Multi-Scale Computational Model of Fuel Homeostasis During Exercise: Effect of Hormonal Control

Abstract

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.

A computational model of adipose tissue metabolism: Evidence for intracellular compartmentation and differential activation of lipases

“…Palmitate and alanine represent the entire family of fatty acids and amino acids. The efficacy of this approach has been demonstrated in other studies (Kim et al, 2007;Zhou et al, 2005). Since the maximum rate coefficients are determined from in vivo flux data and the phenomenological M-M constants, the metabolic fluxes described by this method can be bounded within physiological limits.…”

“…Metabolic fluxes are expressed with a general irreversible bi-bi substrate to product enzymatic reaction coupled with controller energy metabolite pairs (Kim et al, 2007).…”

“…The maximum rate coefficients for these three reactions undergo the further modulation by epinephrine according to an empirical relation (Kim et al, 2007): (9) where C E (t) is the epinephrine concentration in adipose venous at time t;…”

A Mathematical Model of the Human Metabolic System and Metabolic Flexibility

“…Models of skeletal muscle metabolism, particularly in terms of energy balance, have also been proposed [34,35] however these are restricted to skeletal muscle and ignore the rest of the body. Some very detailed although very computational models have also been proposed covering skeletal muscle [46,47] and whole body metabolism [44]. There has also been simpler, more analytical, models proposed to describe whole body metabolism of carbohydrates, fats and proteins on long time scales although these tend to focus on energy balance and weight gain [36][37][38][39][40][41][42].…”

A Minimal Exercise Extension for Models of the Glucoregulatory System

“…In this context, models predicting the effect of exercise can be very useful tools for helping patients to adjust their treatment. Only a few models for exercise are available in the literature [3][4][5][6]. Their level of complexity varies strongly, but the number of parameters is typically high, which makes their identification, using only BG measurements, difficult.…”