Improved models of biological sequence evolution

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dc.contributor.advisorScheffler, Konraden_ZA
dc.contributor.authorMurrel, Benjaminen_ZA
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Mathematical Sciences.en_ZA
dc.date.accessioned2012-10-11T05:36:23Zen_ZA
dc.date.accessioned2012-12-12T08:15:45Z
dc.date.available2012-10-11T05:36:23Zen_ZA
dc.date.available2012-12-12T08:15:45Z
dc.date.issued2012-12en_ZA
dc.descriptionThesis (PhD)--Stellenbosch University, 2012.en_ZA
dc.description.abstractENGLISH ABSTRACT: Computational molecular evolution is a field that attempts to characterize how genetic sequences evolve over phylogenetic trees – the branching processes that describe the patterns of genetic inheritance in living organisms. It has a long history of developing progressively more sophisticated stochastic models of evolution. Through a probabilist’s lens, this can be seen as a search for more appropriate ways to parameterize discrete state continuous time Markov chains to better encode biological reality, matching the historical processes that created empirical data sets, and creating useful tools that allow biologists to test specific hypotheses about the evolution of the organisms or the genes that interest them. This dissertation is an attempt to fill some of the gaps that persist in the literature, solving what we see as existing open problems. The overarching theme of this work is how to better model variation in the action of natural selection at multiple levels: across genes, between sites, and over time. Through four published journal articles and a fifth in preparation, we present amino acid and codon models that improve upon existing approaches, providing better descriptions of the process of natural selection and better tools to detect adaptive evolution.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Komputasionele molekulêre evolusie is ’n navorsingsarea wat poog om die evolusie van genetiese sekwensies oor filogenetiese bome – die vertakkende prosesse wat die patrone van genetiese oorerwing in lewende organismes beskryf – te karakteriseer. Dit het ’n lang geskiedenis waartydens al hoe meer gesofistikeerde waarskynlikheidsmodelle van evolusie ontwikkel is. Deur die lens van waarskynlikheidsleer kan hierdie proses gesien word as ’n soektog na meer gepasde metodes om diskrete-toestand kontinuë-tyd Markov kettings te parametriseer ten einde biologiese realiteit beter te enkodeer – op so ’n manier dat die historiese prosesse wat tot die vorming van biologiese sekwensies gelei het nageboots word, en dat nuttige metodes geskep word wat bioloë toelaat om spesifieke hipotesisse met betrekking tot die evolusie van belanghebbende organismes of gene te toets. Hierdie proefskrif is ’n poging om sommige van die gapings wat in die literatuur bestaan in te vul en bestaande oop probleme op te los. Die oorkoepelende tema is verbeterde modellering van variasie in die werking van natuurlike seleksie op verskeie vlakke: variasie van geen tot geen, variasie tussen posisies in gene en variasie oor tyd. Deur middel van vier gepubliseerde joernaalartikels en ’n vyfde artikel in voorbereiding, bied ons aminosuur- en kodon-modelle aan wat verbeter op bestaande benaderings – hierdie modelle verskaf beter beskrywings van die proses van natuurlike seleksie sowel as beter metodes om gevalle van aanpassing in evolusie te vind.af_ZA
dc.format.extent1 v. (various pagings)
dc.identifier.urihttp://hdl.handle.net/10019.1/71870
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectMolecular evolutionen_ZA
dc.subjectPhylogenetic models -- Mathematical modelsen_ZA
dc.subjectNatural selectionen_ZA
dc.subjectMarkov processesen_ZA
dc.subjectMolecular evolution -- Data processingen_ZA
dc.subjectDissertations -- Mathematical sciencesen_ZA
dc.subjectTheses -- Mathematical sciencesen_ZA
dc.subjectDissertations -- Computer scienceen_ZA
dc.subjectTheses -- Computer scienceen_ZA
dc.titleImproved models of biological sequence evolutionen_ZA
dc.typeThesisen_ZA
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