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BiGG模型:一个用于整合、标准化和共享基因组规模模型的平台。

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models.

作者信息

King Zachary A, Lu Justin, Dräger Andreas, Miller Philip, Federowicz Stephen, Lerman Joshua A, Ebrahim Ali, Palsson Bernhard O, Lewis Nathan E

机构信息

Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.

Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.

出版信息

Nucleic Acids Res. 2016 Jan 4;44(D1):D515-22. doi: 10.1093/nar/gkv1049. Epub 2015 Oct 17.

Abstract

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4887/4702785/dacea3e43f80/gkv1049fig1.jpg

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