Developing in silico tools to analyse dyslipidemia and cardiovascular disease (360G-Wellcome-216963_Z_19_Z)

Cardiovascular disease is the leading cause of death globally and its principle pathology is atherosclerosis. Atherosclerosis is challenging to study due to the location of atheroma in artery walls and the risk of plaque rupture. There are few in vitro experimental systems available and animal models are challenging to work with. Hence, there is a significant opportunity for high quality computational models to serve as quick, cheap, consistent and ethically unlimited experimental systems in studies of cardiovascular disease. We propose to build the first detailed, integrated, predictive model of cholesterol metabolism and atherosclerosis that will enable us to simulate how dyslipidemia drives cardiovascular disease and how interventions can mitigate this. We will generate a new computational model of cholesterol biosynthesis in SBML file formats from published SBGN maps. This will be merged with a published computational model of atherosclerosis that exists as a map in SBGN format and as a model in SBML format. The merged model will provide a resource that enables us to simulate how dyslipidemia drives the formation of atherosclerosis and how interventions such as statin therapy reduce plaque development. We also hope to use the model with systems pharmacology approaches to develop new multi-drug therapeutic hypotheses.