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<title lang=en>Identifying Functional Families of Trajectories in Biological Pathways by Soft Clustering: Application to TGF-β Signaling</title>
<creator>Coquet, Jean</creator>
<creator>Théret, Nathalie</creator>
<creator>Legagneux, Vincent</creator>
<creator>Dameron, Olivier</creator>
<contributor>Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss) ; Inria Rennes – Bretagne Atlantique ; Institut National de Recherche en Informatique et en Automatique (Inria) - Institut National de Recherche en Informatique et en Automatique (Inria) - GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA_D7) ; Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA) ; Université de Rennes 1 (UR1) - Institut National des Sciences Appliquées - Rennes (INSA Rennes) - Université de Bretagne Sud (UBS) - École normale supérieure - Rennes (ENS Rennes) - Institut National de Recherche en Informatique et en Automatique (Inria) - CentraleSupélec - Centre National de la Recherche Scientifique (CNRS) - IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique) - Université de Rennes 1 (UR1) - Institut National des Sciences Appliquées - Rennes (INSA Rennes) - Université de Bretagne Sud (UBS) - École normale supérieure - Rennes (ENS Rennes) - Institut National de Recherche en Informatique et en Automatique (Inria) - CentraleSupélec - Centre National de la Recherche Scientifique (CNRS) - IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique) - Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA) ; Université de Rennes 1 (UR1) - Institut National des Sciences Appliquées - Rennes (INSA Rennes) - Université de Bretagne Sud (UBS) - École normale supérieure - Rennes (ENS Rennes) - Institut National de Recherche en Informatique et en Automatique (Inria) - CentraleSupélec - Centre National de la Recherche Scientifique (CNRS) - IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique) - Université de Rennes 1 (UR1) - Institut National des Sciences Appliquées - Rennes (INSA Rennes) - Université de Bretagne Sud (UBS) - École normale supérieure - Rennes (ENS Rennes) - CentraleSupélec - Centre National de la Recherche Scientifique (CNRS) - IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)</contributor>
<contributor>Institut de recherche, santé, environnement et travail [Rennes] (Irset) ; Université d'Angers (UA) - Université des Antilles et de la Guyane (UAG) - Université de Rennes 1 (UR1) - École des Hautes Études en Santé Publique [EHESP] (EHESP) - Institut National de la Santé et de la Recherche Médicale (INSERM) - Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )</contributor>
<description>International audience</description>
<source>CMSB 2017 - 15th International Conference on Computational Methods in Systems Biology</source>
<coverage>Darmstadt, France</coverage>
<identifier>hal-01559249</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01559249</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01559249/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01559249/file/JeanCoquet_CMSB.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01559249</source>
<source>CMSB 2017 - 15th International Conference on Computational Methods in Systems Biology, Sep 2017, Darmstadt, France. pp.17, Lecture Notes in Computer Sciences</source>
<language>en</language>
<subject lang=en>RSC model</subject>
<subject lang=en>TGF-β</subject>
<subject lang=en>Signaling pathways</subject>
<subject lang=en>Discrete dynamic model</subject>
<subject lang=en>Soft clustering</subject>
<subject>[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>The study of complex biological processes requires to forgo simplified models for extensive ones. Yet, these models' size and complexity place them beyond understanding. Their analysis requires new methods for identifying general patterns. The Transforming Growth Factor TGF-β is a multifunctional cytokine that regulates mammalian cell development , differentiation, and homeostasis. Depending on the context, it can play the antagonistic roles of growth inhibitor or of tumor promoter. Its context-dependent pleiotropic nature is associated with complex sig-naling pathways. The most comprehensive model of TGF-β-dependent signaling is composed of 15,934 chains of reactions (trajectories) linking TGF-β to at least one of its 159 target genes. Identifying functional patterns in such a network requires new automated methods. This article presents a framework for identifying groups of similar trajec-tories composed of the same molecules using an exhaustive and without prior assumptions approach. First, the trajectories were clustered using the Relevant Set Correlation model, a shared nearest-neighbors clustering method. Five groups of trajectories were identified. Second, for each cluster the over-represented molecules were determined by scoring the frequency of each molecule implicated in trajectories. Third, Gene set enrichment analysis on the clusters of trajectories revealed some specific TGF-β-dependent biological processes, with different clusters associated to the antagonists roles of TGF-β. This confirms that our approach yields biologically-relevant results. We developed a web interface that facilitates graph visualization and analysis. Our clustering-based method is suitable for identifying families of functionally-similar trajectories in the TGF-β signaling network. It can be generalized to explore any large-scale biological pathways.</description>
<date>2017-09-27</date>
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