Non-alcoholic fatty liver disease is the commonest cause of liver disease, affecting 1 in 4 people worldwide. Only a proportion of patients with a fatty liver go on to develop serious liver problems but, crucially, we don’t understand why some NAFLD patients progress and others don’t. This interdisciplinary project will investigate the multicellular mechanisms underlying NAFLD using mathematical models. We aim to provide a novel theoretical perspective on NAFLD progression as a slowly fluctuating, stochastic dynamical system. We hypothesise that the variable progression of NAFLD can be explained by complex interactions between different cell states (‘cell circuits’) and different sources of variability (genetic and environmental factors, molecular/cellular variability, spatial heterogeneity). To test this idea, you will develop a theory of stochastic cell circuits to explain variability in disease progression and to integrate multiple data sources. The mathematical modelling will provide a theoretical foundation from which to understand variability and speed of NAFLD progression. From the inferred mechanisms of cell interactions, we may be able to predict which stage-specific interventions (modulations of individual cell activities or their interactions) are optimal to halt or revert progression. There is the possibility to test model predictions with biomedical collaborators in the form of drug combinations in in vitro models or to validate them on medical cohort data. This would allow us to refine the mechanisms in the mathematical model further, in an iterative predict-test-refine cycle between theory and experiments.
The project will be based in the group of Linus Schumacher at the Centre for Regenerative Medicine in Edinburgh, and co-supervised by Jochen Kursawe (Mathematics, St. Andrews) and Jonathan Fallowfield (Centre for Inflammation Research, Edinburgh). The Schumacher group has extensive experience in building mathematical models and collaboration with experimental groups, and the Centre for Regenerative Medicine provides a stimulating research environment and training opportunities for PhD students. Dr Kursawe will contribute expertise in stochastic modelling and modelling of cell states. Prof Fallowfield will provide translational research expertise in NAFLD and fibrosis biology, and access to the SteatoSITE NAFLD Data Commons. There is further opportunity to interact with the systems biology and mathematical biology communities in Edinburgh and St. Andrews.
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