{"article_processing_charge":"No","month":"11","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2023-08-22T12:49:18Z","file":[{"date_updated":"2020-11-18T07:26:10Z","date_created":"2020-11-18T07:26:10Z","file_size":2498594,"file_id":"8768","success":1,"relation":"main_file","checksum":"555456dd0e47bcf9e0994bcb95577e88","file_name":"2020_PlosCompBio_Kaveh.pdf","access_level":"open_access","creator":"dernst","content_type":"application/pdf"}],"type":"journal_article","day":"05","department":[{"_id":"KrCh"}],"abstract":[{"lang":"eng","text":"Resources are rarely distributed uniformly within a population. Heterogeneity in the concentration of a drug, the quality of breeding sites, or wealth can all affect evolutionary dynamics. In this study, we represent a collection of properties affecting the fitness at a given location using a color. A green node is rich in resources while a red node is poorer. More colors can represent a broader spectrum of resource qualities. For a population evolving according to the birth-death Moran model, the first question we address is which structures, identified by graph connectivity and graph coloring, are evolutionarily equivalent. We prove that all properly two-colored, undirected, regular graphs are evolutionarily equivalent (where “properly colored” means that no two neighbors have the same color). We then compare the effects of background heterogeneity on properly two-colored graphs to those with alternative schemes in which the colors are permuted. Finally, we discuss dynamic coloring as a model for spatiotemporal resource fluctuations, and we illustrate that random dynamic colorings often diminish the effects of background heterogeneity relative to a proper two-coloring."}],"publication_status":"published","date_published":"2020-11-05T00:00:00Z","issue":"11","publisher":"Public Library of Science","scopus_import":"1","oa":1,"oa_version":"Published Version","date_created":"2020-11-18T07:20:23Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","title":"The Moran process on 2-chromatic graphs","quality_controlled":"1","status":"public","external_id":{"isi":["000591317200004"]},"publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"doi":"10.1371/journal.pcbi.1008402","publication":"PLOS Computational Biology","has_accepted_license":"1","author":[{"first_name":"Kamran","last_name":"Kaveh","full_name":"Kaveh, Kamran"},{"full_name":"McAvoy, Alex","last_name":"McAvoy","first_name":"Alex"},{"id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu","last_name":"Chatterjee","first_name":"Krishnendu","orcid":"0000-0002-4561-241X"},{"last_name":"Nowak","first_name":"Martin A.","full_name":"Nowak, Martin A."}],"language":[{"iso":"eng"}],"article_number":"e1008402","_id":"8767","article_type":"original","citation":{"short":"K. Kaveh, A. McAvoy, K. Chatterjee, M.A. Nowak, PLOS Computational Biology 16 (2020).","chicago":"Kaveh, Kamran, Alex McAvoy, Krishnendu Chatterjee, and Martin A. Nowak. “The Moran Process on 2-Chromatic Graphs.” PLOS Computational Biology. Public Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1008402.","ista":"Kaveh K, McAvoy A, Chatterjee K, Nowak MA. 2020. The Moran process on 2-chromatic graphs. PLOS Computational Biology. 16(11), e1008402.","apa":"Kaveh, K., McAvoy, A., Chatterjee, K., & Nowak, M. A. (2020). The Moran process on 2-chromatic graphs. PLOS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1008402","ama":"Kaveh K, McAvoy A, Chatterjee K, Nowak MA. The Moran process on 2-chromatic graphs. PLOS Computational Biology. 2020;16(11). doi:10.1371/journal.pcbi.1008402","ieee":"K. Kaveh, A. McAvoy, K. Chatterjee, and M. A. Nowak, “The Moran process on 2-chromatic graphs,” PLOS Computational Biology, vol. 16, no. 11. Public Library of Science, 2020.","mla":"Kaveh, Kamran, et al. “The Moran Process on 2-Chromatic Graphs.” PLOS Computational Biology, vol. 16, no. 11, e1008402, Public Library of Science, 2020, doi:10.1371/journal.pcbi.1008402."},"year":"2020","intvolume":" 16","volume":16,"ddc":["000"],"keyword":["Ecology","Modelling and Simulation","Computational Theory and Mathematics","Genetics","Ecology","Evolution","Behavior and Systematics","Molecular Biology","Cellular and Molecular Neuroscience"],"file_date_updated":"2020-11-18T07:26:10Z","isi":1,"acknowledgement":"We thank Igor Erovenko for many helpful comments on an earlier version of this paper. : Army Research Laboratory (grant W911NF-18-2-0265) (M.A.N.); the Bill & Melinda Gates Foundation (grant OPP1148627) (M.A.N.); the NVIDIA Corporation (A.M.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."}