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    Systematic investigation of protein-metabolite regulatory interactions: methodologies and context

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    SMITH-DISSERTATION-2016.pdf (1.649Mb)
    Date
    2016-11-15
    Author
    Smith, McKenzie L.
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    Abstract
    A systems-level understanding of metabolism will have far-reaching benefits from medicine to ecology to industry, as it will facilitate the comprehensive profiling and prediction of metabolic states in organisms of interest. Systematic characterization strategies have thus far been successfully applied at the genomic, transcriptomic, and proteomic levels, but downstream regulatory interactions have remained comparatively underexplored. We believe this is largely due to the wide sensitivity spectrum required to effect a similarly comprehensive study: the binding affinities of known protein-metabolite regulatory pairs span multiple orders of magnitude. The overarching aim of this work was therefore to explore and develop multiple complementary strategies for discovery and characterization of these interactions. An in vitro reaction assay-based pipeline was developed to provide a flexible framework for both the validation of putative regulatory interactions found via other methods, and the discovery of new regulatory interactions over a wide range of binding affinities. Small-molecule microarrays were explored as a potential platform for high-throughput discovery of the stronger range of binding interactions, which work will provide a basis for future development and wide-range implementation of the assay. Additionally, a concurrent metabolomics study of inappetence in spawning salmon yielded insights into the metabolic profile of inappetent versus fed cohorts; these results also provide high-level context for protein- and pathway- level studies. Taken together, these findings provide a methodological framework to increase the efficiency and range of study for protein-metabolite regulatory interactions, as well as lay groundwork for further expansion of that range, ultimately in service of the future development of systems-level metabolic models.
    URI
    http://hdl.handle.net/1853/56353
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Chemical and Biomolecular Engineering Theses and Dissertations [1516]

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