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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.
<<<!!!<<< This repository is no longer available. >>>!!!>>> NetPath is currently one of the largest open-source repository of human signaling pathways that is all set to become a community standard to meet the challenges in functional genomics and systems biology. Signaling networks are the key to deciphering many of the complex networks that govern the machinery inside the cell. Several signaling molecules play an important role in disease processes that are a direct result of their altered functioning and are now recognized as potential therapeutic targets. Understanding how to restore the proper functioning of these pathways that have become deregulated in disease, is needed for accelerating biomedical research. This resource is aimed at demystifying the biological pathways and highlights the key relationships and connections between them. Apart from this, pathways provide a way of reducing the dimensionality of high throughput data, by grouping thousands of genes, proteins and metabolites at functional level into just several hundreds of pathways for an experiment. Identifying the active pathways that differ between two conditions can have more explanatory power than just a simple list of differentially expressed genes and proteins.
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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