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Found 7 result(s)
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<<<!!!<<< 2019-12-23: the repository is offline >>>!!!>>> Introduction of genome-scale metabolic network: The completion of genome sequencing and subsequent functional annotation for a great number of species enables the reconstruction of genome-scale metabolic networks. These networks, together with in silico network analysis methods such as the constraint based methods (CBM) and graph theory methods, can provide us systems level understanding of cellular metabolism. Further more, they can be applied to many predictions of real biological application such as: gene essentiality analysis, drug target discovery and metabolic engineering
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ConsensusPathDB integrates interaction networks in humans (and in the model organisms - yeast and mouse) including binary and complex protein-protein, genetic, metabolic, signaling, gene regulatory and drug-target interactions, as well as biochemical pathways. Data originate from public resources for interactions and interactions curated from the literature. The interaction data are integrated in a complementary manner to avoid redundancies.
BiGG is a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.
The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. The data stored within the DIP database were curated, both, manually by expert curators and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Please, check the reference page to find articles describing the DIP database in greater detail. The Database of Ligand-Receptor Partners (DLRP) is a subset of DIP (Database of Interacting Proteins). The DLRP is a database of protein ligand and protein receptor pairs that are known to interact with each other. By interact we mean that the ligand and receptor are members of a ligand-receptor complex and, unless otherwise noted, transduce a signal. In some instances the ligand and/or receptor may form a heterocomplex with other ligands/receptors in order to be functional. We have entered the majority of interactions in DLRP as full DIP entries, with links to references and additional information
DEIMS-SDR (Dynamic Ecological Information Management System - Site and dataset registry) is an information management system that allows you to discover long-term ecosystem research sites around the globe, along with the data gathered at those sites and the people and networks associated with them. DEIMS-SDR describes a wide range of sites, providing a wealth of information, including each site’s location, ecosystems, facilities, parameters measured and research themes. It is also possible to access a growing number of datasets and data products associated with the sites. All sites and dataset records can be referenced using unique identifiers that are generated by DEIMS-SDR. It is possible to search for sites via keyword, predefined filters or a map search. By including accurate, up to date information in DEIMS, site managers benefit from greater visibility for their LTER site, LTSER platform and datasets, which can help attract funding to support site investments. The aim of DEIMS-SDR is to be the globally most comprehensive catalogue of environmental research and monitoring facilities, featuring foremost but not exclusively information about all LTER sites on the globe and providing that information to science, politics and the public in general.
The South African Marine Information Management System (MIMS) is an Open Archival Information System (OAIS) repository that plays a multifaceted role in archiving, publishing, and preserving marine-related datasets. As an IODE-accredited Associate Data Unit (ADU), MIMS serves as a national node for the IODE of the IOC of UNESCO. It archives and publishes collections and subsets of marine-related datasets for the National Department of Forestry, Fisheries, and the Environment (DFFE) and its regional partners. As an IOC member organization, DFFE is committed to supporting the long-term preservation and archival of marine and coastal data for South Africa and its regional partners, promoting open access to data, and encouraging scientific collaboration. Tasked with the long-term preservation of South Africa's marine and coastal data, MIMS functions as an institutional data repository. It provides primary access to all data collected by the DFFE Oceans and Coastal Research Directorate and acts as a trusted broker of scientific marine data for a wide range of South African institutions. MIMS hosts the IODE AFROBIS Node, an OBIS Node that coordinates and collates data management activities within the sub-Saharan African region. As part of the OBIS Steering Group, MIMS represents sub-Saharan Africa on issues around biological (biodiversity) data standards. It also facilitates data and metadata publishing for the region through the GBIF and OBIS networks. Operating on the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles, MIMS aligns its practices to maximize ocean data exchange and use while respecting the conditions stipulated by the Data Provider. By integrating various functions and commitments, MIMS stands as a vital component in the marine and coastal data landscape, fostering collaboration, standardization, and accessibility in alignment with international standards and regional needs.
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KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies