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

BACKGROUND: Quantitative 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. DESCRIPTION: BioModels 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. CONCLUSIONS: BioModels 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.

SBMLeditor: effective creation of models in the Systems Biology Markup language (SBML).

BACKGROUND: The need to build a tool to facilitate the quick creation and editing of models encoded in the Systems Biology Markup language (SBML) has been growing with the number of users and the increased complexity of the language. SBMLeditor tries to answer this need by providing a very simple, low level editor of SBML files. Users can create and remove all the necessary bits and pieces of SBML in a controlled way, that maintains the validity of the final SBML file. RESULTS: SBMLeditor is written in JAVA using JCompneur, a library providing interfaces to easily display an XML document as a tree. This decreases dramatically the development time for a new XML editor. The possibility to include custom dialogs for different tags allows a lot of freedom for the editing and validation of the document. In addition to Xerces, SBMLeditor uses libSBML to check the validity and consistency of SBML files. A graphical equation editor allows an easy manipulation of MathML. SBMLeditor can be used as a module of the Systems Biology Workbench. CONCLUSION: SBMLeditor contains many improvements compared to a generic XML editor, and allow users to create an SBML model quickly and without syntactic errors.

LGICdb: a manually curated sequence database after the genomes.

Ligand-gated ion channels form transmembrane ionic pores controlled by the binding of chemicals. The LGICdb aims to be a non-redundant, manually curated resource offering access to the large number of subunits composing extracellularly activated ligand-gated ion channels, such as nicotinic, ATP, GABA and glutamate ionotropic receptors. Composed of more than 500 human curated entries, the XML native database has been relocated in 2004 to the EBI. Its facilities have been enhanced with a new search system, customized multiple sequence alignments and manipulation of protein structures (http://www.ebi.ac.uk/compneur-srv/LGICdb/). Despite the vast improvement of general sequence resources, the LGICdb still provide sequences unavailable elsewhere.

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

BioModels Database (http://www.ebi.ac.uk/biomodels/), 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.

The Molecular Pages of the mesotelencephalic dopamine consortium (DopaNet).

BACKGROUND: DopaNet http://www.dopanet.org is a Systems Biology initiative that aims to investigate precisely and quantitatively all the aspects of neurotransmission in a specific neuronal system, the mesotelencephalic dopamine system. The project should lead to large-scale models of molecular and cellular processes involved in neuronal signaling. A prerequisite is the proper storage of knowledge coming from the literature. METHODS: DopaNet Molecular Pages are highly structured descriptions of quantitative parameters related to a specific molecular complex involved in neuronal signal processing. A Molecular Page is built by maintainers who are experts in the field, and responsible for the quality of the page content. Each piece of data is identified by a specific ontology code, annotated (method of acquisition, species, etc.) and linked to the relevant bibliography. The Molecular Pages are stored as XML files, and processed through the DopaNet Web Service, which provides functionalities to edit the Molecular Pages, to cross-link the Pages and generate the public display, and to search them. CONCLUSIONS: DopaNet Molecular Pages are one of the core resources of the DopaNet project but should be of widespread utility in the field of Systems Neurobiology.