Invasion and effective size of graph-structured populations
Population structure can strongly affect evolutionary dynamics. The most general way to describe population structures are graphs. An important observable on evolutionary graphs is the probability that a novel mutation spreads through the entire population. But what drives this spread of a mutation...
Gespeichert in:
| Veröffentlicht in: | PLoS computational biology 2018-11, Vol.14 (11), p.e1006559-e1006559 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e1006559 |
|---|---|
| container_issue | 11 |
| container_start_page | e1006559 |
| container_title | PLoS computational biology |
| container_volume | 14 |
| creator | Giaimo, Stefano Arranz, Jordi Traulsen, Arne |
| description | Population structure can strongly affect evolutionary dynamics. The most general way to describe population structures are graphs. An important observable on evolutionary graphs is the probability that a novel mutation spreads through the entire population. But what drives this spread of a mutation towards fixation? Here, we propose a novel way to understand the forces driving fixation by borrowing techniques from evolutionary demography to quantify the invasion fitness and the effective population size for different graphs. Our method is very general and even applies to weighted graphs with node dependent fitness. However, we focus on analytical results for undirected graphs with node independent fitness. The method will allow to conceptually integrate evolutionary graph theory with theoretical genetics of structured populations. |
| doi_str_mv | 10.1371/journal.pcbi.1006559 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2250635297</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A564080820</galeid><doaj_id>oai_doaj_org_article_48b4aa11a0484f0b9a79c2b3fd2d3e1e</doaj_id><sourcerecordid>A564080820</sourcerecordid><originalsourceid>FETCH-LOGICAL-c633t-575803bebe7130c27f60edd7d26d65dc0bcb8f561c2c8a983963ccd22e82446c3</originalsourceid><addsrcrecordid>eNqVkltrFDEUxwdRbK1-A9EBX-rDrLlMLoMglOJloSh4eQ6Z5GSaZXYyTWYW66c3daelK75IHnJIfv__yTk5RfEcoxWmAr_ZhDkOul-NpvUrjBBnrHlQHGPGaCUokw_vxUfFk5Q2COWw4Y-LI4pq3CAsjou362Gnkw9DqQdbgnNgJr-DMvlfUAZXdlGPl1Wa4mymOYItxzDOvZ6yIj0tHjndJ3i27CfFjw_vv59_qi6-fFyfn11UhlM6VUwwiWgLLQhMkSHCcQTWCku45cwa1JpWOsaxIUbqRtKGU2MsISBJXXNDT4qXe9-xD0ktdSdFCEOcMtKITKz3hA16o8botzpeq6C9-nMQYqd0nLzpQdWyrbXGWKNa1g61jRaNIS11llgKGLLXuyXb3G7BGhimqPsD08ObwV-qLuwUJ0zmj8kGp4tBDFczpEltfTLQ93qAMOd3Y0oEbRpRZ_TVX-i_q1vtqU7nAvzgQs5r8rKw9SYM4Hw-P2O8RhJJgrLg9YEgMxP8nDo9p6TW377-B_v5kK33rIkhpQjurisYqZuhvH2-uhlKtQxllr2439E70e0U0t84cN29</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2250635297</pqid></control><display><type>article</type><title>Invasion and effective size of graph-structured populations</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS)</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Giaimo, Stefano ; Arranz, Jordi ; Traulsen, Arne</creator><contributor>Schreiber, Sebastian</contributor><creatorcontrib>Giaimo, Stefano ; Arranz, Jordi ; Traulsen, Arne ; Schreiber, Sebastian</creatorcontrib><description>Population structure can strongly affect evolutionary dynamics. The most general way to describe population structures are graphs. An important observable on evolutionary graphs is the probability that a novel mutation spreads through the entire population. But what drives this spread of a mutation towards fixation? Here, we propose a novel way to understand the forces driving fixation by borrowing techniques from evolutionary demography to quantify the invasion fitness and the effective population size for different graphs. Our method is very general and even applies to weighted graphs with node dependent fitness. However, we focus on analytical results for undirected graphs with node independent fitness. The method will allow to conceptually integrate evolutionary graph theory with theoretical genetics of structured populations.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1006559</identifier><identifier>PMID: 30419017</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Biological Evolution ; Biology and Life Sciences ; Computer and Information Sciences ; Computer Simulation ; Demography ; Evolution ; Evolution (Biology) ; Evolutionary biology ; Fitness ; Fixation ; Game theory ; Genetics ; Graph theory ; Graphic methods ; Graphs ; Models, Biological ; Mutation ; Physical Sciences ; Population ; Population Dynamics ; Population genetics ; Population number ; Population research ; Population structure ; Populations ; Probability ; Reproductive fitness ; Society ; Theoretical genetics</subject><ispartof>PLoS computational biology, 2018-11, Vol.14 (11), p.e1006559-e1006559</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Giaimo 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>2018 Giaimo et al 2018 Giaimo et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c633t-575803bebe7130c27f60edd7d26d65dc0bcb8f561c2c8a983963ccd22e82446c3</citedby><cites>FETCH-LOGICAL-c633t-575803bebe7130c27f60edd7d26d65dc0bcb8f561c2c8a983963ccd22e82446c3</cites><orcidid>0000-0003-0421-3065 ; 0000-0002-0669-5267</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/PMC6258371/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258371/$$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/30419017$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Schreiber, Sebastian</contributor><creatorcontrib>Giaimo, Stefano</creatorcontrib><creatorcontrib>Arranz, Jordi</creatorcontrib><creatorcontrib>Traulsen, Arne</creatorcontrib><title>Invasion and effective size of graph-structured populations</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Population structure can strongly affect evolutionary dynamics. The most general way to describe population structures are graphs. An important observable on evolutionary graphs is the probability that a novel mutation spreads through the entire population. But what drives this spread of a mutation towards fixation? Here, we propose a novel way to understand the forces driving fixation by borrowing techniques from evolutionary demography to quantify the invasion fitness and the effective population size for different graphs. Our method is very general and even applies to weighted graphs with node dependent fitness. However, we focus on analytical results for undirected graphs with node independent fitness. The method will allow to conceptually integrate evolutionary graph theory with theoretical genetics of structured populations.</description><subject>Age</subject><subject>Biological Evolution</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Computer Simulation</subject><subject>Demography</subject><subject>Evolution</subject><subject>Evolution (Biology)</subject><subject>Evolutionary biology</subject><subject>Fitness</subject><subject>Fixation</subject><subject>Game theory</subject><subject>Genetics</subject><subject>Graph theory</subject><subject>Graphic methods</subject><subject>Graphs</subject><subject>Models, Biological</subject><subject>Mutation</subject><subject>Physical Sciences</subject><subject>Population</subject><subject>Population Dynamics</subject><subject>Population genetics</subject><subject>Population number</subject><subject>Population research</subject><subject>Population structure</subject><subject>Populations</subject><subject>Probability</subject><subject>Reproductive fitness</subject><subject>Society</subject><subject>Theoretical genetics</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVkltrFDEUxwdRbK1-A9EBX-rDrLlMLoMglOJloSh4eQ6Z5GSaZXYyTWYW66c3daelK75IHnJIfv__yTk5RfEcoxWmAr_ZhDkOul-NpvUrjBBnrHlQHGPGaCUokw_vxUfFk5Q2COWw4Y-LI4pq3CAsjou362Gnkw9DqQdbgnNgJr-DMvlfUAZXdlGPl1Wa4mymOYItxzDOvZ6yIj0tHjndJ3i27CfFjw_vv59_qi6-fFyfn11UhlM6VUwwiWgLLQhMkSHCcQTWCku45cwa1JpWOsaxIUbqRtKGU2MsISBJXXNDT4qXe9-xD0ktdSdFCEOcMtKITKz3hA16o8botzpeq6C9-nMQYqd0nLzpQdWyrbXGWKNa1g61jRaNIS11llgKGLLXuyXb3G7BGhimqPsD08ObwV-qLuwUJ0zmj8kGp4tBDFczpEltfTLQ93qAMOd3Y0oEbRpRZ_TVX-i_q1vtqU7nAvzgQs5r8rKw9SYM4Hw-P2O8RhJJgrLg9YEgMxP8nDo9p6TW377-B_v5kK33rIkhpQjurisYqZuhvH2-uhlKtQxllr2439E70e0U0t84cN29</recordid><startdate>20181101</startdate><enddate>20181101</enddate><creator>Giaimo, Stefano</creator><creator>Arranz, Jordi</creator><creator>Traulsen, Arne</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0421-3065</orcidid><orcidid>https://orcid.org/0000-0002-0669-5267</orcidid></search><sort><creationdate>20181101</creationdate><title>Invasion and effective size of graph-structured populations</title><author>Giaimo, Stefano ; Arranz, Jordi ; Traulsen, Arne</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c633t-575803bebe7130c27f60edd7d26d65dc0bcb8f561c2c8a983963ccd22e82446c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Age</topic><topic>Biological Evolution</topic><topic>Biology and Life Sciences</topic><topic>Computer and Information Sciences</topic><topic>Computer Simulation</topic><topic>Demography</topic><topic>Evolution</topic><topic>Evolution (Biology)</topic><topic>Evolutionary biology</topic><topic>Fitness</topic><topic>Fixation</topic><topic>Game theory</topic><topic>Genetics</topic><topic>Graph theory</topic><topic>Graphic methods</topic><topic>Graphs</topic><topic>Models, Biological</topic><topic>Mutation</topic><topic>Physical Sciences</topic><topic>Population</topic><topic>Population Dynamics</topic><topic>Population genetics</topic><topic>Population number</topic><topic>Population research</topic><topic>Population structure</topic><topic>Populations</topic><topic>Probability</topic><topic>Reproductive fitness</topic><topic>Society</topic><topic>Theoretical genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Giaimo, Stefano</creatorcontrib><creatorcontrib>Arranz, Jordi</creatorcontrib><creatorcontrib>Traulsen, Arne</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Giaimo, Stefano</au><au>Arranz, Jordi</au><au>Traulsen, Arne</au><au>Schreiber, Sebastian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Invasion and effective size of graph-structured populations</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2018-11-01</date><risdate>2018</risdate><volume>14</volume><issue>11</issue><spage>e1006559</spage><epage>e1006559</epage><pages>e1006559-e1006559</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Population structure can strongly affect evolutionary dynamics. The most general way to describe population structures are graphs. An important observable on evolutionary graphs is the probability that a novel mutation spreads through the entire population. But what drives this spread of a mutation towards fixation? Here, we propose a novel way to understand the forces driving fixation by borrowing techniques from evolutionary demography to quantify the invasion fitness and the effective population size for different graphs. Our method is very general and even applies to weighted graphs with node dependent fitness. However, we focus on analytical results for undirected graphs with node independent fitness. The method will allow to conceptually integrate evolutionary graph theory with theoretical genetics of structured populations.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30419017</pmid><doi>10.1371/journal.pcbi.1006559</doi><orcidid>https://orcid.org/0000-0003-0421-3065</orcidid><orcidid>https://orcid.org/0000-0002-0669-5267</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1553-7358 |
| ispartof | PLoS computational biology, 2018-11, Vol.14 (11), p.e1006559-e1006559 |
| issn | 1553-7358 1553-734X 1553-7358 |
| language | eng |
| recordid | cdi_plos_journals_2250635297 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central |
| subjects | Age Biological Evolution Biology and Life Sciences Computer and Information Sciences Computer Simulation Demography Evolution Evolution (Biology) Evolutionary biology Fitness Fixation Game theory Genetics Graph theory Graphic methods Graphs Models, Biological Mutation Physical Sciences Population Population Dynamics Population genetics Population number Population research Population structure Populations Probability Reproductive fitness Society Theoretical genetics |
| title | Invasion and effective size of graph-structured populations |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T05%3A11%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Invasion%20and%20effective%20size%20of%20graph-structured%20populations&rft.jtitle=PLoS%20computational%20biology&rft.au=Giaimo,%20Stefano&rft.date=2018-11-01&rft.volume=14&rft.issue=11&rft.spage=e1006559&rft.epage=e1006559&rft.pages=e1006559-e1006559&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1006559&rft_dat=%3Cgale_plos_%3EA564080820%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2250635297&rft_id=info:pmid/30419017&rft_galeid=A564080820&rft_doaj_id=oai_doaj_org_article_48b4aa11a0484f0b9a79c2b3fd2d3e1e&rfr_iscdi=true |