A novel hybrid discrete-continum cellelar autoaton model to study tuberculosis disease progression and treatment

Tuberculosis (TB) is an infection caused by a bacterium, M tuberculosis. It is the biggest infectious killer globally, with a person dying from the disease every twenty seconds. Treatment length urgently needs to be reduced in order to aid compliance to therapy, reducing emergence of antibiotic resistance. Until more can be learnt about the disease pathology and how drugs behave in the lung, however, treatment will remain at six months. New drugs are crucial to permit elimination of the disease, but clinical trials are expensive and long, and not all of the possible new regimens can be evaluated rapidly.

This research will use mathematical modelling to assist in the fight against tuberculosis. Model simulations can potentially be used to accelerate the rate of discovery, while reducing the need for expensive lab work and clinical trials. These models are driven by observations and are based on our understanding of the question at hand. They generate specific, explicitly testable predictions that can be proved by experiment. Previous tuberculosis models have quantified treatment response in clinical trials by analysing patients' sputum samples during treatment. A mathematical model that captures disease more accurately will enable better predications to be made.

When TB bacteria enter the lungs, the immune system attempts to control the disease, resulting in a localised reaction: a granuloma. When granulomas are unable to contain the bacteria, active disease develops. After diagnosis, patients are given a combination of antibiotics for a minimum of six months. How well the standard treatments penetrate into the granulomas or how well bacteria respond to the mix of antibiotics will define what the outcome of treatment will be.

I have developed a model to study tuberculosis disease progression and treatment in the lung. The model describes, using numbers, the movement and interactions of bacteria and immune cells in both time and space. My research plan outlines how I will enhance this model: by completing comprehensive training with collaborating experimentalists, mathematicians and computer scientists, I will develop the skills and knowledge required to consolidate my ability to develop the model. Collaboration with identified key individuals whose research focuses on the penetration of tuberculosis antibiotics into granulomas is the first vital step in our model development. Alongside this, we will incorporate data from a laboratory simulator that mimics changes in drug concentration over time, as they would occur in humans. The system allows multiple combinations of drugs to be integrated into our model. Researchers at the University of Michigan have a well-established model called 'GranSim'. Although they have a different focus to their work, their model simulates granuloma formation in TB infection and both the modelling and the immunology knowledge I would gain from spending time in their research group would be hugely beneficial for this project.

Finally, in collaboration with the computer scientists at the University, I plan to extend our mathematical model to 3D. Using various visualization techniques, we will be able to view the model simulations in a more understandable way, and features that were impossible in 2D will be seen. It might be possible to display the model on a 360 degree screen enabling the complex activities going on in the depth of the lung to be seen and the detail understood. My PhD student will develop this work further to create a model which follows the interaction of the granuloma in the wider lung: a key step along the path to a virtual patient.

Thus, our proposed model developments will allow us to answer some of the complex questions that underlie poor treatment response and relapse in TB. My innovative research approach integrates clinical and experimental results with mathematical techniques to address the problem of shortening tuberculosis treatment.