Integration of Proteomic, Transcriptional, and Interactome Data Reveals Hidden Signaling Components
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Fraenkel_Ingregration Proteomic.pdf
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1.69 MB
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Author(s)
Huang, Shao-shan Carol
Fraenkel, Ernest
Alternative Title
Integrating Proteomic, Transcriptional, and Interactome Data Reveals Hidden Components of Signaling and Regulatory Networks
Date Issued
July 2009
Journal
Science Signaling
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Huang, S.-s. C., and E. Fraenkel. “Integrating Proteomic, Transcriptional, and Interactome Data Reveals Hidden Components of Signaling and Regulatory Networks.” Science Signaling 2.81 (2009): ra40–ra40.
Version
Author's final manuscript
Abstract
Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the majority of hits identified by transcriptional, proteomic, and genetic assays lie outside of the expected pathways. These unexpected components of the cellular response are often the most interesting, because they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree problem, that uses previously reported protein-protein and protein-DNA interactions to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. Applied simultaneously to phosphoproteomic and transcriptional data for the yeast pheromone response, it identifies changes in diverse cellular processes that extend far beyond the expected pathways.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1126/scisignal.2000350
