Metabolic modeling of mycobacterium tuberculosis through the integration of large-scale genomics datasets
Date
2014
DOI
Authors
Garay, Christopher Dane
Version
OA Version
Citation
Abstract
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.
Description
Thesis (Ph. D.)--Boston University