Computationally Modelling Cholesterol Metabolism and Atherosclerosis

SIMPLE SUMMARY: Heart disease and stroke are major global health problems. There are many risk factors for these conditions. However, the main risk factor is high levels of low-density lipoprotein cholesterol (LDL-C). LDL-C is involved in the formation of plaques which eventually lead to either a heart attack or a stroke. The biology associated with this process is exceptionally complex. Computational modelling can be used to understand this complexity. In this work computational modelling was used to better understand the relationship between high levels of LDL-C and plaque progression. The model was able to identify therapeutic interventions which are effective at slowing plaque growth. ABSTRACT: Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation.