Integrated Strategies to Gain a Systems-Level View of Dynamic Signaling Networks.
In order to survive and function properly in the face of an ever changing environment, cells must be able to sense changes in their surroundings and respond accordingly. Cells process information about their environment through complex signaling networks composed of many discrete signaling molecules. Individual pathways within these networks are often tightly integrated and highly dynamic, allowing cells to respond to a given stimulus (or, as is typically the case under physiological conditions, a combination of stimuli) in a specific and appropriate manner. However, due to the size and complexity of many cellular signaling networks, it is often difficult to predict how cellular signaling networks will respond under a particular set of conditions. Indeed, crosstalk between individual signaling pathways may lead to responses that are nonintuitive (or even counterintuitive) based on examination of the individual pathways in isolation. Therefore, to gain a more comprehensive view of cell signaling processes, it is important to understand how signaling networks behave at the systems level. This requires integrated strategies that combine quantitative experimental data with computational models. In this chapter, we first examine some of the progress that has recently been made toward understanding the systems-level regulation of cellular signaling networks, with a particular emphasis on phosphorylation-dependent signaling networks. We then discuss how genetically targetable fluorescent biosensors are being used together with computational models to gain unique insights into the spatiotemporal regulation of signaling networks within single, living cells.
North Carolina Agricultural and Technical State University, Greensboro, NC, United States. Electronic address: rhnewman@ncat.edu.
Robert H
RH
Newman
University of California, San Diego, San Diego, CA, United States. Electronic address: jzhang32@ucsd.edu.
Jin
J
Zhang
D000818:Animals; D015374:Biosensing Techniques / Q000379:methods*; D003198:Computer Simulation; D005434:Flow Cytometry / Q000379:methods; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D010744:Phosphoric Monoester Hydrolases / Q000032:analysis / Q000378:metabolism; D010766:Phosphorylation; D040081:Protein Array Analysis / Q000379:methods; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D011494:Protein Kinases / Q000032:analysis / Q000378:metabolism; D011506:Proteins / Q000032:analysis / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods
D011506:Proteins; D011494:Protein Kinases; D010744:Phosphoric Monoester Hydrolases
Computational model; Fluorescent biosensor; Mass cytometry; Mass spectrometry; Phosphoproteomics; Phosphorylation network; Protein kinase; Protein microarray; Signaling network; Systems biology