Multi-scale computational model of fuel homeostasis during exercise: effect of hormonal control

A mathematical model of the whole-body metabolism is developed to predict fuel homeostasis during exercise by using hormonal control over cellular metabolic processes. The whole body model is composed of seven tissue compartments: brain, heart, liver, GI (gastrointestinal) tract, skeletal muscle, adipose tissue, and "other tissues". Each tissue compartment is described by dynamic mass balances and major cellular metabolic reactions. The glucagon-insulin controller is incorporated into the whole body model to predict hormonal changes during exercise. Moderate [150 W power output at 60% of peak oxygen consumption (VO(2max))] exercise for 60 min was implemented by increasing ATP utilization rates in heart and skeletal muscle. Arterial epinephrine level was given as an input function, which directly affects heart and skeletal muscle metabolism and indirectly other tissues via glucagon-insulin controller. Model simulations were validated with experimental data from human exercise studies. The exercise induced changes in hormonal signals modulated metabolic flux rates of different tissues in a coordinated way to achieve glucose homeostasis, demonstrating the efficacy of hormonal control over cellular metabolic processes. From experimental measurements of whole body glucose balance and arterial substrate concentrations, this model could predict the dynamic changes of hepatic glycogenolysis and gluconeogenesis, which are not easy to measure experimentally, suggesting the higher contribution of glycogenolysis ( approximately 75%). In addition, it could provide dynamic information on the relative contribution of carbohydrates and lipids for fuel oxidation in skeletal muscle. Model simulations indicate that external fuel supplies from other tissue/organ systems to skeletal muscle become important for prolonged exercise emphasizing the significance of interaction among tissues. In conclusion, this model can be used as a valuable complement to experimental studies due to its ability to predict what is difficult to measure directly, and usefulness to provide information about dynamic behaviors.