Workflow Description to Dynamically Model β-Arrestin Signaling Networks

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 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.Running title: β-arrestins signaling dynamical models β-arrestins serve as multiprotein scaffolds, connecting many partners (6), defining different pathways that act at different time-and-spatial scales (7-8). It is important to better understand β-arrestin signaling networks since it could lead towards the development of strategies, including the development of biased agonists (e.g. discriminating between G protein-dependent and β-arrestin-dependent signaling), with promising therapeutic implications (8)(9)(10)(11)(12)(13)(14).The complexity of β-arrestin-mediated cellular regulation motivates the use of mathematical models that take into account its dynamic (see Note 4.1). Dynamic biochemical reaction network models, presented in this chapter, allow formulation of hypotheses on molecular networks linked to β-arrestins, by explicitly integrating detailed knowledge on signal transduction. The first impact of such framework is to reveal potential alternatives or conflicting signaling mechanisms that have been hypothesized in the literature. Subsequently, iterative confrontations of model and data facilitate hypotheses rejection or acceptance. Finally, this methodology can be used to predict the behavior of unobserved quantities : concentration of molecules, parameter values, etc. For further information, readers can refer to reviews and papers dealing with applications of GPCR signaling modeling (15)(16)(17)(18)(19).At the very beginning of the modeling pipeline, one has to gather existing biological knowledge from the literature. In light of the available information, and according to the specific biological questions that are being addressed, the modeler decides the level of precision of the dynamic model. Hence, which molecules of a particular signaling subnetwork are taken into account and how the reactions between molecules should be represented will drive the choice of a particular mathematical framework. The nature and amount of available data to be confronted with the model outputs is also of primary importance. We would like to emphasize that the use of time series experimental data, which is becoming more and more favoured due to the development of Bioluminescence Resonance Energy Transfer (BRET) or Fluorescence resonance energy transfer (FRET) techno...

Advances in computational modeling approaches of pituitary gonadotropin signaling

“…A standard iterative workflow between model construction and experimental data is applied to address specific biological questions [94]. Once the dynamic model has been built, extensive optimization algorithms are used to estimate unknown parameters (kinetic rates, initial concentration, etc) [95], and dedicated statistical frameworks can be used for model selection [96].…”