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Supervisions (1)

Mathematical Medicine and Biology
Doctorate (PhD or DPhil)

Level Doctorate (PhD or DPhil)
Student Dr JAMES PRESTON
Status Complete
Part Time No
Years 2015 - 2019
Project Title Modelling the dynamics between Enteropathogenic Escherichia coli infections, the immune response and antibiotic therapies
Project Description Enteropathogenic Escherichia coli (EPEC) is a virulent strain of bacteria that is a leading cause of
death in children with diarrhoea, especially in countries with underdeveloped healthcare. EPEC
attach to host intestinal epithelial cells (IECs) and secrete proteins into them that subvert the
immune response. In particular, these proteins inhibit pro-inflammatory signalling, IEC apoptosis
and phagocytosis by leukocytes. It is estimated that there are approximately 800,000 fatalities
worldwide per year due to EPEC. Therefore, finding effective treatments for EPEC infections is
crucial to the improvement of human health across the globe. In order to find effective treatments,
a detailed understanding of EPEC and its infection strategy is needed. Researchers at the Centre
for Biomolecular Sciences at the University of Nottingham have recently discovered experimentally
that EPEC internalise host pro-inflammatory cytokines, granting dynamic control of protein secretion and reduced susceptibility to aminoglycosides (a class of antibiotic). However, while biological
studies have uncovered vital information on many of the mechanisms of EPEC’s infection strategy,
much is still unknown about the infection strategy.
In this thesis, we first formulate and analyse a mathematical model describing a general infection
resulting from interactions between host epithelial cells and non-specific bacteria in the human gut.
The model comprises a set of ODEs describing the dynamics between a bacterial infection and the
innate immune response. We then extend the model to account for EPEC-specific mechanisms with
analysis providing insight into the effectiveness of the various aspects of EPEC’s infection strategy,
allowing the effectiveness of an EPEC infection to be compared to that of a general infection. The
EPEC model was then adapted into an in silico multi-scale hybrid model that includes mechanisms
on the macro-, cell-, and sub-cellular scales, implemented deterministically and stochastically, with
the aim of exploring the dynamics between EPEC and the host’s immune system both spatially
and temporally. Finally, the hybrid model is extended to explore the effects of bacterial biofilms
on the success of the infection. In each of the EPEC models, we account for the effects of antibiotics and the recent experimental discoveries discussed above. Analysis of the models via steady
state, stability, sensitivity and bifurcation analyses and numerical simulations allows us to make
predictions on the effectiveness of antibacterial treatment strategies. Our models predict effective
treatment strategies targeting bacterial protein synthesis and replication, the immune response, the
application of antibiotic, the positioning of bacteria and the ability of bacteria to form biofilms. We
hope our predictions will inform, and reduce the time and cost of, future experimental work
Awarding Institution University of Nottingham
Director of Studies REUBEN O'DEA
Second Supervisor BINDI BROOK