Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem.

TitleSimultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem.
Publication TypeJournal Article
Year of Publication2013
AuthorsTuncbag, N, Braunstein, A, Pagnani, A, Huang, S-SC, Chayes, J, Borgs, C, Zecchina, R, Fraenkel, E
JournalJ Comput Biol
Date Published2013 Feb
KeywordsAlgorithms, Brain Neoplasms, Cell Communication, Gene Expression Profiling, Gene Regulatory Networks, Glioblastoma, Humans, Models, Biological, Neoplasm Proteins, Pharmacogenetics, Pheromones, Protein Interaction Mapping, Receptors, Cell Surface, Saccharomyces cerevisiae, Signal Transduction

Signaling and regulatory networks are essential for cells to control processes such as growth, differentiation, 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 and to propose new therapeutic strategies. We overcome these problems and use "omic" data to reconstruct simultaneously 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. Although the algorithm was not provided with any information about the phosphorylation status of receptors, it identifies a small set of clinically relevant receptors among hundreds present in the interactome.

Alternate JournalJ. Comput. Biol.
PubMed ID23383998
PubMed Central IDPMC3576906
Grant ListR01 GM089903 / GM / NIGMS NIH HHS / United States
R01GM089903 / GM / NIGMS NIH HHS / United States
U54 CA112967 / CA / NCI NIH HHS / United States
U54CA112967 / CA / NCI NIH HHS / United States