Inflammation is a fundamental mechanism for the body to induce repair and healing in tissues, and exacerbated inflammatory responses are associated with a wide variety of diseases and disorders. Categorising the various cells, proteins, and precise mechanisms involved in initiating and driving inflammation poses significant challenges, due to the complex interplay that occurs between them.
In this thesis, I will introduce a deadly parasitic disease called Visceral Leishmaniasis (VL) as a case study in using computational modelling techniques to elucidate the mechanisms underpinning inflammation. During VL infection, inflammatory aggregations of immune system cells form, these are called granulomas. Granulomas function to contain and subsequently remove infection. Whilst immunological studies have provided insights into the structure and function of granulomas, there remains a breadth of questions which laboratory techniques are currently incapable of answering. As such, the challenges facing biologists from a scientific perspective will be addressed, I will then argue after a thorough review of the relevant literature, that agent-based computational modelling is a logical choice for research into granuloma formation, and that such models can help answer some outstanding questions in the field.
The thesis presents the process of designing and developing the first spatially resolved model of liver localised granuloma formation during VL. The development and use of modelling and simulation to study granulomas has involved close collaboration with immunologists at all stages through conceptualisation, modelling, implementation, and also results interpretation. I describe the use of established statistical techniques to instill confidence in both the model, and the results it can produce through simulation.
Through iterative hypothesis generation and testing, the research undertaken has allowed for several predictions to be made, some of which have biological significance and which were later validated experimentally. Specifically, transcriptomic data analysis revealed that both infected and uninfected Kupffer cells are equally capable of responding to infection in a similar manner, something which wasn't previously evident in the literature. Using this transcriptomic data, I investigated through simulation, several experimental scenarios and elucidated a novel mechanism of immune system regulation in the liver microenvironment. Using an experimental model of Leishmania donovani infection, I demonstrated that such an immune regulatory mechanism can be overcome with the expansion of early promoter cells called Natural Killer T cells.
