The human mitochondrial metabolic network was recently reconstructed based on proteomic and biochemical data. Linear programming and uniform random sampling were applied herein to identify candidate steady states of the metabolic network that were consistent with the imposed physico-chemical constraints and available experimental data. The activity of the mitochondrion was studied under four metabolic conditions: normal physiologic, diabetic, ischemic, and dietetic. Pairwise correlations between steady-state reaction fluxes were calculated in each condition to evaluate the dependence among the reactions in the network. Applying constraints on exchange fluxes resulted in predictions for intracellular fluxes that agreed with experimental data. Analyses of the steady-state flux distributions showed that the experimentally observed reduced activity of pyruvate dehydrogenase in vivo could be a result of stoichiometric constraints and therefore would not necessarily require enzymatic inhibition. The observed changes in the energy metabolism of the mitochondrion under diabetic conditions were used to evaluate the impact of previously suggested treatments. The results showed that neither normalized glucose uptake nor decreased ketone body uptake have a positive effect on the mitochondrial energy metabolism or network flexibility. Taken together, this study showed that sampling of the steady-state flux space is a powerful method to investigate network properties under different conditions and provides a basis for in silico evaluations of effects of potential disease treatments.The emergence of genomic, metabolic, and proteomic data has facilitated the reconstruction of genome-scale metabolic networks. Various network reconstructions have been carried out in recent years for microorganisms such as Escherichia coli (1), Saccharomyces cerevisiae (2), Helicobacter pylori (3), Haemophilus influenzae (4), and Geobacter sulferreducans.1 Most recently, proteomics data (6, 7) were utilized to reconstruct the metabolic network of the human cardiac mitochondrion (8).Constraint-based analyses have proven to be valuable for studying genome-scale metabolic networks. The constraintbased approach is based on the fact that cellular networks are constrained to operate within boundaries set by physico-chemical constraints (mass conservation, directional flow, enzymatic capacity, etc). In this study, uniform random sampling was used to calculate candidate steady-state flux distributions in the human cardiac mitochondrion under different sets of constraints representing various physiological conditions. Experimental data from many literature sources were integrated as physico-chemical constraints of the mitochondrial metabolic network. Constraints based on these experimental data were applied to segment the steady-state flux space defined by mass-balance constraints. This segmentation resulted in a characterization of all feasible steadystate flux distributions, termed candidate steady-state flux distributions, that occurred under each speci...