skip to main content

DOE PAGESDOE PAGES

2 results for: All records
Author ORCID ID is 0000000212405553
Full Text and Citations
Filters
  1. In this study, 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-scalemore » 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.« less
    Cited by 29Full Text Available

"Cited by" information provided by Web of Science.

DOE PAGES offers free public access to the best available full-text version of DOE-affiliated accepted manuscripts or articles after an administrative interval of 12 months. The portal and search engine employ a hybrid model of both centralized and distributed content, with PAGES maintaining a permanent archive of all full text and metadata.