Signal transduction systems are known to widely regulate complex biological events such as cell proliferation and differentiation. Although numerous biological analyses have revealed many of the key molecules and events involved in cell signaling, an integrative view of this complicated system cannot provide a fundamental theory on the regulation of the entire network without analyzing the dynamic behavior of these molecules and events at the system level. Recent technological advances in mass spectrometry-based proteomics and bioinformatics have enabled us to obtain a networkwide description of signaling dynamics through the large-scale identification and quantification of phosphorylated molecules. Accordingly, computational modeling on the basis of dynamic proteomics data has also been applied to the network analysis of representative signaling systems such as the epidermal growth factor receptor pathway. This review focuses on the current status of quantitative proteomics technology for temporal studies of signal transduction and on the application of comprehensive signaling dynamics data to mathematical analyses of regulatory networks. The perspective on proteomics data-driven systems biology is also discussed.
Keywords: Signal transduction, quantitative proteomics, systems biology, LC-MS/MS, computational modeling
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