutionary developmental memory capacity and robustness</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>There is increased awareness of the possibility of developmental memories resulting from evolutionary learning. Genetic regulatory and neural networks can be modelled by analogous formalism raising the important question of productive analogies in principles, processes and performance. We investigate the formation and persistence of various developmental memories of past phenotypes asking how the number of remembered past phenotypes scales with network size, to what extent memories stored form by Hebbian-like rules, and how robust these developmental "devo-engrams" are against networks perturbations (graceful degradation). The analogy between neural and genetic regulatory networks is not superficial in that it allows knowledge transfer between fields that used to be developed separately from each other. Known examples of spectacular phenotypic radiations could partly be accounted for in such terms.</description><subject>Adaptation</subject><subject>Analysis</subject><subject>Biological Evolution</subject><subject>Biology and Life Sciences</subject><subject>Cichlids</subject><subject>Computer and Information Sciences</subject><subject>Developmental biology</subject><subject>Developmental stages</subject><subject>Divergence</subject><subject>Ecological niches</subject><subject>Embryos</subject><subject>Gene expression</subject><subject>Gene Expression Regulation, Developmental</subject><subject>Gene Regulatory Networks</subject><subject>Genetic aspects</subject><subject>Genotype & phenotype</subject><subject>Genotypes</subject><subject>Humans</subject><subject>Lakes</subject><subject>Memory</subject><subject>Morphology</subject><subject>Mutation</subject><subject>Natural history</subject><subject>Natural selection</subject><subject>Neural circuitry</subject><subject>Neural networks</subject><subject>Niches</subject><subject>Ontogeny</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Population</subject><subject>Radiation</subject><subject>Technology 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subjects | Adaptation Analysis Biological Evolution Biology and Life Sciences Cichlids Computer and Information Sciences Developmental biology Developmental stages Divergence Ecological niches Embryos Gene expression Gene Expression Regulation, Developmental Gene Regulatory Networks Genetic aspects Genotype & phenotype Genotypes Humans Lakes Memory Morphology Mutation Natural history Natural selection Neural circuitry Neural networks Niches Ontogeny Phenotype Phenotypes Population Radiation Technology application |
title | Phenotypes to remember: Evolutionary developmental memory capacity and robustness |
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