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- W2247165656 abstract "B46 Biological signaling networks are complex dynamical systems, the analysis of which is greatly facilitated when mathematical modeling is used in combination with traditional experimental techniques. High throughput genomics and proteomics provide clustering data based on co-expression of different molecules without a clear understanding of relationships and the resulting system properties of these networks. Mathematical modeling enables quantitative understanding of the signaling dynamics, but their use is limited due to an incomplete knowledge of the molecular interactions that define the causal network topology. In this work, we have reconstructed the signal transduction network through receptor tyrosine kinases (EGFR and IGFR) by overlaying consensus networks onto Reverse Phase Protein Array (RPPA) data. We have also developed a detailed mathematical model that describes the dynamics of the reconstructed network. The model was employed as a predictive tool to: a) determine signaling network response to perturbations of targeted nodes, b) understand the dynamic properties of the de novo regulatory loops and c) indentify optimal combinations of targeted agents that more effectively disrupted the loops responsible for activation of key signaling intermediates. The modeling predictions were experimentally tested in breast cancer cell lines using immunoblotting and found in good agreement with the data. Therefore, overlying causal networks onto genomics and proteomics data provide a more detailed systems approach to understand the dynamics of complex signaling networks. Moreover, further implementation of our study on the EGFR/IGFR signaling network will lead to the development of new combinations of targeted pharmaceutical agents for cancer treatment and prevention." @default.
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- W2247165656 date "2008-10-01" @default.
- W2247165656 modified "2023-09-26" @default.
- W2247165656 title "Phospho proteomics and systems reconstruction of signaling networks to identify targets for therapy" @default.
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