4.8 Article

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Journal

CELL REPORTS
Volume 24, Issue 13, Pages 3607-3618

Publisher

CELL PRESS
DOI: 10.1016/j.celrep.2018.08.085

Keywords

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Funding

  1. Microsoft Research
  2. NSF [DBI-1553206, CCF-1139138, CCF-1337415, ACI-1535191]
  3. NIH [U54-AI117924, T32-HL007312, T32-HG002760, U01-CA184898, U54-NS091046]
  4. DOE [FOA-0000619]
  5. DARPA [FA8750-14-C-0011, FA8750-16-2-0032]
  6. Office of Advanced Cyberinfrastructure (OAC)
  7. Direct For Computer & Info Scie & Enginr [1535191] Funding Source: National Science Foundation

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We present a method for automatically discovering signaling pathways from time-resolved phosphoproteomic data. The Temporal Pathway Synthesizer (TPS) algorithm uses constraint-solving techniques first developed in the context of formal verification to explore paths in an interaction network. It systematically eliminates all candidate structures for a signaling pathway where a protein is activated or inactivated before its upstream regulators. The algorithm can model more than one hundred thousand dynamic phosphosites and can discover pathway members that are not differentially phosphorylated. By analyzing temporal data, TPS defines signaling cascades without needing to experimentally perturb individual proteins. It recovers known pathways and proposes pathway connections when applied to the human epidermal growth factor and yeast osmotic stress responses. Independent kinase mutant studies validate predicted substrates in the TPS osmotic stress pathway.

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