Insulin resistance (IR) is a key feature of aging, cardiovascular disease, obesity, and type 2 diabetes, and skeletal muscle is responsible for over 80% of insulin-stimulated glucose uptake. It is not known whether or not specific metabolic reactions could be targeted to improve insulin sensitivity, and, given the complexity of metabolism, and the interactions between genetic and environmental risk factors, which contribute to metabolic defects in IR, it is difficult to address these key questions experimentally.
Christopher Nogiec et al. utilize an integrative approach, using flux balance analysis (FBA) computational modeling, together with experimental validation. FBA is a constraint-based approach which has been used successfully to study metabolism by modeling relationships between input and output fluxes through biochemical reactions in metabolic networks. It is now applied to the study of muscle metabolism, the fasted to fed transition, and the impact of increased substrate availability (as in IR conditions).
This issue's cover illustrates the complexity of the computational metabolic network.