Computational Models of Endothelial Cell Growth Factor Kinetics and Glucose Metabolism

Abstract

Cardiovascular disease (CVD) is the leading cause of death worldwide, and endothelial dysfunction contributes to CVD progression. While experimental studies attribute endothelial dysfunction to changes in cell signaling, protein expression, and metabolism, it remains unclear how these individual changes interact within the complex endothelium to lead to the dysfunctional phenotype. Computational models provide an essential tool to understand the endothelium as a system. The dissertation objective was to use mass action kinetics and stoichiometric computational models to predict endothelial growth factor binding and glucose metabolic flux. I first created a mass action kinetics model to investigate fibroblast growth factor-2 (FGF2) binding to endothelial cells under laminar flow by incorporating flow-induced changes to the endothelial cell apical surface. FGF2 model dynamics were described by a set of ordinary differential equations and kinetic parameters from the literature. When the model included increased heparan sulfate proteoglycan production and binding site availability with shear stress, as well as increased FGF2 dissociation with shear stress, the model successfully predicted FGF2 biphasic binding response as a function of shear stress. Next, I created a carbon transition model to predict endothelial cell intracellular metabolic fluxes using isotope labeling experiments. The model traces carbon movement through glycolysis, the pentose phosphate pathway and the TCA cycle. The model was iteratively updated to reduce the error between the predicted and measured experimental data. The final model highlighted the importance of glutamine in reproducing TCA cycle intermediates and in feeding back into glycolysis. Finally, I developed a stoichiometric endothelial model to describe the metabolic state of a quiescent endothelial cell in vitro. A genome scale cell model was curated into an endothelial-specific model using publicly available transcriptomic data to remove reactions associated with proteins that were not expressed in endothelial cells. The carbon transition model fluxes were used to constrain the minimum and maximum fluxes in the stoichiometric endothelial model. I then used the model to explore possible endothelial metabolic objective functions, including minimizing oxidative stress or resource consumption. I concluded that no single metabolic objective function accurately predicted quiescent endothelial monolayer metabolism. Together these models provide valuable insight into FGF2 binding kinetics and steady state metabolic endothelial cell objectives. In addition, the mass action kinetic and stoichiometric endothelial models provide a systems-level analysis of endothelial function, enhancing our understanding of the complex interactions that contribute to endothelial dysfunction and eventual cardiovascular disease.Ph.D., Biomedical Engineering -- Drexel University, 201