Glucagon and insulin production in pancreatic cells modeled using Petri nets and Boolean networks
This work addresses the complex interactions in glucose regulation for diabetes research, but it is incremental as it builds on existing modeling approaches.
The researchers tackled the problem of understanding glucose regulation in diabetes by creating Petri net models of insulin and glucagon secretion in pancreatic cells, and they analyzed the dynamics and transformed the system into a Boolean network.
Diabetes is a civilization chronic disease characterized by a constant elevated concentration of glucose in the blood. Many processes are involved in the glucose regulation, and their interactions are very complex. To better understand those processes we set ourselves a goal to create a Petri net model of the glucose regulation in the whole body. So far we have managed to create a model of glycolysis and synthesis of glucose in the liver, and the general overview models of the glucose regulation in a healthy and diabetic person. In this paper we introduce Petri nets models of insulin secretion in beta cell of the pancreas, and glucagon in the pancreas alpha cells. Those two hormones have mutually opposite effects: insulin preventing hyperglycemia, and glucagon preventing hypoglycemia. Understanding the mechanisms of insulin and glucagon secretion constitutes the basis for understanding diabetes. We also present a model in which both processes occur together, depending on the blood glucose level. The dynamics of each model is analysed. Additionally, we transform the overall insulin and glucagon secretion system to a Boolean network, following standard transformation rules.