Simulations of the insulin-like growth factor receptor signaling pathway with randomly sampled parameters

The insulin-like growth factor (IGF) receptor signaling pathway plays a crucial role in regulating growth, development, and metabolism in the human body. It encompasses a group of signalling molecules, receptors, and downstream signaling pathways that are involved in various physiological processes. Given the complexity of molecular processes within this pathway, mathematical models have emerged as potent tools for dissecting the dynamic behaviors of this signaling pathways. To address the heterogeneity of cellular signalling observed in single-cell studies, the nonlinear mixed-effects model has been used recently to generate different simulations that represent the molecular dynamics in different cells. In this study, we use a nonlinear mixed-effects ordinary differential equation model, based on an established ordinary differential equation model, to generate simulations for the IGFR signaling pathway. Simulations shows that, when the AKT and mitogen-activated protein (MAP) kinase have different activities, other kinases in the pathway have various dynamic properties. We also test the influence of two inhibitors for the MAP kinase pathway and PI3K/AKT pathway separately. Simulation results are consistent with the regulatory mechanisms of each pathway as well as the cross-talk between the MAP kinase pathway and AKT pathway. This study provides an alternative method to study the functions of model parameter variations on the dynamics of complex systems.