Characterizing the genetic basis, fitness effects, and predictability of adaptive evolution
Abstract/Contents
- Abstract
- The idea that species can change in response to their fitness effects was famously proposed by Charles Darwin more than a century ago and merged with our burgeoning understanding of genetics as the "modern synthesis" in the early 20th century. The relationship between genotype and fitness has been studied extensively in the decades since, with the goal of not only understanding evolutionary history, but to predict future evolution. Evolutionary prediction is critical for solving problems of biomedical interest such as drug resistance evolution in pests and pathogens or the evolution of cancers. Major questions in the field include understanding the distribution of selective effects of new mutations, the number and types of loci that are targets of adaptive mutations, and the dependency of the fitness effect of a mutation on its genetic background or the environment. In the first chapter of this thesis, I present a theoretical treatment of the predictability of evolution, by studying predictability via historical reconstruction as well as more traditional methods looking at the similarity of independently evolving populations starting from the same initial condition. I relax the assumption made in this prior work that adaptation always proceed through the successive fixation of adaptive mutations, and characterize the changes in evolutionary predictability that result. I find that the two approaches to studying predictability are not identical, and can even be anti-correlated. Remarkably, I find that stable polymorphisms can make the historical reconstruction method unreliable even with perfect sampling, which would never occur under the successive fixation assumption. In order to predict evolution in biological systems, we need a comprehensive genome-wide survey of all possible adaptive mutations in a system and their fitness effects, not just the ones that occurred during a single bout of evolution. In the next chapter, I present a novel and highly extensible approach to overcome the current technical limitations in conducting such a survey in experimentally evolving yeast populations based on recently developed the DNA barcoding technology. In the final chapter, I use the methods developed in the second chapter to study how the genotype-fitness map changes under different growth conditions by remeasuring the fitness of the same clones under many alternative conditions. These conditions were chosen to specifically vary different parts of the yeast growth cycle, such as the exponential growth phase or stationary phase, giving us additional insight into the physiological basis of adaptation in this system. In sum, the work presented in this thesis studies the predictability of evolution using theoretical models and develops the experimental tools necessary to empirically study evolutionary predictability by comprehensively characterizing the genotype-fitness map in experimentally evolving yeast.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Venkataram, Sandeep | |
|---|---|---|
| Associated with | Stanford University, Department of Biology. | |
| Primary advisor | Petrov, Dmitri Alex, 1969- | |
| Thesis advisor | Petrov, Dmitri Alex, 1969- | |
| Thesis advisor | Fraser, Hunter B | |
| Thesis advisor | Sherlock, Gavin | |
| Advisor | Fraser, Hunter B | |
| Advisor | Sherlock, Gavin |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sandeep Venkataram. |
|---|---|
| Note | Submitted to the Department of Biology. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Sandeep Venkataram
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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