📖 ABSTRACT/OVERVIEW
The complex, dynamic interactions between Mycobacterium tuberculosis and the human host immune system involve feedback loops and non-linear regulatory mechanisms that cannot be adequately captured by reductionist experimental approaches alone, motivating the application of systems biology modelling. This research proposes and validates a systems biology framework integrating computational modelling with experimental immunological data to characterise host-pathogen dynamics in M. tuberculosis infection among patients in Kano and Sokoto States, North West Nigeria. A mixed methods design combines transcriptomic profiling of peripheral blood mononuclear cells from 150 active TB patients, 150 LTBI individuals, and 100 healthy controls, with ordinary differential equation-based mathematical modelling of macrophage-mycobacterium dynamics. Network analysis, gene regulatory network inference using ARACNE algorithm, and agent-based simulation of granuloma formation will be applied. Experimental validation using ex vivo macrophage infection assays will benchmark model predictions against empirical observations. The framework will identify critical regulatory nodes including TNF-alpha, IL-10, and IFN-gamma signalling axes governing transition from contained to progressive infection. System-level perturbation analysis will predict potential immunomodulatory therapeutic targets. The research will contribute an original computational-experimental platform for TB immunology that integrates systems-level thinking into West African host-pathogen research, providing a theoretical and methodological contribution that extends beyond Nigeria. Findings will inform targeted immunotherapeutic adjunct strategies for TB management and latent infection treatment in high-burden Northern Nigerian settings. Keywords: systems biology, tuberculosis, host-pathogen interactions, computational modelling, granuloma
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