Date of Award


Degree Type



Chemical and Biomedical Engineering

First Advisor

Belovich, Joanne

Subject Headings

Gluconeogenesis -- Mathematical models, Liver -- Metabolism -- Mathematical models, Mathematical modeling Gluconeogenesis Ketogenesis Metabolism


An in silico liver was developed in attempt to represent the in vivo state of the fasted liver. It featured two conceptual models. The first one represented carbohydrate metabolism of the human liver, which included the heterogeneous nature of the liver by incorporating spatial variation of key enzyme activities. This model was able to predict the overall fluxes in tissue and the effect of high intensity exercise on the various hepatic fluxes. A second model of hepatic metabolism was developed to represent the complex interplay between gluconeogenesis, lipid metabolism, and alcohol metabolism in the fasted rat liver. Biochemical pathways are represented by key kinetic reactions that include allosteric and substrates effectors, and phosphorylation/dephosphorylation enzymes regulation. The model also incorporates the compartmentation and intercompartmental transports between the cytosol and the mitochondria, and transport of metabolites between blood compartment and the tissue. The model is based on the experimental set-up of fasted perfused rat livers. The model was used to simulate the effects of the two main gluconeogenic substrates available during the fasting state-- lactate and pyruvate--along with the addition of fatty acids and/or ethanol. The model predicts successfully the rates of glucose and ketone production, substrate uptake, and citric acid cycle. Parameter estimations were performed in order to obtain a set of physiological parameters capable of representing the liver under various combinations of nutrients. Parameter sensitivity analysis was generated to quantify the contribution of each parameter to the model output. The model was validated with data available in the published literature from ex vivo studies. The in silico liver constitutes a tool that can be used to predict the effect of physiological stimuli on flux and concentration distributions. This will provide an increase in the understanding of such effects and to determine what parameters, enzymes, and fluxes are responsible f