标识符
可视化
计算机科学
基因组
数据科学
生物
计算生物学
计算模型
比例(比率)
数据可视化
SBML公司
数据挖掘
万维网
人工智能
遗传学
基因
物理
量子力学
程序设计语言
标记语言
XML
作者
Zachary A. King,Justin S. Lu,Andreas Dräger,Philip Miller,Stephen Federowicz,Joshua A. Lerman,Ali Ebrahim,Bernhard Ø. Palsson,Nathan E. Lewis
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI