http://precedings.nature.com/tags/biology Recently posted documents tagged with 'biology' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3124.1 BioModels Database (http://www.ebi.ac.uk/biomodels/) is a free resource for storing, viewing and retrieving published, peer-reviewed, quantitative models of biochemical and cellular systems. As a storage format, BioModels Database uses the Systems Biology Markup Language (SBML), but also allows submission and export of models in various other commonly used formats.To offer scientists reliable information, models are curated to comply with the MIRIAM (Minimal Information Requested In the Annotation of biochemical Models) standard. This curation process involves verification of the model structure, the parameter and variable values and its mathematical relations. Furthermore reproduction of results in the reference publication is checked.The different elements of the models are extensively annotated with references to controlled vocabularies and links to other databases, to allow for identification and search. Those references and links are provided in the exported SBML files as a URN (Uniform Resource Name), identifying the data-type and the data-set, and a qualifier, indicating the relation between the element and the referenced data-set. The URNs follow the MIRIAM scheme and are resolved, for instance to URLs, using the Web Services of MIRIAM Resources (http://www.ebi.ac.uk/miriam/). http://dx.doi.org/10.1038/npre.2009.3124.1 Wed, 22 Apr 2009 12:58:44 UTC Curation and annotation for BioModels Database, a resource of published quantitative kinetic models doi:10.1038/npre.2009.3124.1 2009-05-06 Lukas Endler Nature Precedings 2009-04-22T12:58:44Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2715.1 With the rise of Systems Biology as a new paradigm for understanding biological processes, the development of quantitative models is no longer restricted to a small circle of theoreticians. The dramatic increase in the number of these models precipitates the need to exchange and reuse both existing and newly created models. The Systems Biology Markup Language (SBML) is a free, open, XML-based format for representing quantitative models of biological interest that advocates the consistent specification of such models and thus facilitates both software development and model exchange.Principally oriented towards describing systems of biochemical reactions, such as cell signalling pathways, metabolic networks and gene regulation etc., SBML can also be used to encode any kinetic model. SBML offers mechanisms to describe biological components by means of compartments and reacting species, as well as their dynamic behaviour, using reactions, events and arbitrary mathematical rules. SBML also offers all the housekeeping structures needed to ensure an unambiguous understanding of quantitative descriptions.This is Release 1 of the specification for SBML Level 2 Version 4, describing the structures of the language and the rules used to build a valid model. SBML XML Schema and other related documents and software are also available from the SBML project web site, http://sbml.org/. http://dx.doi.org/10.1038/npre.2008.2715.1 Wed, 24 Dec 2008 15:41:15 UTC Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions doi:10.1038/npre.2008.2715.1 2008-12-24 Nicolas Le Novère Nature Precedings 2008-12-24T15:41:15Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2005/version/1 Current thinking emphasizes the primacy of CD14 in facilitating TLR recognition of microbes to initiate proinflammatory signaling events and the importance of p38-MAPK in augmenting such responses. Herein, this paradigm is challenged by demonstrating that recognition of Borrelia burgdorferi not only triggers an inflammatory response in the absence of CD14, but one that is uncontrolled as a consequence of impaired PI3K/AKT/p38-MAPK signaling and negative regulation of TLR2. CD14 deficiency results in hyperphosphorylation of AKT and reduced activation of p38. Such aberrant signaling leads to decreased negative regulation by SOCS1, SOCS3, and CIS thereby engendering a more severe and persistent inflammatory response to B. burgdorferi. Perturbation of this CD14/p38-MAPK-dependent mechanism of immune regulation may underlie development of infectious chronic inflammatory syndromes. http://precedings.nature.com/documents/2005/version/1 Wed, 25 Jun 2008 10:07:23 UTC CD14 Modulates PI3K/AKT/p38-MAPK Licensing of Negative Regulators of TLR Signaling to Restrain Chronic Inflammation hdl:10101/npre.2008.2005.1 2008-07-01 Timothy J. Sellati Nature Precedings 2008-06-25T10:07:23Z Manuscript Immunology Microbiology http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1768/version/1 Multivariate experiments and genomics studies applied to mammalian cells often produce lists of genes or proteins altered under treatment/disease vs. control/normal conditions. Such lists can be identified in known protein-protein interaction networks to produce subnetworks that “connect” the genes or proteins from the lists. Such subnetworks are valuable for biologists since they can suggest regulatory mechanisms that are altered under different conditions. Often such subnetworks are overloaded with links and nodes resulting in connectivity diagrams that are illegible due to edge overlap. In this study, we attempt to address this problem by implementing an approximation to the Steiner Tree problem to connect seed lists of mammalian proteins/genes using literature-based protein-protein interaction networks. To avoid over-representation of hubs in the resultant Steiner Trees we assign a cost to Steiner Vertices based on their connectivity degree. We applied the algorithm to lists of genes commonly mutated in colorectal cancer to demonstrate the usefulness of this approach. http://precedings.nature.com/documents/1768/version/1 Mon, 07 Apr 2008 16:22:36 UTC Connecting Seed Lists of Mammalian Proteins Using Steiner Trees hdl:10101/npre.2008.1768.1 2008-04-07 Avi Ma'ayan Nature Precedings 2008-04-07T16:22:36Z Manuscript Biotechnology Cancer Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/35/version/2 In recent years, in-silico literature-based mammalian protein-protein interaction network datasets have been developed. These datasets contain binary interactions extracted manually from legacy experimental biomedical research literature. Placing lists of genes or proteins identified as significantly changing in multivariate experiments, in the context of background knowledge about binary interactions, can be used to place these genes or proteins in the context of pathways and protein complexes.Genes2Networks is a software system that integrates the content of ten mammalian literature-based interaction network datasets. Filtering to prune low-confidence interactions was implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from “seed” lists of human Entrez gene names. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is available at http://actin.pharm.mssm.edu/genes2networks.Genes2Network is a powerful web-based software application tool that can help experimental biologists to interpret high-throughput experimental results used in genomics and proteomics studies where the output of these experiments is a list of significantly changing genes or proteins. The system can be used to find relationships between nodes from the seed list, and predict novel nodes that play a key role in a common function. http://precedings.nature.com/documents/35/version/2 Fri, 08 Jun 2007 11:29:03 UTC Genes2Networks: Connecting Lists of Proteins by Using Background Literature-based Mammalian Networks hdl:10101/npre.2007.35.2 2007-06-11 Avi M. Ma'ayan Nature Precedings 2007-06-08T11:29:03Z Manuscript Biotechnology Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://precedings.nature.com/documents/35/version/1 In recent years, in-silico literature-based mammalian protein-protein interaction network datasets have been developed. These datasets contain binary interactions extracted manually from legacy experimental biomedical research literature. Placing lists of genes or proteins identified as significantly changing in multivariate experiments, in the context of background knowledge about binary interactions, can be used to place these genes or proteins in the context of pathways and protein complexes.Genes2Networks is a software system that integrates the content of ten mammalian literature-based interaction network datasets. Filtering to prune low-confidence interactions was implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from “seed” lists of human Entrez gene names. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is available at http://actin.pharm.mssm.edu/genes2networks.Genes2Network is a powerful web-based software application tool that can help experimental biologists to interpret high-throughput experimental results used in genomics and proteomics studies where the output of these experiments is a list of significantly changing genes or proteins. The system can be used to find relationships between nodes from the seed list, and predict novel nodes that play a key role in a common function. http://precedings.nature.com/documents/35/version/1 Thu, 07 Jun 2007 11:14:46 UTC Genes2Networks: Connecting Lists of Proteins by Using Background Literature-based Mammalian Networks hdl:10101/npre.2007.35.1 2007-06-11 Avi Ma'ayan Nature Precedings 2007-06-07T11:14:46Z Manuscript Biotechnology Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/2.5/