Signal transduction system is known to widely regulate complex biological events such as cell proliferation and differentiation. As phosphotyrosine-dependent signaling networks play a key role in transmitting signals, a comprehensive description of their dynamics would contribute substantially toward understanding the regulatory mechanisms that result in each biological effect. Recent proteomics approaches based on highly sensitive LC-MS/MS technology have enabled us to obtain large-scale quantitative information on the focused proteome such as phosphoproteome. Based on the SILAC technology, we developed a simple method for making a temporal quantitative analysis of phosphotyrosine-related proteins. In order to automate quantitation based on large volumes of nanoLC-MS/MS proteome data, we also developed software named AYUMS (Saito et al. 2007 BMC Bioinformatics 8, 15). This software has enabled us to obtain the activation profiles of signaling molecules in a high-throughput fashion, providing detailed quantitative information with the high time-resolution necessary for a systems biology approach. Sophisticated computational analysis of quantitative phosphoproteome data can lead us to grasp a system-level view of complex regulatory networks in signal transduction. We tried bioinformatical estimation of the well-known epidermal growth factor receptor pathway from the time-resolved phosphoproteome data (Tasaki et al. 2006 Genome Inform. 17, 226-238). We constructed an EFGR signal transduction pathway model based on the biological data available in the literature. The kinetic parameters were then determined in the data assimilation framework with some manual tuning so as to fit the proteome data. Our in silico model was further refined by adding or removing some regulatory loops, providing knowledge about some possible regulation that had not been inferred or confirmed experimentally.
