Petri net modeling, computational analysis, and simulation of interleukin 17E signaling pathway: A study of dynamics

Abstract

Understanding the signaling mechanisms and regulation of IL-17 cytokines is important for studying immune system function and developing therapeutic interventions for inflammatory and infectious diseases. The study was focused on Petri Net modeling, a mathematical modeling tool used to model discrete event dynamic systems, to study the dynamic behavior of the Interleukin 17 (IL-17) signaling pathway. The study involves computational analysis including state space, deadlock sequence, reachability and behavioral properties in the Interleukin 17E signaling pathways. Extended Petri nets, where the arc set also includes inhibitor arcs in the model, were used to model the signaling pathways. Inhibitor arc helps where the condition of turning off a transition is modeled when a token is present in its corresponding input place. Petri nets are used in pathway analysis to model and analyze the behavior of biological pathways. They provide a graphical representation that captures the dynamic interactions between components within a pathway, such as genes, proteins, and metabolites. Through this model, we were able to study the behavior of the Interleukin 17E
Signaling pathway that plays a significant role in controlling infection.