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Evolution on Arbitrary Fitness Landscapes when Mutation is Weak

dc.contributor.advisor Magwene, Paul M
dc.contributor.advisor McShea, Daniel W
dc.contributor.author McCandlish, David Martin
dc.date.accessioned 2012-05-25T20:18:32Z
dc.date.available 2013-05-20T04:30:05Z
dc.date.issued 2012
dc.identifier.uri https://hdl.handle.net/10161/5538
dc.description.abstract <p>Evolutionary dynamics can be notoriously complex and difficult to analyze. In this dissertation I describe a population genetic regime where the dynamics are simple enough to allow a relatively complete and elegant treatment. Consider a haploid, asexual population, where each possible genotype has been assigned a fitness. When mutations enter a population sufficiently rarely, we can model the evolution of this population as a Markov chain where the population jumps from one genotype to another at the birth of each new mutant destined for fixation. Furthermore, if the mutation rates are assigned in such a manner that the Markov chain is reversible when all genotypes are assigned the same fitness, then it is still reversible when genotypes are assigned differing fitnesses. </p><p>The key insight is that this Markov chain can be analyzed using the spectral theory of finite-state, reversible Markov chains. I describe the spectral decomposition of the transition matrix and use it to build a general framework with which I address a variety of both classical and novel topics. These topics include a method for creating low-dimensional visualizations of fitness landscapes; a measure of how easy it is for the evolutionary process to `find' a specific genotype or phenotype; the index of dispersion of the molecular clock and its generalizations; a definition for the neighborhood of a genotype based on evolutionary dynamics; and the expected fitness and number of substitutions that have occurred given that a population has been evolving on the fitness landscape for a given period of time. I apply these various analyses to both a simple one-codon fitness landscape and to a large neutral network derived from computational RNA secondary structure predictions.</p>
dc.subject Evolution & development
dc.subject Genetics
dc.subject Fitness landscape
dc.subject Neutral network
dc.subject Random walk
dc.subject Reversible Markov chain
dc.subject Spectral graph theory
dc.subject Weak mutation
dc.title Evolution on Arbitrary Fitness Landscapes when Mutation is Weak
dc.type Dissertation
dc.department Biology
duke.embargo.months 12


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