Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data
Ali Sinan Köksal,
Kirsten Beck,
Dylan R. Cronin,
Aaron McKenna,
Nathan D. Camp,
Saurabh Srivastava,
Matthew E. MacGilvray,
Rastislav Bodík,
Alejandro Wolf-Yadlin,
Ernest Fraenkel,
Jasmin Fisher,
Anthony Gitter
Affiliations
Ali Sinan Köksal
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
Kirsten Beck
Department of Genome Sciences, University of Washington, Seattle, WA, USA
Dylan R. Cronin
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
Aaron McKenna
Department of Genome Sciences, University of Washington, Seattle, WA, USA
Nathan D. Camp
Department of Genome Sciences, University of Washington, Seattle, WA, USA
Saurabh Srivastava
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
Matthew E. MacGilvray
Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
Rastislav Bodík
Paul G. Allen Center for Computer Science and Engineering, University of Washington, Seattle, WA, USA
Alejandro Wolf-Yadlin
Department of Genome Sciences, University of Washington, Seattle, WA, USA
Ernest Fraenkel
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Jasmin Fisher
Microsoft Research, Cambridge, UK; Department of Biochemistry, University of Cambridge, Cambridge, UK
Anthony Gitter
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; Morgridge Institute for Research, Madison, WI, USA; Corresponding author
Summary: 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. : Köksal et al. present a computational technique, the temporal pathway synthesizer (TPS), that combines time series global phosphoproteomic data and protein-protein interaction networks to reconstruct the vast signaling pathways that control post-translational modifications. Keywords: protein-protein interactions, time series phosphorylation, network algorithm, program synthesis, mass spectrometry
