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Modeling of metabolism and gene expression under hypoxia in Mycobacterium tuberculosis
Tuberculosis is a major disease in the several high-burden countries and remains a global threat according to the World Health Organization. Constraint-based modeling of metabolism has proven to be predictive of the metabolic adjustments due to genetic and environmental perturbations and is made more accurate by the inclusion of gene-expression pathways. Such models can be used to help develop new diagnostics and treatments for the disease by illuminating our understanding of the organism?s function in different conditions. M. tuberculosis in particular is known to encounter several stresses amenable to modeling such as hypoxia, nutrient deprivation, and oxidative/reductive stress. Here, an automated pipeline for reconstructing combined metabolic and gene expression models was implemented and used to create a reconstruction of central metabolic and the associated gene expression networks for M. tuberculosis. This model was then used to simulate the organism's hypoxic response and compare simulated transcriptomic data with an experimental dataset. The final model accounted for 347 genes in metabolic and gene expression subsystems. While the simulated transcription fluxes and transcriptomic data were on the whole uncorrelated, the model correctly predicted the downregulation of genes encoding subunits of the Type I NADH dehydrogenase. The CD-ROM, an appendix to the thesis, is available for viewing at the Media Center of Library & Information Access.
Bioinformatics and Medical Informatics
Biological and Medical Informatics
San Diego State University
Master of Science (M.S.) San Diego State University, 2015
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