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dc.contributor.authorOgbunugafor, C. Brandon
dc.contributor.authorHartl, Daniel
dc.date.accessioned2017-04-21T18:10:11Z
dc.date.available2017-04-21T18:10:11Z
dc.date.issued2016-10
dc.identifier.issn1553-7358
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/1721.1/108354
dc.description.abstractMuch of the public lacks a proper understanding of Darwinian evolution, a problem that can be addressed with new learning and teaching approaches to be implemented both inside the classroom and in less formal settings. Few analogies have been as successful in communicating the basics of molecular evolution as John Maynard Smith’s protein space analogy (1970), in which he compared protein evolution to the transition between the terms WORD and GENE, changing one letter at a time to yield a different, meaningful word (in his example, the preferred path was WORD → WORE → GORE → GONE → GENE). Using freely available computer science tools (Google Books Ngram Viewer), we offer an update to Maynard Smith’s analogy and explain how it might be developed into an exploratory and pedagogical device for understanding the basics of molecular evolution and, more specifically, the adaptive landscape concept. We explain how the device works through several examples and provide resources that might facilitate its use in multiple settings, ranging from public engagement activities to formal instruction in evolution, population genetics, and computational biology.en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pcbi.1005046en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleA New Take on John Maynard Smith's Concept of Protein Space for Understanding Molecular Evolutionen_US
dc.typeArticleen_US
dc.identifier.citationOgbunugafor, C. Brandon, and Hartl, Daniel L. “A New Take on John Maynard Smith’s Concept of Protein Space for Understanding Molecular Evolution.” Ed. Francis Ouellette. PLOS Computational Biology 12.10 (2016): e1005046. © 2016 Ogbunugafor and Hartlen_US
dc.contributor.departmentBroad Institute of MIT and Harvarden_US
dc.contributor.mitauthorHartl, Daniel
dc.relation.journalPLOS Computational Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsOgbunugafor, C. Brandon; Hartl, Daniel L.en_US
dspace.embargo.termsNen_US
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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