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      Pathway databases and tools for their exploitation: benefits, current limitations and challenges

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          Abstract

          In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue.

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          Most cited references47

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Modelling and analysis of gene regulatory networks.

            Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.
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              The Universal Protein Resource (UniProt)

              The Universal Protein Resource (UniProt) provides a stable, comprehensive, freely accessible, central resource on protein sequences and functional annotation. The UniProt Consortium is a collaboration between the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt website, and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of four major components, each optimized for different uses: the UniProt Knowledgebase, the UniProt Reference Clusters, the UniProt Archive and the UniProt Metagenomic and Environmental Sequences database. UniProt is updated and distributed every three weeks, and can be accessed online for searches or download at http://www.uniprot.org.
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                Author and article information

                Journal
                Mol Syst Biol
                Molecular Systems Biology
                Nature Publishing Group
                1744-4292
                2009
                28 July 2009
                : 5
                : 290
                Affiliations
                [1 ]Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, Spain
                Author notes
                [a ]Research Unit on Biomedical Informatics, Universitat Pompeu Fabra, IMIM-Hospital del Mar, PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain. Tel.: +34 9331 60521; Fax: +34 9331 60550; lfurlong@ 123456imim.es
                Article
                msb200947
                10.1038/msb.2009.47
                2724977
                19638971
                1f9f12f0-14b5-495d-a69a-20c4b94ee87d
                Copyright © 2009, EMBO and Nature Publishing Group

                This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.

                History
                : 19 March 2009
                : 15 June 2009
                Page count
                Pages: 1
                Categories
                Perspectives

                Quantitative & Systems biology
                systems biology,biological pathways,cell signalling,network models,pathway databases

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