Modeling of G-protein-coupled Receptor Signaling Pathways

Abstract

G-protein-coupled receptors (GPCRs) 2 are the largest family of cell membrane receptors. An estimated 50% of current pharmaceuticals target GPCRs (1), suggesting that further increases in our understanding of GPCRs and the signaling pathways they initiate will lead to new drug targets. Mathematical and computational modeling (here, simply "modeling") has a substantial history in modern biology and pharmacology (2, 3) and offers a powerful tool for examining GPCR pathways. Such models can be used to better understand hypothesized mechanisms, run virtual (in silico) experiments, interpret data, suggest new drug targets, motivate experiments, and offer new explanations for observed phenomena. Many Simultaneous Kinetic ProcessesThe more we learn about GPCRs and the pathways they activate, the more complicated our picture of GPCR signaling becomes. At the subsecond to minute time scale, ligand binding, interactions of receptors with G-proteins, G-protein activation/deactivation, and the action of RGS (regulator of G-protein signaling) proteins in GAP or non-GAP roles occur. Depending on the particular G-protein subunit, many downstream signaling molecules (e.g. adenylyl cyclase, cAMP, phospholipase C, Ca 2ϩ , membrane channels) are transiently modulated, locally or over the entire cell. At a slightly longer time scale, receptor phosphorylation, arrestin binding, and activation of non-G-protein-dependent signaling pathways occur (4, 5). Receptor trafficking events, i.e. internalization, recycling, routing to lysosomes, and up-regulation, as well as new receptor synthesis and regulation of gene expression, also occur over the time scale of minutes to hours. Many of the receptor and membrane level events are shown schematically in Fig. 1A. Noble attempts to consolidate information on intracellular signaling pathways initiated by GPCR binding are available (and evolving) (e.g. Database of Science Signaling) (6, 7).Put simply, it is difficult to intuit the net result of so many simultaneous kinetic processes. Add nonlinearities such as feedback, time-varying sequestering of molecules via scaffolds and other mechanisms, and multiple receptor and G-protein species to the temporal and spatial variations already mentioned, and it is virtually impossible. Modeling helps: putting in hypothesized mechanisms and numbers (rates, concentrations) allows both qualitative and quantitative insights. With models one can ask questions such as, which of several simultaneous pathways plays a larger role? Do these events happen fast enough to be important in signaling? What mechanisms might allow modulation of signaling, desensitization, or receptor cross-talk? What factors influence ligand efficacy? Interruption of processes at which points (i.e. drug targets) is most effective? Which experimental protocol is most likely to emphasize a particular mechanism? Model DevelopmentHow do you create a model? Mathematical/computational modeling that is mechanistic (as opposed to empirical models, which approximate the shape of a relationship with...

Cellular modelling: experiments and simulation to develop a physiological model of G-protein control of muscarinic K + channels in mammalian atrial cells

“…The G-protein signalling pathways are therefore regarded as therapy targets for cardiovascular diseases and their characteristics need to be understood quantitatively (Alemany et al 2007). Mathematical models of G-protein signalling can play an important role in developing quantitative understanding of these signalling pathways (Thomsen et al 1988;Mackay 1990;Felber et al 1996;Mosser et al 2002;Hao et al 2003;Saucerman et al 2003;Yi et al 2003;Zhong et al 2003;Suh et al 2004;Katanaev & Chornomorets 2007;Linderman 2009). In the heart, models of sympathetic control of voltage-dependent inactivation in the L-type Ca 2+ channel (Faber & Rudy 2007;Findlay et al 2008) and a biochemical pathway model for b-adrenergic receptor stimulation (Saucerman et al 2003) have been developed.…”

Modelling and mathematical analysis of the M$_{2}$ receptor-dependent joint signalling and secondary messenger network in CHO cells

“…GPCR-induced signalling is well-known in common literature (Pierce et al, 2002;Linderman, 2009;Taylor et al, 2012;Sunahara & Taussig, 2002). Specifically the link to the cyclic AMP (cAMP -a secondary messenger molecule)-induced signalling is in the focus of current pharmaceutical research (Milligan & Kostenis, 2006;Hu et al, 2010).…”

“…The pathway consists of proteins as well as the secondary messenger cyclic adenosine monophosphate (cAMP). The respective biochemical reactions are principally well known (Pierce et al, 2002;Linderman, 2009;Sunahara & Taussig, 2002;Taylor et al, 2012), but to the best of our knowledge no effort has been taken so far to derive a mathematical model, especially for CHO cells, which are very important in pharmaceutical research and for the industrial production of recombinant protein therapeutics (De Jesus & Wurm, 2011;Walsh, 2010).…”

How uniform is cAMP signaling? Focus on “Systems analysis of GLP-1 receptor signaling in pancreatic β-cells”

“…In recent years this approach has been gaining popularity as a tool to investigate cell signaling, especially those signaling pathways involving G protein-coupled receptors (11). With the extensive volume of quantitative data that has accumulated, some of these signaling pathways are ideally suited for such an approach.…”