The dynamics of cholesterol metabolism and atherosclerosis across population subgroups

Abstract

Atherosclerosis is an inflammatory disorder characterized by the formation of plaque inside an artery wall. Despite the significance of atherosclerotic cardiovascular disease to healthcare, the pathophysiology of atherosclerosis is not fully understood. To allow us to examine the dynamical process of atherogenesis, a theoretical approach has the potential to increase knowledge of the interactions involved. A computational model of atherosclerosis has been built to study the process of atheroma formation and to suggest therapeutic hypotheses. Previously, computational models of disease pathways have aided in combinatorial drug discovery, and have led to the generation of therapeutic hypotheses. The model has been developed to conform to Systems Biology Markup Language (SBML) and Systems Biology Graphical Notation (SBGN) open standards. Collating parameters from multiple sources, the curated model displays atherosclerosis-like behaviour such as lipoprotein oxidation, cellular build-up, extra-cellular matrix formation and reverse cholesterol transport. Publicly available genomic data has been utilised to evaluate the changes in pathway dynamics across population subgroups. Data taken from the 1000 Genome Project, a worldwide effort to create an expansive catalogue of human variation, has been used to predict a tertiary protein structure for all proteins contained within the mathematical model, and the variation in structure for a collection of mutations is studied. A combination of molecular dynamics methods and electrostatic potential analysis are then used to estimate how the binding rates of these proteins are affected by individual mutations. These updated binding rates are subsequently used to reparameterise the mathematical model. With population data available from the 1000 Genomes Project, these new parameters can be used to study population specific dynamics of atherosclerosis, and subsequently suggest new therapeutic responses.

Date of Award	Sept 2018
Original language	English
Supervisor	Steven Watterson (Supervisor) & Victoria Mc Gilligan (Supervisor)