Workflow description to dynamically model \beta-arrestin signaling networks

TL;DR

This paper presents a detailed methodology for dynamic modeling of eta-arrestin signaling networks, demonstrated through FSH receptor eta-arrestin recruitment kinetics using BRET data.

Contribution

It provides a comprehensive framework and step-by-step guide for modeling eta-arrestin signaling, including mathematical and statistical approaches, applied to real experimental data.

Findings

Successful modeling of eta-arrestin recruitment kinetics at FSH receptor

Demonstration of the methodology with BRET experimental data

Enhanced understanding of eta-arrestin signaling dynamics

Abstract

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 \beta-arrestin-dependent signaling networks. We illustrate our methodology through the modeling of \beta-arrestin recruitment kinetics at the Follicle Stimulating Hormone (FSH) receptor supported by in-house Bioluminescence Resonance Energy Transfer (BRET) data.