Predictive Model of Pheromone Signaling

Abstract

To fully understand complex signaling networks and to be able to predict their behavior, quantitative models are required to keep track of dynamic changes in signal propagation. Hao et al. provide a clear example of the power of combined experimental and computational analysis in a study of G protein signaling in response to mating pheromone in yeast. They used quantitative immunoblotting to determine the amounts per cell of G protein α subunit (about 8000 copies) and of an RGS protein (about 2000 copies). RGS, regulator of G protein signaling, is an inhibitory factor that enhances guanosine triphosphatase activity of the Gα protein. They also measured changes in the abundance of these proteins in response to pheromone and how twofold increases in the abundance of these proteins or of Gβγ subunits affected signaling. A model based on ordinary differential equations was consistent with a feedback loop in which increased synthesis of the RGS protein in response to pheromone would prevent prolonged signaling. However, inconsistencies in predictions of the model with the experimental data and observation of binary (all or none) signaling in cells overexpressing the RGS protein led the authors to consider a positive feedback loop that would cause pheromone-dependent degradation of the RGS protein. A revised model, which included a stochastic term simulating random fluctuations in protein concentrations, was consistent with the experimental data. To determine whether the unanticipated positive-feedback loop predicted from the modeling does in fact exist, the authors monitored pheromone-dependent degradation of the RGS protein and discovered a previously unknown pheromone-induced ubiquitination of the RGS protein. The authors note that the relatively simple and highly studied yeast pheromone signaling pathway may be amenable to further computer simulation of the entire pathway. N. Hao, N. Yildirim, Y. Wang, T. C. Elston, H. G. Dohlman, Regulators of G protein signaling and transient activation of signaling: Experimental and computational analysis reveals negative and positive feedback controls on G protein activity. J. Biol. Chem. 278 , 46506-46515 (2003). [Abstract] [Full Text]