Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2

Anatoly Sorokin, Nicolas Le Novère, Augustin Luna, Tobias Czauderna, Emek Demir, Robin Haw, Huaiyu Mi, Stuart Moodie, Falk Schreiber, Alice Villéger

    Research output: Contribution to journalArticleResearchpeer-review

    5 Citations (Scopus)

    Abstract

    The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

    Original languageEnglish
    Article number264
    Pages (from-to)281-340
    Number of pages59
    JournalJournal of integrative bioinformatics
    Volume12
    Issue number2
    DOIs
    Publication statusPublished - 2015

    Cite this

    Sorokin, Anatoly ; Le Novère, Nicolas ; Luna, Augustin ; Czauderna, Tobias ; Demir, Emek ; Haw, Robin ; Mi, Huaiyu ; Moodie, Stuart ; Schreiber, Falk ; Villéger, Alice. / Systems Biology Graphical Notation : Entity Relationship language Level 1 Version 2. In: Journal of integrative bioinformatics. 2015 ; Vol. 12, No. 2. pp. 281-340.
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    abstract = "The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.",
    author = "Anatoly Sorokin and {Le Nov{\`e}re}, Nicolas and Augustin Luna and Tobias Czauderna and Emek Demir and Robin Haw and Huaiyu Mi and Stuart Moodie and Falk Schreiber and Alice Vill{\'e}ger",
    year = "2015",
    doi = "10.2390/biecoll-jib-2015-264",
    language = "English",
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    Sorokin, A, Le Novère, N, Luna, A, Czauderna, T, Demir, E, Haw, R, Mi, H, Moodie, S, Schreiber, F & Villéger, A 2015, 'Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2', Journal of integrative bioinformatics, vol. 12, no. 2, 264, pp. 281-340. https://doi.org/10.2390/biecoll-jib-2015-264

    Systems Biology Graphical Notation : Entity Relationship language Level 1 Version 2. / Sorokin, Anatoly; Le Novère, Nicolas; Luna, Augustin; Czauderna, Tobias; Demir, Emek; Haw, Robin; Mi, Huaiyu; Moodie, Stuart; Schreiber, Falk; Villéger, Alice.

    In: Journal of integrative bioinformatics, Vol. 12, No. 2, 264, 2015, p. 281-340.

    Research output: Contribution to journalArticleResearchpeer-review

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    AU - Sorokin, Anatoly

    AU - Le Novère, Nicolas

    AU - Luna, Augustin

    AU - Czauderna, Tobias

    AU - Demir, Emek

    AU - Haw, Robin

    AU - Mi, Huaiyu

    AU - Moodie, Stuart

    AU - Schreiber, Falk

    AU - Villéger, Alice

    PY - 2015

    Y1 - 2015

    N2 - The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

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