BiGG Models: A platform for integrating, standardizing and sharing genome-scale models
Open accessAbstract
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.
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Flux balance analysis methods have been applied to many cells, from simple bacteria to eukaryotes such as yeast and human cells, to predict fluxes through metabolic reactions.
“Flux balance analysis (FBA) methods have been applied to many cells, from simple bacteria to eukaryotes such as yeast and human cells, to predict fluxes through metabolic reactions.”
From Bringing the genetically minimal cell to life on a computer in 4D (2026) · cites this paper as [10]
The Introduction (page not available) says genome-scale metabolic models can predict cellular phenotypes, and the paper describes an E. coli model. It mentions yeast and humans as organisms covered by other knowledge bases, but does not establish that FBA was applied to their cells to predict reaction fluxes. (LLM, conf=0.81)
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- Authors
- King, Z. A., Lu, J., Dräger, A., Miller, P., Federowicz, S., Lerman, J. A., Ebrahim, A., Palsson, B. O., & Lewis, N. E.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Lewis, Nathan E.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-10-17
- Volume
- 44
- Issue
- D1
- Pages
- D515-D522
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4702785
- OpenAlex
- W2202347610
- Cited by
- 938
- References
- 7
- Citations by year
- 2026: 92 · 2025: 80 · 2024: 112 · 2023: 115 · 2022: 96 · 2021: 77 · 2020: 113 · 2019: 98 · 2018: 74 · 2017: 53 · 2016: 25
- FWCI
- 22.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- HighWire Press Open Archive (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by)
- Funders
- National Science Foundation
- U.S. Department of Energy (DESC0008701)
- European Commission
- Novo Nordisk
- Danmarks Tekniske Universitet
- Novo Nordisk Fonden (NNF10CC1016517)
- National Institutes of Health
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD080682)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- genome-scale metabolic models, model standardization, model curation, model repository, model visualization, application programming interfaces, systems biology
- MeSH terms
- Models, Genetic, Genome, Genomics, Knowledge Bases, Metabolic Networks and Pathways, Metabolomics, Databases, Chemical
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