Metabolic modeling of mycobacterium tuberculosis through the integration of large-scale genomics datasets
Garay, Christopher Dane
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Mycobacterium tuberculosis (MTB) is the bacterium that is the causal agent of tuberculosis. MTB is estimated to infect one-third of the world's population. The emergence of multi drug-resistant and extensively drug-resistant strains of the bacterium are becoming a larger threat to global health as they decrease the efficacy of current treatments and make the disease more fatal. These factors combine to make MTB an interesting target for study with novel systems biology approaches. Genome-scale metabolic models have emerged as important platforms for the analysis of datasets that describe highly-interconnected biological processes. We have the first comprehensive profiling of mRNA, proteins, metabolites, and lipids in MTB during an in vitro model of infection that includes a time course of induced hypoxia andre-aeration. Hypoxia and reaeration are important cues during infection of the human host and act to model the environment seen in the host. We use genome-scale metabolic modeling methods to integrate these data with our metabolic model will allow us to generate experimentally testable predictions about metabolic adaptations that occur in response to experimental perturbations that represent an in vitro model of important environmental cues present during infection, dormancy, and re-activation in the human host.
Thesis (Ph. D.)--Boston University