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Title: Mathematical modelling of tuberculosis infection
Author: El-Khairi, Muna
ISNI:       0000 0004 2732 382X
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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Tuberculosis is one of the leading causes of death by infectious disease in the world today. However, the majority of individuals infected with Mycobacterium tuberculosis are able to contain bacterial growth and establish a latent infection. The aim of this thesis is to develop mathematical models to study the progression of disease in individuals infected with Mycobacterium tuberculosis. This work focuses on understanding bacterial and host defence mechanisms that govern the outcome of infection, and on identifying factors that affect the outcome of anti-tuberculosis chemotherapy. A detailed model of human tuberculosis infection in the lung and peripheral draining lymph node is developed that builds on models published in the literature. Analysis of this model suggests a differential role for innate and adaptive immune responses in determining the outcome of infection, and a possible role for an intracellular bacterial population in establishing a persistent infection. For certain parameter values this system has multiple steady states so the outcome of infection may also depend on initial conditions. This model is then modified to incorporate the effect of treatment with the antimycobacterial agent rifampicin. The model is used to investigate different treatment regimens and simulation results suggest that the length of tuberculosis therapy can be reduced by further optimizing the standard rifampicin dosing regimen. Simple predator-prey type models of infection are constructed to gain further insight into the mechanisms that control the establishment and maintenance of latency. These models support observations made from the full disease model regarding the roles of innate and adaptive immunity in fighting infection and the influence of an intracellular bacterial population that is protected from the innate immune system. The addition of a population of non-replicating or slow growing bacteria contributes to the establishment of latent infection and generally makes latency a more robust and stable state.
Supervisor: Shahrezaei, Vahid ; Robertson, Brian Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral