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    <title>Nature Precedings - Tag feed for pathway</title>
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      <title>Systems Biology Graphical Notation: Activity Flow language Level 1</title>
      <link>http://dx.doi.org/10.1038/npre.2009.3724.1</link>
      <description>Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialized notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Descriptions, the Entity Relationships and the Activity Flows. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage ranging from textbooks and teaching in high schools to peer reviewed articles in scientific journals. The first level of the SBGN Activity Flow language has been publicly released. Shared by the communities of biochemists, genomic scientists, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signaling pathways, metabolic networks and gene regulatory maps.</description>
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      <pubDate>Mon, 07 Sep 2009 19:43:48 UTC</pubDate>
      <dc:title>Systems Biology Graphical Notation: Activity Flow language Level 1</dc:title>
      <dc:identifier>doi:10.1038/npre.2009.3724.1</dc:identifier>
      <dc:date>2009-09-07</dc:date>
      <dc:creator>Huaiyu Mi</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2009-09-07T19:43:48Z</prism:publicationDate>
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      <prism:section>Bioinformatics</prism:section>
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      <title>Systems Biology Graphical Notation:  Process Description language Level 1</title>
      <link>http://dx.doi.org/10.1038/npre.2009.3721.1</link>
      <description>Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps.</description>
      <guid>http://dx.doi.org/10.1038/npre.2009.3721.1</guid>
      <pubDate>Mon, 07 Sep 2009 07:52:01 UTC</pubDate>
      <dc:title>Systems Biology Graphical Notation:  Process Description language Level 1</dc:title>
      <dc:identifier>doi:10.1038/npre.2009.3721.1</dc:identifier>
      <dc:date>2009-09-07</dc:date>
      <dc:creator>Stuart L. Moodie</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2009-09-07T07:52:01Z</prism:publicationDate>
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      <prism:section>Biotechnology</prism:section>
      <prism:section>Bioinformatics</prism:section>
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      <title>Systems Biology Graphical Notation: Entity Relationship language Level 1</title>
      <link>http://dx.doi.org/10.1038/npre.2009.3719.1</link>
      <description>Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Descriptions, the Entity Relationships and the Activity Flows. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Entity Relationship language has been publicly released. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps.</description>
      <guid>http://dx.doi.org/10.1038/npre.2009.3719.1</guid>
      <pubDate>Fri, 04 Sep 2009 15:21:22 UTC</pubDate>
      <dc:title>Systems Biology Graphical Notation: Entity Relationship language Level 1</dc:title>
      <dc:identifier>doi:10.1038/npre.2009.3719.1</dc:identifier>
      <dc:date>2009-09-04</dc:date>
      <dc:creator>Nicolas Le Nov&#232;re</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2009-09-04T15:21:22Z</prism:publicationDate>
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      <prism:section>Bioinformatics</prism:section>
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      <title>ChlamyCyc &amp;#8211; a comprehensive database and web-portal centered on Chlamydomonas reinhardtii</title>
      <link>http://dx.doi.org/10.1038/npre.2009.3108.1</link>
      <description>Background &amp;#8211; The unicellular green alga Chlamydomonas reinhardtii is an important eukaryotic model organism for the study of photosynthesis and growth, as well as flagella development and other cellular processes. In the era of high-throughput technologies there is an imperative need to integrate large-scale data sets from high-throughput experimental techniques using computational methods and database resources to provide comprehensive information about the whole cellular system of a single organism.Results &amp;#8211; In the framework of the German Systems Biology initiative GoFORSYS a pathway/genome database and web-portal for Chlamydomonas reinhardtii (ChlamyCyc) was established, which currently features about 270 metabolic pathways with related genes, enzymes, and compound information. ChlamyCyc was assembled using an integrative approach combining the recently published genome sequence, bioinformatics methods, and experimental data from metabolomics and proteomics experiments. We analyzed and integrated a combination of primary and secondary database resources, such as existing genome annotations from JGI, EST collections, orthology information, and MapMan classification.Conclusion &amp;#8211; Chlamycyc provides a curated and integrated systems biology repository that will enable and assist in systematic studies of fundamental cellular processes in Chlamydomonas reinhardtii. The ChlamyCyc database and web-portal is freely available under http://chlamycyc.mpimp-golm.mpg.de.</description>
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      <pubDate>Tue, 21 Apr 2009 17:08:33 UTC</pubDate>
      <dc:title>ChlamyCyc &amp;#8211; a comprehensive database and web-portal centered on Chlamydomonas reinhardtii</dc:title>
      <dc:identifier>doi:10.1038/npre.2009.3108.1</dc:identifier>
      <dc:date>2009-04-21</dc:date>
      <dc:creator>Jan-Ole  Christian</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2009-04-21T17:08:33Z</prism:publicationDate>
      <prism:category>Poster</prism:category>
      <prism:section>Genetics &amp; Genomics</prism:section>
      <prism:section>Bioinformatics</prism:section>
      <prism:section>Plant Biology</prism:section>
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      <title>Systems Biology Graphical Notation: Process Diagram Level 1</title>
      <link>http://precedings.nature.com/documents/2320/version/1</link>
      <description>Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps.</description>
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      <pubDate>Tue, 23 Sep 2008 20:07:50 UTC</pubDate>
      <dc:title>Systems Biology Graphical Notation: Process Diagram Level 1</dc:title>
      <dc:identifier>hdl:10101/npre.2008.2320.1</dc:identifier>
      <dc:date>2008-09-23</dc:date>
      <dc:creator>Nicolas Le Novere</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2008-09-23T20:07:50Z</prism:publicationDate>
      <prism:category>Manuscript</prism:category>
      <prism:section>Biotechnology</prism:section>
      <prism:section>Bioinformatics</prism:section>
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      <title>PID: The Pathway Interaction Database</title>
      <link>http://precedings.nature.com/documents/2243/version/1</link>
      <description>The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the U.S. National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists, and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest.  In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.</description>
      <guid>http://precedings.nature.com/documents/2243/version/1</guid>
      <pubDate>Fri, 29 Aug 2008 10:33:26 UTC</pubDate>
      <dc:title>PID: The Pathway Interaction Database</dc:title>
      <dc:identifier>hdl:10101/npre.2008.2243.1</dc:identifier>
      <dc:date>2008-11-18</dc:date>
      <dc:creator>Kira Anthony</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2008-08-29T10:33:26Z</prism:publicationDate>
      <prism:category>Manuscript</prism:category>
      <prism:section>Cancer</prism:section>
      <prism:section>Molecular Cell Biology</prism:section>
      <prism:section>Bioinformatics</prism:section>
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      <title>Reactome &amp;#8211; a knowledgebase of human biological pathways</title>
      <link>http://dx.doi.org/10.1038/npre.2007.1281.1</link>
      <description>Pathway curation is a powerful tool for systematically associating gene products with functions. Reactome (www.reactome.org) is a manually curated human pathway knowledgebase describing a wide range of biological processes in a computationally accessible manner. The core unit of the Reactome data model is the Reaction, whose instances form a network of biological interactions through entities that are consumed, produced, or act as catalysts. Entities are distinguished by their molecular identities and cellular locations. Set objects allow grouping of related entities. Curation is based on communication between expert authors and staff curators, facilitated by freely available data entry tools. Manually curated data are subjected to quality control and peer review by a second expert. Reactome data are released quarterly. At release time, electronic orthology inference performed on human data produces reaction predictions in 22 species ranging from mouse to bacteria. Cross-references to a large number of publicly available databases are attached, providing multiple entry points into the database. The Reactome Mart allows query submission and data retrieval from Reactome and across other databases. The SkyPainter tool provides visualization and statistical analysis of user supplied data, e.g. from microarray experiments. Reactome data are freely available in a number of data formats (e.g. BioPax, SBML).</description>
      <guid>http://dx.doi.org/10.1038/npre.2007.1281.1</guid>
      <pubDate>Wed, 31 Oct 2007 21:20:37 UTC</pubDate>
      <dc:title>Reactome &amp;#8211; a knowledgebase of human biological pathways</dc:title>
      <dc:identifier>doi:10.1038/npre.2007.1281.1</dc:identifier>
      <dc:date>2009-05-07</dc:date>
      <dc:creator>Peter D'Eustachio</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2007-10-31T21:20:37Z</prism:publicationDate>
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      <prism:section>Molecular Cell Biology</prism:section>
      <prism:section>Bioinformatics</prism:section>
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      <title>DNA expression microarrays may be the wrong tool to identify biological pathways </title>
      <link>http://dx.doi.org/10.1038/npre.2007.1036.1</link>
      <description>DNA microarray expression signatures are expected to provide new insights into patho- physiological pathways. Numerous variant statistical methods have been described for each step of the signal analysis. We employed five similar statistical tests on the same data set at the level of gene selection. Inter-test agreement for the identification of biological pathways in BioCarta, KEGG and Reactome was calculated using Cohen&#8217;s k- score. The identification of specific biological pathways showed only moderate agreement (0.30 &lt; k &lt; 0.79) between the analysis methods used.  Pathways identified by microarrays must be treated cautiously as they vary according to the statistical method used. </description>
      <guid>http://dx.doi.org/10.1038/npre.2007.1036.1</guid>
      <pubDate>Wed, 19 Sep 2007 17:07:01 UTC</pubDate>
      <dc:title>DNA expression microarrays may be the wrong tool to identify biological pathways </dc:title>
      <dc:identifier>doi:10.1038/npre.2007.1036.1</dc:identifier>
      <dc:date>2007-09-19</dc:date>
      <dc:creator>Adrian Mondry</dc:creator>
      <prism:publicationName>Nature Precedings</prism:publicationName>
      <prism:publicationDate>2007-09-19T17:07:01Z</prism:publicationDate>
      <prism:category>Manuscript</prism:category>
      <prism:section>Biotechnology</prism:section>
      <prism:section>Genetics &amp; Genomics</prism:section>
      <prism:section>Molecular Cell Biology</prism:section>
      <prism:section>Pharmacology</prism:section>
      <prism:section>Bioinformatics</prism:section>
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