BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

By Chen Li, M. Donizelli, Nicolas Rodriguez, Harish Dharuri, L. Endler, Vijayalakshmi Chelliah, Lu Li, Enuo He, Arnaud Henry, Melanie I. Stefan and 4 more. 2010

590 citations recorded by the source corpus.

BackgroundQuantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification.DescriptionBioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database.ConclusionsBioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License.

Subfields: Molecular Biology, Physical and Theoretical Chemistry

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Citations in the corpus

Identifying Causal Structure in Large-Scale Kinetic Systems (2018, methodology): “Finding causal predictors in the Maillard reaction The first simulation study is based on a biological ODE system from BioModels Database due to Li et al. [2010]. More specifically, we use the model BIOMD0000000052 due to Brands and van Boekel [2002] which describes reactions in heated monosaccharid”
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023, unknown): “In the context of LLM training, there are various data challenges to address, including the high cost of Healthcare data, contamination in benchmark data, personally identifiable information, and the mixture of domains during pre-training and fine-tuning tasks.”
Controlled vocabularies and semantics in systems biology (2011, methodology): “The resource is accessible programmatically through Web Services, with a Java library available to aid consumption (Li et al , 2010b).”
The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013 (2012, methodology): “As mentioned above, ChEBI has already been adopted as the primary chemical annotation and identification standard in several biological databases and ontologies, including BioModels (5), Reactome (4) and the Gene Ontology (7).”
MEMOTE for standardized genome-scale metabolic model testing (2020, methodology): “Although a model repository can be used offline, we encourage community collaboration via distributed version control development platforms, such as GitHub (https://github.com), GitLab (https://gitlab. com/) or BioModels12 (http://wwwdev.ebi. ac.uk/biomodels/).”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, methodology): “Additionally, COBRApy can read SBML-formatted models [32] downloaded from a variety of sources, such as the Model SEED [33] and the BioModels database [34].”
The EBI RDF platform: linked open data for the life sciences (2014, background): “Availability: http://www.ebi.ac.uk/rdf Contact: jupp@ebi.ac.uk Received on October 10, 2013; revised on November 22, 2013; accepted on December 23, 2013”