Abstract
The increase in Type 2 diabetes and other metabolic disorders has led to an intense focus on the areas of research related to metabolism. Because the liver is essential in regulating metabolite concentrations that maintain life, it is especially important to have good knowledge of the functions within this organ. In silico mathematical models that can adequately describe metabolite concentrations, flux and transport rates in the liver in vivo can be a useful predictive tool. Fully dynamic models, which contain expressions for Michaelis-Menten reaction kinetics can be utilized to investigate different metabolic states, for example exercise, fed or starved state. In this paper we describe a two compartment (blood and tissue) spatially lumped liver metabolism model. First, we use Bayesian Flux Balance Analysis (BFBA) to estimate the values of flux and transport rates at steady state, which agree closely with values from the literature. These values are then used to find a set of Michaelis-Menten parameters and initial concentrations which identify a dynamic model that can be used for exploring different metabolic states. In particular, we investigate the effect of doubling the concentration of lactate entering the system via the hepatic artery and portal vein. This change in lactate concentration forces the system to a new steady state, where glucose production is increased.
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