Mathematical models of biological networks can provide important predictions and insights into complex disease. In principle, gene regulatory networks and metabolic networks underlay the same complex phenotypes and diseases. However, systematic integration of these two model systems remains a fundamental challenge. We describe a general framework for the direct integration of probabilistic regulatory networks into constraint-based models of metabolism. The novel approach utilizes probabilistic reasoning in Bayesian Network models of regulatory networks as the “glue” that enables a natural interface between the two systems. Probabilistic reasoning is used to predict and quantify system-wide effects of perturbation to the regulatory network in the form of constraints for flux variability analysis. In this setting, both regulatory and metabolic networks inherently account for uncertainty. Applications leverage constraint-based metabolic models of brain metabolism and gene regulatory networks parameterized by gene expression data from the hippocampus to investigate the role of the HIF-1 pathway in Alzheimer’s disease.