Modeling a Complex Biological Network with Temporal Heterogeneity: Cardiac Myocyte Plasticity as a Case Study

Complex biological systems often characterize nonlinear dynamics. Employing traditional deterministic or stochastic approaches to quantify these dynamics either fail to capture their existing deviant effects or lead to combinatorial explosion. In this work we devised a novel approach that projects the biological functions within a pathway to a network of stochastic events that are random in time and space. By applying this approach recursively to the object system we build the event network of the entire system. The dynamics of the system evolves through the execution of the event network by a simulation engine which comprised of a time prioritized event queue. As a case study we utilized the current method and conducted an in-silico experiment on the metabolic plasticity of a cardiac myocyete. We aimed to quantify the down stream effects of insulin signaling that predominantly controls the plasticity in myocardium. Intriguingly, our in-silico results on transcription regulatory effect of insulin showed a good agreement with experimental data. Meanwhile we were able to characterize the flux change across major metabolic pathways over 48 hours of the in-silico experiment. Our simulation performed a remarkable efficiency by conducting 48 hours of simulation-time in less that 2 hours of processor time.