Date of Award
Summer 8-15-2022
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Transcription factors (TFs) are a set of proteins that play a key role in the information processing system that enables a cell to respond to changes in internal and external state. By binding near a gene in a cell’s DNA, a TF can influence that gene’s expression level, triggering the appropriate increase or decrease in production levels of proteins that are needed to handle stressors like a change in nutrient availability or damage to the cell’s internal structures. Transcription factor activity (TFA) is a measure of how much effect a TF has on its target genes in a given sample of cells. TFA depends on several factors including expression of the gene that encodes the TF, the TF’s access to genes, and how much of the TF protein has the modifications needed to activate it. Because there are so many molecular factors influencing TF activity, there is no one assay that can measure TFA directly.In this dissertation, we build on previous work in TFA inference that uses the measurable output of cell signaling pathways – gene expression levels – to infer TFA values and to utilize these inferred values to better understand the roles of individual TFs within gene regulatory systems. First, we applied TFA inference to microarray data on the well-studied Saccharomyces cerevisiae (baker’s yeast) in order to define systematic, objective accuracy metrics. With these metrics, we explore the robustness of TFA inference to changes in the studied organism, the type of data input, and the optimization approach. Finally, we optimize the TFA inference algorithm to study RNA-seq data from a pathogenic yeast, Cryptococcus neoformans, to analyze the signaling pathway involved in its capsule formation response to environmental stress, a major factor of its virulence in humans.
Language
English (en)
Chair
Michael Brent
Committee Members
Richard Bonneau, Jeremy Buhler, Barak Cohen, Roman Garnett,