The value of this parameter in the original publication is incorrect. It has been corrected in agreement with the authors.
Smith2009 - RGS mediated GTP hydrolysis This model is described in the article: Dual positive and negative regulation of GPCR signaling by GTP hydrolysis. Smith B, Hill C, Godfrey EL, Rand D, van den Berg H, Thornton S, Hodgkin M, Davey J, Ladds G. Cell Signal. 2009 Jul;21(7):1151-60. Abstract: G protein-coupled receptors (GPCRs) regulate a variety of intracellular pathways through their ability to promote the binding of GTP to heterotrimeric G proteins. Regulator of G protein signaling (RGS) proteins increases the intrinsic GTPase activity of Galpha-subunits and are widely regarded as negative regulators of G protein signaling. Using yeast we demonstrate that GTP hydrolysis is not only required for desensitization, but is essential for achieving a high maximal (saturated level) response. Thus RGS-mediated GTP hydrolysis acts as both a negative (low stimulation) and positive (high stimulation) regulator of signaling. To account for this we generated a new kinetic model of the G protein cycle where Galpha(GTP) enters an inactive GTP-bound state following effector activation. Furthermore, in vivo and in silico experimentation demonstrates that maximum signaling output first increases and then decreases with RGS concentration. This unimodal, non-monotone dependence on RGS concentration is novel. Analysis of the kinetic model has revealed a dynamic network motif that shows precisely how inclusion of the inactive GTP-bound state for the Galpha produces this unimodal relationship. To reproduce dose-response plots in the publication, the model is simulated with 12 different concentrations (see parameter Ligand_conc). For each concentration, a single value must be obtained from the integral of the trajectory of species z3 from time=0 to time=30. These values are then used to build a dose-response plot (authors used GraphPad Prism). Mutant strains are simulated with alternative parameter values or initial conditions in Table S3. This model is hosted on BioModels Database and identified by: MODEL1212040001 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
The model in the publication uses a tanh step function to add ligand, but only because the solver used by the authors did not support Events. Results are identical using this event, and has been changed in agreement with the authors.