Computational models provide a useful tool for experimentalists in understanding the processes occurring in a biological system that may otherwise be impossible toobserve directly. The pivotal role of the liver in metabolic regulation makes it a challenging organ to model and simulate. Computational models that can adequately describe hepatic metabolism further the understanding of the functions within the organ. This thesis designs, identifies and analyzes three computational models of hepatic metabolism which account for the complexity of liver biochemistry, hepatic heterogeneity and perfused organ states.
These models are governed by systems of ordinary or partial differential equations that depend on a large number of parameters that need to be identified. The classical deterministic parameter estimation problem is recast in the form of Bayesian statistical inference, allowing the integration of a priori belief and data from several
experiments. In this approach, the unknowns are modeled as random variables and their values are probability densities. Effcient Markov Chain Monte Carlo techniques are designed and adapted to draw samples effectively from the parameter densities.
Setting deterministic models inside a statistical framework makes it possible to study the correlations of different pathways with the time courses of metabolites. This
methodology is applied to quantify the sensitivity of various hepatic pathways related to glucose production to redox state under varying conditions, providing insight into
the regulation of hepatic gluconeogenesis.
The Bayesian framework that we utilize allows us to incorporate into our parameter estimation process information available prior to considering the data. We show
that the choices made in the encoding of this a priori information may affect both the parameter estimation and the corresponding model predictions by introducing three priors for a particular model and scrutinizing their effects.