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      SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

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          Abstract

          Background

          Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.

          Results

          We present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.

          Conclusions

          SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

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          Most cited references73

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          The Systems Biology Graphical Notation.

          Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
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            Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle.

            To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks. Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge. The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (http://gin.univ-mrs.fr/GINsim). Higher resolution state transitions graphs are also found on this web site (Model Repository page).
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              The Yeast Cell-Cycle Network Is Robustly Designed

              , , (2003)
              The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the cell-cycle regulatory network of the budding yeast. With the use of a simple dynamical model, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state--the G1 state--is a global attractor of the dynamics. The biological pathway--the cell-cycle sequence of protein states--is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.
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                Author and article information

                Journal
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central
                1752-0509
                2013
                10 December 2013
                : 7
                : 135
                Affiliations
                [1 ]Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
                [2 ]Institut de Mathématiques de Luminy, Campus de Luminy, Case 907, 13288 Marseille Cedex 9, France
                [3 ]European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
                [4 ]Center for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland
                [5 ]The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
                [6 ]Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA
                [7 ]Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany
                [8 ]College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
                [9 ]Swiss-Prot & Vital-IT group, SIB- Swiss Institute of Bioinformatics, Center for Integrative Genomics, University of Lausanne, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland
                [10 ]INRIA Grenoble – Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France
                [11 ]IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, 38040 Grenoble Cedex 9, France
                [12 ]Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento (INESC-ID), Rua Alves Redol 9, 1000-029 Lisbon, Portugal
                [13 ]Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
                [14 ]Computing and Mathematical sciences, California Institute of Technology, Pasadena, CA 91125, USA
                [15 ]Institut de Biologie de l’Ecole Normale Supérieure (IBENS) - UMR CNRS 8197 - INSERM 1024 46 rue d’Ulm, 75230 Paris Cedex 05, France
                [16 ]Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
                Article
                1752-0509-7-135
                10.1186/1752-0509-7-135
                3892043
                24321545
                a9687b6a-73b7-43c3-9882-a911f2ba304c
                Copyright © 2013 Chaouiya et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 August 2013
                : 26 November 2013
                Categories
                Methodology Article

                Quantitative & Systems biology
                Quantitative & Systems biology

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