BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems

By N. Novère, B. Bornstein, A. Broicher, Mélanie Courtot, M. Donizelli, Harish Dharuri, Lu Li, H. Sauro, M. Schilstra, B. Shapiro and 2 more. Nucleic Acids Res., 2005

829 citations recorded by the source corpus.

BioModels Database (), part of the international initiative BioModels.net, provides access to published, peer-reviewed, quantitative models of biochemical and cellular systems. Each model is carefully curated to verify that it corresponds to the reference publication and gives the proper numerical results. Curators also annotate the components of the models with terms from controlled vocabularies and links to other relevant data resources. This allows the users to search accurately for the models they need. The models can currently be retrieved in the SBML format, and import/export facilities are being developed to extend the spectrum of formats supported by the resource.

Subfields: Molecular Biology, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/e13a71f639bed2c1c739b206a6a053e8f18651a6

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In Molecular Biology

In Physical and Theoretical Chemistry

Bernhard O. Palsson

Citations in the corpus

BioModels—15 years of sharing computational models in life science (2019, methodology): “To address the growing need for a platform to exchange models, BioModels, a repository for computational models of biological and biomedical processes, was established in 2005 at the European Bioinformatics Institute (2).”
Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data (2009, methodology): “Full details and parameters are available in the Biomodels database[21] and ref.”
Controlled vocabularies and semantics in systems biology (2011, methodology): “As an example, the number of models deposited in BioModels Database (Le Novère et al, 2006; Li et al, 2010a) is doubling roughly every 22 months while the average number of relationships between variables per model is doubling every 13 months.”
Reactome: a knowledge base of biologic pathways and processes (2007, background): “Recent efforts [7, 8 ] have established databases of published kinetic models of biologic processes ranging in complexity from glycolysis to regulation of the cell cycle.”
The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions (2017, background): “Available GEMs can be browsed at the BioModels database (Le Novere et al., 2006), where most of the reconstructed models are deposited and usually classified according to their level of curation.”
Bio-PEPA: A framework for the modelling and analysis of biological systems (2009, methodology): “networks. Many SBML models are collected in the BioModels Database [5, 35 ].”
BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions (2010, background): “This field is an active area of research with an extensive number of models [2-5] as well as computational tools [6,7] available.”
OptFlux: an open-source software platform for in silico metabolic engineering (2010, methodology): “The compatibility with SBML allows the use of models stored in public databases, e. g. BioModels [28] or the BiGG database [29] or built using other software tools, e.g. Cell Designer [20].”
Universally Sloppy Parameter Sensitivities in Systems Biology Models (2007, methodology): “Models discussed that are in the BioModels database [42] are: (a) BIOMD0000000005, (c) BIOMD0000000003, (d) BIOMD0000000035, (e) BIOMD0000000002, (f) BIOMD0000000010, (h) BIOMD0000000021, (i) BIOMD0000000033, (k) BIOMD0000000022, (l) BIOMD0000000055, (n) BIOMD0000000015, (o) BIOMD0000000051, (p) BIO”
Systems Biology of Cancer Metastasis (2019, methodology): “Systems biology model description standards (Hucka et al., 2003; Konig et al., 2012; Le Novere et al., 2006; Lopez et al., 2013) : SBML, pySB etc. Interpretable machine learning models (Broecker et al., 2017; Hua et al., 2006; Ma et al., 2018; Michael et al., 2018; Samek et al., 2017) Able to…”
NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail (2010, background): “SBML [30], CellML [31]) and curated databases of models [32], allowing greater interoperability and validation of model behavior across multiple simulators.”
BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models (2010, methodology): “We developed BioModels Database [11-13] precisely with these goals in mind, while other resources, such as ModelDB [14], JWS Online [15] or the CellML Model Repository [16,17], focus on different aspects.”