Modelling and genomic analysis of competition and diversity in Mycobacterium tuberculosis

Numerous studies have identified tuberculosis patients in whom more than one distinct strain of Mycobacterium tuberculosis (M. tuberculosis) is present. The diversity of M. tuberculosis can have dramatic effects on disease dynamics. This thesis focuses on the study of diversity of M. tuberculosis and competition between its strains by analysing mathematical models and applying statistical techniques to clinical, genetic and epidemiological data. Mathematical models of M. tuberculosis, both in-vitro and within host are developed and analysed. Single strain models are analysed and then extended to incorporate the interaction of two or more M. tuberculosis strains. We find that during active disease, competition between strains is not as severe as during latency. Analysis of the within host models using approaches from data science identify key model parameters that affect the outcome of infection. These models are further explored using virtual experiments to answer questions such as how does re-infection affect disease progression? Evolutionary tools, especially phylogenetic trees, are increasingly being used to study short-term variation in M. tuberculosis. Some regions of a genome sequence may be disruptive ... (continues)