Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01sj139471c
 Title: Post-transcriptional regulation of protein abundance and function Authors: Goya, Jonathan Kenshin Advisors: Troyanskaya, Olga G Contributors: Quantitative Computational Biology Department Keywords: Gene function predictionProteomicsStable isotope labelingTissue specificity Subjects: BioinformaticsAnalytical chemistry Issue Date: 2018 Publisher: Princeton, NJ : Princeton University Abstract: Animals, plants, and other multicellular organisms all begin life with a static genome in a single cell and grow to a diverse array of cell types, each providing unique functions to the organism and maintaining a stable physiology through generation after generation of cell division. Single-cell organisms also adapt their physiology in response to the environment by regulating the abundance and function of proteins. Understanding the system of molecular functions and interactions that create and maintain cellular physiology would provide us with new tools for investigating development and treating disease, yet we are limited in our ability to directly observe the state of, and interactions between, the thousands of different proteins in each cell. This dissertation develops several tools to observe and infer regulation of protein abundance and function across many conditions, with an emphasis on the regulation that occurs after a gene has been transcribed into RNA. In Chapter 2, I describe and implement a novel framework for quantifying peptide abundances in label-free and stable isotope labeled experiments. In Chapter 3, I describe and implement a probabilistic model of peptide coelution to locate precursor signal of peptide abundance in tandem mass spectrometry experiments with limited fragmentation depth and quality. In Chapter 4, I applied the approaches described in Chapters 2 and 3 to a stable isotope labeling experiment in which isolated nuclei were pulsed with terminally $^{18}$O-labeled ATP to measure the rate of nuclear protein phosphorylation. This work shows how the approaches to peptide abundance and location discovery reduce missing values in proteomic experiments with many samples, thus enabling proteome-scale analyses of timecourse data. In Chapter 5, I created tissue-specific predictions of protein functional relationship in mice. Using known protein interactions, patterns of protein expression across mouse tissues, and thousands of mouse microarray experiments, I predicted the probability of a functional relationship between every pair of genes in the mouse genome, in each of 200 mouse tissues. Finally, in Chapter 6, I describe preliminary work towards understanding condition-specific regulation of protein abundance and function in the contexts of mouse tissues and yeast growing under various nutrient limitations. URI: http://arks.princeton.edu/ark:/88435/dsp01sj139471c Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu Type of Material: Academic dissertations (Ph.D.) Language: en Appears in Collections: Quantitative Computational Biology