Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data

Overview

Journal Cell Rep

Publisher Cell Press

Specialties Biology
Cell Biology
Molecular Biology

Date 2018 Sep 27

PMID 30257219

Citations 17

Authors

Ali Sinan Koksal

Kirsten Beck

Dylan R Cronin

Aaron McKenna

Nathan D Camp

Saurabh Srivastava

Matthew E MacGilvray

Rastislav Bodik

Alejandro Wolf-Yadlin

Ernest Fraenkel

Jasmin Fisher

Anthony Gitter

Affiliations

Soon will be listed here.

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.

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