Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem
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Fraenkel_Simultaneous with SI.pdf
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Author(s)
Tuncbag, Nurcan
Braunstein, Alfredo
Pagnani, Andrea
Huang, Shao-Shan Carol
Chayes, Jennifer
Borgs, Christian
Zecchina, Riccardo
Fraenkel, Ernest
Tuncbag, Nurcan
Huang, Shao-Shan Carol
Date Issued
April 2012
Journal
Proceedings of the International Conference on Research in Computational Molecular Biology, RECOMB 2012
Publisher
Springer Science + Business Media B.V.
Citation
Tuncbag, Nurcan, Alfredo Braunstein, Andrea Pagnani, Shao-Shan Carol Huang, Jennifer Chayes, Christian Borgs, Riccardo Zecchina, and Ernest Fraenkel. Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem. In Research in Computational Molecular Biology. Benny Chor, ed. Pp. 287–301. Berlin: Springer-Verlag Berlin Heidelberg, 2012. (Lecture notes in computer science; 7262).
Version
Author's final manuscript
Abstract
Signaling networks are essential for cells to control processes such as growth and response to stimuli. Although many “omic” data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases. We overcome these problems and use “omic” data to simultaneously reconstruct multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees each of which is rooted in a different cell surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies.
MIT Department
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/10.1007/978-3-642-29627-7_31
