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Cell Rep. 2018 Sep 25;24(13):3607-3618. doi: 10.1016/j.celrep.2018.08.085.

Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data.

Author information

1
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
2
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
3
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Department of Biological Sciences, Bowling Green State University, Bowling Green, OH, USA.
4
Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA.
5
Paul G. Allen Center for Computer Science and Engineering, University of Washington, Seattle, WA, USA.
6
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
7
Microsoft Research, Cambridge, UK; Department of Biochemistry, University of Cambridge, Cambridge, UK.
8
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Morgridge Institute for Research, Madison, WI, USA. Electronic address: gitter@biostat.wisc.edu.

Abstract

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.

KEYWORDS:

mass spectrometry; network algorithm; program synthesis; protein-protein interactions; time series phosphorylation

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