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Thesis (PhD)--Stellenbosch University, 2025. ; de la Harpe, A. 2025. Mathematical modelling of cholesterol metabolism in Mycobacterium tuberculosis. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/efcb2a03-dfaa-4497-a6f7-a7925423bd77 ; ENGLISH ABSTRACT: Tuberculosis (TB) remains a global health challenge with significant mortality due to Mycobacterium tuberculosis, a pathogen adept at evading host immune responses and persisting in nutrient-poor environments. Although curable, issues such as latent infection, treatment adherence, and the emergence of multidrug-resistant strains highlight the urgent need for novel therapeutic strategies. Approximately one-quarter of the global population is estimated to have latent TB infection (LTBI). A notable characteristic of M. tuberculosis is its ability to metabolise cholesterol, a key component of host membranes, via a specialised pathway crucial for its survival and persistence. Targeting this pathway presents a promising avenue for new drug development. Genome-scale models integrate computational and experimental data to simulate the entirety of an organism’s metabolic processes, providing insights into metabolic networks and vulnerabilities exploitable for therapeutic interventions. We developed and validated a new genome-scale metabolic model for Mycolicibacterium smegmatis, iADLH1356, consisting of 1356 genes, 1672 reactions, and 1245 metabolites. M. smegmatis is commonly used in laboratory settings for studying M. tuberculosis, as it possesses similar genetic and metabolic characteristics. Additionally, we updated and validated an existing genome-scale model for M. tuberculosis, iEK1011_2.1, with a focus on the cholesterol metabolic pathway to identify potential drug targets. The updated model consists of 1016 genes, 1259 reactions, and 1018 metabolites. Using these two models, we demonstrated various applications of genome-scale models, including analysing growth on cholesterol, ... |