
The structure of dynamic GPCR signaling networks
Author(s) -
O'Neill Patrick R.,
Giri Lopamudra,
Karunarathne W. K. Ajith,
Patel Anilkumar K.,
Venkatesh K. V.,
Gautam N.
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: systems biology and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.087
H-Index - 51
eISSN - 1939-005X
pISSN - 1939-5094
DOI - 10.1002/wsbm.1249
Subject(s) - g protein coupled receptor , cell signaling , computer science , signal transduction , biological network , systems biology , computational biology , biology , neuroscience , microbiology and biotechnology
G‐protein‐coupled receptors ( GPCRs ) stimulate signaling networks that control a variety of critical physiological processes. Static information on the map of interacting signaling molecules at the basis of many cellular processes exists, but little is known about the dynamic operation of these networks. Here we focus on two questions. First, Is the network architecture underlying GPCR ‐activated cellular processes unique in comparison with others such as transcriptional networks? We discuss how spatially localized GPCR signaling requires uniquely organized networks to execute polarized cell responses. Second, What approaches overcome challenges in deciphering spatiotemporally dynamic networks that govern cell behavior? We focus on recently developed microfluidic and optical approaches that allow GPCR signaling pathways to be triggered and perturbed with spatially and temporally variant input while simultaneously visualizing molecular and cellular responses. When integrated with mathematical modeling, these approaches can help identify design principles that govern cell responses to extracellular signals. We outline why optical approaches that allow the behavior of a selected cell to be orchestrated continually are particularly well suited for probing network organization in single cells. WIREs Syst Biol Med 2014, 6:115–123. doi: 10.1002/wsbm.1249 This article is categorized under: Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models