Mathematical Modelling of Tuberculosis Infection
Author(s)
El-Khairi, Muna
Type
Thesis
Abstract
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.
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.
Date Issued
2012-11
Date Awarded
2013-01
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Shahrezaei, Vahid
Robertson, Brian
Sponsor
Engineering and Physical Sciences Research Council
Publisher Department
Mathematics
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)