Glucose concentration varies logarithmically under both glycemic conditions in a computationally reconstructed human energy pool network (HEPNet)

The developing method of network medicine offers a stage to investigate efficiently not only the molecular complexity of a certain disease but additionally recognizes the ailment modules and the interconnectivity of the pathways. Hyper and hypo glycemia are one of the major diseases faced today in the western world. The level of glucose in the blood is controlled by insulin and glucagon henceforth a homeostasis is maintained. To solve this problem we present a formulated hypothesis where the basis revolves around the central idea of energy conservation and the energy currencies in a cell. Further we reconstructed a Human Energy Pool Network (HEPNet) with 4 compartments, 173 metabolic reactions and 158 metabolites; analyzed and characterized from various resources under a range of conditions into a kinetic model backbone. Ordinary differential equations generated through the network inferred that the glucose concentration is dependent on several factors but varies as a logarithmic function. Time course simulations were carried out and flux balance analysis validated the findings that ATP flux is dependent on 7 reactions with glucose playing a major role referred to as the Glucose component which varies logarithmically in a flux based ordinary differential equation. This glucose component denotes the change of flux behavior in the hypoglycemic and hyperglycemic condition.