Numerous factors can influence the evolutionary fate of mutations. Despite this,
we tend to study strong evolutionary drivers, or evolution under simple contexts, in
part because they are the conditions we have a means to study. My thesis evaluates
novel computational approaches to advance detection, and study, of factors that
influence a mutation’s evolutionary outcome. First, I present the novel
computational tool AEGIS that I use to detect phylogenetic signals of correlated
evolution followed by an experimental approach to evaluate the role of epistasis as a
potential cause of correlated evolution among sites associated with antibiotic
resistance in Pseudomonas aeruginosa. Second, I developed rSHAPE, a novel in
silico approach for experimental evolution with asexual haploids, to complement
empirical work by providing a common framework in which to test various
evolutionary scenarios. After demonstrating that rSHAPE replicates the expected
evolutionary dynamics of de novo mutations, I provide evidence that the common
laboratory practice of serial passaging may increase stochasticity of evolutionary
outcome. Through my work, I have demonstrated that a marriage of computational
and experimental approaches will offer new opportunities to understand how the
interaction of evolutionary factors influence the fate of mutations.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/40415 |
| Date | 23 April 2020 |
| Creators | Dench, Jonathan |
| Contributors | Aris-Brosou, Stéphane, Kassen, Rees |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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