Doctor of Philosophy in Biology
in the
School of Biological Sciences
Ling Wang
Will defend her dissertation
Evolution of Essential and Ancient Genes: Understanding and Modeling Fitness in Transfer RNAs
10, December 2024
1PM
https://gatech.zoom.us/j/93126822349?pwd=9qbAlkbjXpBEHG2vk7Zo9nuVRG9qcA.1
Thesis Advisor:
Dr. Annalise Paaby, Ph.D.
School of Biological Sciences
Georgia Institute of Technology
Committee Members:
Dr. Francesca Storici, Ph.D.
School of Biological Sciences
Georgia Institute of Technology
Dr. Joseph Lachance, Ph.D.
School of Biological Science
Georgia Institute of Technology
Dr. Matthew Torres, Ph.D.
School of Biological Sciences
Georgia Institute of Technology
Dr. Loren Williams, Ph.D.
School of Chemistry & Biochemistry
Georgia Institute of Technology
Abstract: A central question in evolutionary biology is how genes evolve over time and how mutations to those genes affect fitness. How a mutation affects fitness may depend in part upon mutations at other sites. For example, the negative impact of a bad mutation may be compensated by other mutations that restore or enhance function. Understanding this dynamic is crucial to elucidating not just the evolution of genes, but the evolution of complex traits, including interactions between genes.
This thesis is structured into three related projects that examine the following fundamental questions: 1. How do genes evolve, and what does the compensatory evolution pattern of single-copy genes look like? Specifically, I explored mutations in mitochondrial tRNAs (mt-tRNAs) in Caenorhabditis nematodes to identify patterns of compensatory evolution over short and intermediate evolutionary timescales, including interactions between mt-tRNAs and associated factors encoded in the nuclear genome. 2. To what extent can computational techniques enhance our understanding of the impact of mutations, particularly on overall fitness? In this project, I utilized experimentally derived fitness estimates for thousands of S. cerevisiae allelic variants of a nuclear-encoded arginine tRNA (tRNACCU). I assessed how well computational inferences from sequence information, such as secondary structure prediction and the minimum free energy folding score, explained fitness variation. 3. Can machine learning improve upon computational fitness prediction and address the limitations of existing prediction tools? In this project, I developed an inclusive machine learning model that integrates multiple features to estimate fitness from sequence data. Together, these studies aim to provide a more comprehensive understanding of the dynamics of tRNA evolution by combining advanced computational techniques with concepts of evolutionary biology.
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