Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

BioModels Database: a public repository for sharing models of biological processes

  • Vijayalakshmi ChelliahEmail author
  • Nick Juty
  • Camille Laibe
  • Henning Hermjakob
  • Nicolas Le Novère
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_636-1

Synonyms

Definition

BioModels Database is a public repository for mathematical models of biological processes. It provides access to models described in peer-reviewed scientific literature, as well as those generated automatically from pathway resources. Model components, structure, and behavior of a large proportion of models in the database are thoroughly checked. This manual procedure ensures model correspondence with the original publication. Furthermore, model components are semantically enriched with cross-references to external ontologies and other relevant data resources. Models are exported in various formats, including SBML. Graphical representations of reaction networks are also provided. Simple and advanced search options are available to facilitate model retrieval. BioModels Database can be accessed through a web interface and programmatically using web services.

All models are distributed under the terms of the Creative Commons CCO Public Domain Dedication License, ensuring that models are available free for use, modification, and distribution for any user.

Detailed Description

BioModels Database (Li et al. 2010) hosts mathematical models of varying complexity, from simple biochemical reaction systems to larger and complex dynamic models, metabolic network models, and flux balance analysis (FBA) models. It is a part of the BioModels.net initiative (http://biomodels.net; Le Novère 2006), which aims to help modelers to exchange and integrate existing models, through the use of established and shared standards.

Model Categories

BioModels Database holds two major sets of models: (A) models described in scientific literature and (B) models automatically generated from pathway resources.

A. Models published in peer-reviewed scientific literature

These models are included in the resource either directly by a team of curators or after direct submission by modelers or authors. Models have also been contributed through established collaborations with other model repositories, such as the former SBML model repository (Caltech, USA), JWS Online (http://jjj.biochem.sun.ac.za), the Database Of Quantitative Cellular Signaling (DOQCS) (http://doqcs.ncbs.res.in), and the CellML Repository. More importantly, several publishers of scientific journals recommend deposition of models to BioModels Database in their author’s instruction guidelines. These include Biophysical Journal, FEBS, PNAS, Nature Publishing Group (NPG), Public Library of Science (PLoS), Royal Society of Chemistry (RSC), and BioMed Central (BMC).

Model submission to BioModels Database is free and open to everyone. Models are accepted in two formats, SBML (Systems Biology Markup Language) (Hucka et al. 2003) and CellML (Lloyd et al. 2004). Once submitted, each model is assigned a unique and perennial identifier, which allows users to access and retrieve it. It then passes through a series of checking steps before it is published in BioModels Database (Fig. 1). Models are divided in two subbranches. Models in both subbranches are fully SBML compliant but differ in their curation status:
Fig. 1

Production pipeline of the literature-based models. This figure illustrates the sequence of steps that each model undergoes, from submission to publication

  1. i.

    Curated models: Models that satisfy the MIRIAM (Minimum Information Required in the Annotation of Models) guidelines (Le Novère et al. 2005) progress to the curated branch. These models are thoroughly checked for standard compliance, correspondence with the reference publication and reproducibility of results.

     
  2. ii.

    Non-curated models: Models can be in the non-curated branch for several reasons: non-MIRIAM-compliant models (e.g., they do not reproduce results published in the reference publication), pathway maps or models of networks, and models that provide insufficient quantitative results required for validation. This branch also temporarily includes models that have not yet been curated due to time or resource constraints.

     

Following curation and annotation (see section “Model Annotation” below), models are tagged as ready for publication and become available online with the following release of BioModels Database. Releases are scheduled two to four times a year.

Figure 2 shows the growth in number and size (complexity) of models, since its origin in 2005. Figure 3 shows the classification of curated models based on Gene Ontology (GO) (Ashburner et al. 2000) terms present in the model annotation.
Fig. 2

Growth of BioModels Database. Number of models (blue) and number of variable relationships (green). The number of relationships includes SBML’s “reactions,” “rate rules,” “assignment rules,” and “events.” There has been approximately a 20-fold increase in the number of models since the launch of the resource in 2005, with the average complexity of the models being increased five times during the same period

Fig. 3

Classification of models based on GO annotation of curated models

Certain simulation tools support only specific Levels or Versions of SBML. For this reason, BioModels Database provides models in alternative SBML versions, in addition to the version that was used by the curators to check the model. It also provides models in a variety of other formats such as BioPAX (http://www.biopax.org/), the Virtual Cell Markup Language (VCML) (http://vcell.org/), XPPAUT (http://www.math.pitt.edu/∼bard/xpp/xpp.html), Scilab (http://www.scilab.org/), Octave (m-file) (http://www.gnu.org/software/octave/), and PDF (http://www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX). Furthermore, the reaction network of the model encoded in the Systems Biology Graphical Notation (SBGN) (Le Novère et al. 2009) is available in PNG and SVG formats.

BioModels Database provides a basic online simulation using SOSlib (Machne et al. 2006). The simulation results are returned both in graphical and textual forms. An additional and more flexible simulation tool is available for many models using JWS Online. Some models are described in greater detail in the “Model of the Month.” This takes the form of a brief article discussing the biological background, significance, structure, and results of a particular model. Another important feature provided for curated models is the ability to generate sub-models from a user-selected set of model elements. These sub-models can be downloaded in SBML and used, for instance, in the modular design of new models or to expand existing models.

B. Models Generated from Pathway Resources (Path2Models)

Driven by the growing number of biochemical pathways and reconstructions, the Path2Models project (Büchel 2013) targeted the conversion of pathway information initially from KEGG (Kanehisa and Goto 2000), BioCarta (Schaefer et al. 2009), MetaCyc (Karp et al. 2000), and SABIO-RK (Wittig et al. 2012) into standard-compliant computational models.

There are currently three major types of models in this branch:
  1. i.

    Genome-scale metabolic reconstructions: These models are generated by extracting pathway data from the KEGG and MetaCyc databases and subjected to FBA.

     
  2. ii.

    Quantitative, kinetic models of metabolic pathways: These models are generated based on the metabolic pathways distributed by KEGG described as processes, in combination with experimentally determined rate laws and parameter values from the SABIO-RK database.

     
  3. iii.

    Qualitative, logical models of non-metabolic (primarily signaling) pathways: These models are generated based on the non-metabolic pathways distributed by KEGG, with additional information from BioCarta pathways.

     

Model Annotation

In order to facilitate efficient search and enhance interpretability both by users and software tools, models are cross-linked with other database entries and terms from controlled vocabularies. Annotations are included in the models directly using Identifiers.org URIs (Juty et al. 2012). Such annotations provide a consistent and perennial way to identify model components, and an anchor which may be used to merge, or expand the scope of models.

Model Search, Browse, and Retrieval

BioModels Database provides a complete list of models available for browsing. Curated models can also be browsed based on taxonomy or Gene Ontology (GO) terms used in their annotation. Users can also search and retrieve models of interest using the annotations and related information.

Web Services

BioModels Database provides web services (http://www.ebi.ac.uk/biomodels-main/webservices), allowing, for example, direct search and retrieval, and the creation of sub-models. A Web Services Description Language (WSDL) file enables software to understand available functions and their usage. The complete list of available methods, as well as a Java library and the associated documentation is provided on the BioModels Database website.

BioModels Database is developed under the GNU General Public License and the software is freely available from its SourceForge repository (http://www.ebi.ac.uk/biomodels-main/webservices).

Cross-References

References

  1. Li C et al (2010) BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92PubMedCentralPubMedCrossRefGoogle Scholar
  2. Le Novère N (2006) Model storage, exchange and integration. BMC Neurosci 7(Suppl 1):S11.PubMedCentralPubMedCrossRefGoogle Scholar
  3. Hucka M et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524–531PubMedCrossRefGoogle Scholar
  4. Lloyd CM, Halstead MD, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85(2–3):433–450PubMedCrossRefGoogle Scholar
  5. Le Novère N et al (2005) Minimum information requested in the annotation of biochemical models (MIRIAM). Nat Biotechnol 23(12):1509–1515PubMedCrossRefGoogle Scholar
  6. Ashburner M et al (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25(1):25–29PubMedCentralPubMedCrossRefGoogle Scholar
  7. Le Novère N et al (2009) The systems biology graphical notation. Nat Biotechnol 27(8):735–741PubMedCrossRefGoogle Scholar
  8. Machne R et al (2006) The SBML ODE solver library: a native API for symbolic and fast numerical analysis of reaction networks. Bioinformatics 22(11):1406–1407PubMedCrossRefGoogle Scholar
  9. Büchel Fea (2013) Path2Models: large-scale generation of computational models from biochemical pathway maps. BMC Syst Biol 7:116Google Scholar
  10. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30PubMedCentralPubMedCrossRefGoogle Scholar
  11. Schaefer CF et al (2009) PID: the pathway interaction database. Nucleic Acids Res 37(Database issue):D674–D679PubMedCentralPubMedCrossRefGoogle Scholar
  12. Karp PD et al (2000) The EcoCyc and MetaCyc databases. Nucleic Acids Res 28(1):56–59PubMedCentralPubMedCrossRefGoogle Scholar
  13. Wittig U et al (2012) SABIO-RK – database for biochemical reaction kinetics. Nucleic Acids Res 40(Database issue):D790–D796PubMedCentralPubMedCrossRefGoogle Scholar
  14. Juty N, Le Novère N, Laibe C (2012) Identifiers.org and MIRIAM registry: community resources to provide persistent identification. Nucleic Acids Res 40(Database issue):D580–D586PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Her Majesty the Queen in Right of United Kingdom 2013

Authors and Affiliations

  • Vijayalakshmi Chelliah
    • 1
    Email author
  • Nick Juty
    • 1
  • Camille Laibe
    • 1
  • Henning Hermjakob
    • 1
  • Nicolas Le Novère
    • 2
  1. 1.European Bioinformatics Institute (EMBL-EBI)European Molecular Biology LaboratoryHinxton, CambridgeUK
  2. 2.Babraham InstituteBabraham Research CampusCambridgeUK