Directed random walks and constraint programming reveal active pathways in hepatocyte growth factor signaling

Autor: Aristotelis Kittas, Kai Breuhahn, Carito Guziolowski, Sabrina Schmitt, Aurélien Delobelle, Niels Grabe
Přispěvatelé: Department of Physics, Aristotle University of Thessaloniki, Molecular Hepatology - Alcohol Associated Diseases, Department of Medicine II, University of Heidelberg, Medical Faculty of Mannheim-University of Heidelberg, Medical Faculty of Mannheim, Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Institute for Pathology Heidelberg, Heidelberg University Hospital [Heidelberg]
Rok vydání: 2015
Předmět:
Zdroj: FEBS Journal
FEBS Journal, Wiley, 2016, 283 (2), pp.350-360. ⟨10.1111/febs.13580⟩
ISSN: 1742-4658
1742-464X
DOI: 10.1111/febs.13580⟩
Popis: An effective means to analyze mRNA expression data is to take advantage of established knowledge from pathway databases, using methods such as pathway-enrichment analyses. However, pathway databases are not case-specific and expression data could be used to infer gene-regulation patterns in the context of specific pathways. In addition, canonical pathways may not always describe the signaling mechanisms properly, because interactions can frequently occur between genes in different pathways. Relatively few methods have been proposed to date for generating and analyzing such networks, preserving the causality between gene interactions and reasoning over the qualitative logic of regulatory effects. We present an algorithm (MCWalk) integrated with a logic programming approach, to discover subgraphs in large-scale signaling networks by random walks in a fully automated pipeline. As an exemplary application, we uncover the signal transduction mechanisms in a gene interaction network describing hepatocyte growth factor-stimulated cell migration and proliferation from gene-expression measured with microarray and RT-qPCR using in-house perturbation experiments in a keratinocyte-fibroblast co-culture. The resulting subgraphs illustrate possible associations of hepatocyte growth factor receptor c-Met nodes, differentially expressed genes and cellular states. Using perturbation experiments and Answer Set programming, we are able to select those which are more consistent with the experimental data. We discover key regulator nodes by measuring the frequency with which they are traversed when connecting signaling between receptors and significantly regulated genes and predict their expression-shift consistently with the measured data. The Java implementation of MCWalk is publicly available under the MIT license at: https://bitbucket.org/akittas/biosubg.
Databáze: OpenAIRE
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