Pathways on demand: automated reconstruction of human signaling networks

Signaling pathways are a cornerstone of systems biology. Several databases store high-quality representations of these pathways that are amenable for automated analyses. Despite painstaking and manual curation, these databases remain incomplete. We present P ATH L INKER , a new computational method to reconstruct the interactions in a signaling pathway of interest. P ATH L INKER efficiently computes multiple short paths from the receptors to transcriptional regulators (TRs) in a pathway within a background protein interaction network. We use P ATH L INKER to accurately reconstruct a comprehensive set of signaling pathways from the NetPath and KEGG databases. We show that P ATH L INKER has higher precision and recall than several state-of-the-art algorithms, while also ensuring that the resulting network connects receptor proteins to TRs. P ATH L INKER ’s reconstruction of the Wnt pathway identified CFTR, an ABC class chloride ion channel transporter, as a novel intermediary that facilitates the signaling of Ryk to Dab2, which are known components of Wnt/β-catenin signaling. In HEK293 cells, we show that the Ryk–CFTR–Dab2 path is a novel amplifier of β-catenin signaling specifically in response to Wnt 1, 2, 3, and 3a of the 11 Wnts tested. P ATH L INKER captures the structure of signaling pathways as represented in pathway databases better than existing methods. P ATH L INKER ’s success in reconstructing pathways from NetPath and KEGG databases point to its applicability for complementing manual curation of these databases. P ATH L INKER may serve as a promising approach for prioritizing proteins and interactions for experimental study, as illustrated by its discovery of a novel pathway in Wnt/β-catenin signaling. Our supplementary website at http://bioinformatics.cs.vt.edu/~murali/supplements/2016-sys-bio-applications-pathlinker/ provides links to the P ATH L INKER software, input datasets, P ATH L INKER reconstructions of NetPath pathways, and links to interactive visualizations of these reconstructions on GraphSpace. Signaling pathways: Computing the best routes An algorithm that predicts molecular signaling pathways in humans provides a powerful way to 'join the dots' within protein networks. An important goal for systems biology is to identify the chains of reactions that carry cellular signals from receptors to the transcriptional regulators (TRs) that orchestrate gene activity. T. M. Murali, Anna Ritz, Shiv Kale and co-workers at Virginia Tech develo