Dynamic models of signaling networks allow the formulation of hypotheses on the topology and kinetic rate laws characterizing a given molecular network, in-depth exploration, and confrontation with kinetic biological data. Despite its standardization, dynamic modeling of signaling networks still requires successive technical steps that need to be carefully performed. Here, we detail these steps by going through the mathematical and statistical framework. We explain how it can be applied to the understanding of β-arrestin-dependent signaling networks. We illustrate our methodology through the modeling of β-arrestin recruitment kinetics at the follicle-stimulating hormone (FSH) receptor supported by in-house bioluminescence resonance energy transfer (BRET) data.
Keywords: Biochemical reaction network; Data fitting; Dynamic models; Model selection; Parameter identification; β-Arrestins.