Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data
Author(s) -
Ali Sinan Köksal,
Kirsten Beck,
Dylan Cronin,
Aaron McKenna,
Nathan D. Camp,
Saurabh Srivastava,
Matthew E. MacGilvray,
Rastislav Bodík,
Alejandro WolfYadlin,
Ernest Fraenkel,
Jasmin Fisher,
Anthony Gitter
Publication year - 2018
Publication title -
cell reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.264
H-Index - 154
eISSN - 2639-1856
pISSN - 2211-1247
DOI - 10.1016/j.celrep.2018.08.085
Subject(s) - phosphoproteomics , signal transduction , computational biology , context (archaeology) , phosphorylation , biology , biological pathway , computer science , microbiology and biotechnology , protein kinase a , biochemistry , protein phosphorylation , gene , paleontology , gene expression
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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