| Summary: | My Ph.D. research has focused on some general properties of compensatory mutations, as well as the impact of compensatory mutations on fitness recovery and deleterious mutations on populations extinction risks. I have addressed these topics using a variety of techniques.
Chapter 2 addresses mutational meltdown using computer simulation models to explicitly incorporate both environmental stochasticity and mutation accumulation. The results show that a small amount of environmental stochasticity can significantly hasten extinction times and that the mutational meltdown process hastens time to extinction even when levels of environmental stochasticity are high enough to cause rapid extinctions on their own. Even large populations with 1000 individuals can be at risk of going extinct via the mutational meltdown with sufficient environmental stochasticity.
Chapter 3 looks at the potential for low fitness lines of Caenorhabditis elegans to recover lost fitness due to gene knockouts. Using gene knockout assures that any fitness recovery is due to compensatory mutations elsewhere in the genome and not back mutation. We show that rapid fitness recovery is possible, even in relatively small populations.
Chapter 4 examines the distribution of the number of compensatory mutations that exist per deleterious mutation, using published datasets. We determined that the distribution of number of compensatory mutations is best fit by a gamma distribution, with a mean of 11 compensatory mutations per deleterious mutation.
Chapter 5 utilizes the same dataset as was gathered for Chapter 4, but addresses a different set of questions. Are all amino acid positions equally capable of producing a compensatory mutation? Do compensatory mutations significantly cluster around the site of their associated deleterious mutations? Above and beyond the clustering found in the second question, do compensatory mutations cluster amongst themselves? All of these questions were answered in the affirmative.
Chapter 6 is concerned with the evolution of cooperation through resource sharing. Here we showed that the evolution of cooperation through resource sharing is difficult to achieve and requires heterogeneity in resource state within the population and that an individual’s resource state must change frequently in order to create the conditions by which the evolution of cooperation is favoured.
|
|---|
