# Workflow description to dynamically model \\beta-arrestin signaling networks

Source: [Workflow description to dynamically model \\beta-arrestin signaling networks](<https://www.sota2.com/research/paper/68fa5ee11e209131ce9e5fe4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:44:10.915Z

Authors: Romain Yvinec, Mohammed Akli Ayoub, Francesco De Pascali, Pascale Cr\\'epieux, Eric Reiter, Anne Poupon

Published: 2018-07-31T13:35:50.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.11811>)

[PDF](<https://arxiv.org/pdf/1807.11811.pdf>)