Abstract
Mapping intra-cellular signaling networks is a critical step in developing an understanding of and treatments for many devastating diseases. The predominant ways of discovering pathways in these networks are knockout and pharmacological inhibition experiments. However, experimental evidence for new pathways can be difficult to explain within existing maps of signaling networks.
In this paper, we present a novel computational method that integrates pharmacological intervention experiments with protein interaction data in order to predict new signaling pathways that explain unexpected experimental results. Biologists can use these hypotheses to design experiments to further elucidate underlying signaling mechanisms or to directly augment an existing signaling network model.
When applied to experimental results from human breast cancer cells targeting the epidermal growth factor receptor (EGFR) network, our method proposes several new, biologically-viable pathways that explain the evidence for a new signaling pathway. These results demonstrate that the method has potential for aiding biologists in generating hypothetical pathways to explain experimental findings.
Our method is implemented as part of the PathwayOracle toolkit and is available from the authors upon request.
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Ruths, D., Tseng, JT., Nakhleh, L., Ram, P.T. (2007). De Novo Signaling Pathway Predictions Based on Protein-Protein Interaction, Targeted Therapy and Protein Microarray Analysis. In: Ideker, T., Bafna, V. (eds) Systems Biology and Computational Proteomics. RSB RCP 2006 2006. Lecture Notes in Computer Science(), vol 4532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73060-6_8
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DOI: https://doi.org/10.1007/978-3-540-73060-6_8
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