Within-Host Individual-Based Model for Diabetic Tuberculosis Patients

For several decades, Tuberculosis (TB) has been preventable and curable. However, someone still dies from tuberculosis across the world every 15-20 seconds, and 50 percent of those deaths are children. Interestingly, data from WHO confirms the outcome of TB infection is usually different for an immunocompromised host, and thus, this project will look at comorbidity with diabetes. This would allow us to gain an in-depth understanding of how the immune system responds in tackling more than one disease and how this impacts the treatment and outcome of tuberculosis disease. Exploring TB and diabetes is important and timely, as data shows that in the next 10 years, the population of individuals with diabetes will double. This amounts to over 600 million people in the world, thus posing a major threat, as patients with diabetes have a higher mortality rate when infected with TB and have an increased likelihood of TB relapse.

To this effect, building on the mathematical framework of Bowness et al. (2018), this Ph.D. project aims to use an individual-based model (also known as agent-based model) to develop a within-host model of TB infection in diabetic patients. The goal would be to understand the impact of type 2 diabetes on the severity of pulmonary tuberculosis (i.e., the differences in immune responses in diabetic patients to TB). Whilst we do not intend to establish novel drugs, this will involve the simulation of novel treatment strategies that could serve as personalised treatment therapy for tuberculosis patients with type 2 diabetes. Thus, our understanding of the altered disease dynamics of the TB/diabetes model will help inform our decision on the inclusion of different drug doses and combinations that will help with favourable treatment outcomes. For example, if it is found that diabetic patients typically have more lipid-rich (slower-growing) TB bacteria, more effective administration of antibiotics that are particularly known for targeting slow-growing bacteria will be simulated. In addition, provided there is available data on immunotherapy treatment, this will also be simulated. We will work with experimentalists, using laboratory data to calibrate the model, and clinicians who have access to clinical data to validate model findings. Specifically, this project will benefit from a collaboration with Dr. Muge Cevik (University of St Andrews/NHS Lothian), who is an expert on treating diabetic TB patients and is currently involved in a clinical trial in this area of research.

Bowness, R., Chaplain, M.A., Powathil, G.G. and Gillespie, S.H., 2018. Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model. Journal of theoretical biology, 446, pp.87-100.

Combining specialised modelling techniques with complex data analysis in order to deliver prediction with quantified uncertainties lies at the heart of many of the major challenges facing UK industry and society over the next decades. Indeed, the recent Government Office for Science report "Computational Modelling, Technological Futures, 2018" specifies putting the UK at the forefront of the data revolution as one of their Grand Challenges.

The beneficiaries of our research portfolio will include a wide range of UK industrial sectors such as the pharmaceutical industry, risk consultancy, telecommunications and advanced materials, as well as government bodies, including the NHS, the Met Office and the Environment Agency.

Examples of current impactful projects pursued by students and in collaboration with stake-holders include:

- Using machine learning techniques to develop automated assessment of psoriatic arthritis from hand X-Rays, freeing up consultants' time (with the NHS).

- Uncertainty quantification for the Neutron Transport Equation improving nuclear reactor safety (co-funded by Wood).

- Optimising the resilience and self-configuration of communication networks with the help of random graph colouring problems (co-funded by BT).

- Risk quantification of failure cascades on oil platforms by using Bayesian networks to improve safety assessment for certification (co-funded by DNV-GL).

- Krylov regularisation in a Bayesian framework for low-resolution Nuclear Magnetic Resonance to assess properties of porous media for real-time exploration (co-funded by Schlumberger).

- Machine learning methods to untangle oceanographic sound data for a variety of goals in including the protection of wildlife in shipping lanes (with the Department of Physics).

Future committed partners for SAMBa 2.0 are: BT, Syngenta, Schlumberger, DNV GL, Wood, ONS, AstraZeneca, Roche, Diamond Light Source, GKN, NHS, NPL, Environment Agency, Novartis, Cytel, Mango, Moogsoft, Willis Towers Watson.

SAMBa's core mission is to train the next generation of academic and industrial researchers with the breadth and depth of skills necessary to address these challenges. SAMBa's most sustained impact will be through the contributions these researchers make over the longer term of their careers. To set the students up with the skills needed to maximise this impact, SAMBa has developed a bespoke training experience in collaboration with industry, at the heart of its activities. Integrative Think Tanks (ITTs) are week-long workshops in which industrial partners present high-level research challenges to students and academics. All participants work collaboratively to formulate mathematical
models and questions that address the challenges. These outputs are meaningful both to the non-academic partner, and as a mechanism for identifying mathematical topics which are suitable for PhD research. Through the co-ownership of collaboratively developed projects, SAMBa has the capacity to lead industry in capitalising on recent advances in mathematics. ITTs occur twice a year and excel in the process of problem distillation and formulation, resulting in an exemplary environment for developing impactful projects.

SAMBa's impact on the student experience will be profound, with training in a broad range of mathematical areas, in team working, in academic-industrial collaborations, and in developing skills in communicating with specialist and generalist audiences about their research. Experience with current SAMBa students has proven that these skills are highly prized: "The SAMBa approach was a great template for setting up a productive, creative and collaborative atmosphere. The commitment of the students in getting involved with unfamiliar areas of research and applying their experience towards producing solutions was very impressive." - Dr Mike Marsh, Space weather researcher, Met Office.