Publications by authors named "Camille Laibe"

22 Publications

  • Page 1 of 1

BioModels: expanding horizons to include more modelling approaches and formats.

Nucleic Acids Res 2018 01;46(D1):D1248-D1253

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

BioModels serves as a central repository of mathematical models representing biological processes. It offers a platform to make mathematical models easily shareable across the systems modelling community, thereby supporting model reuse. To facilitate hosting a broader range of model formats derived from diverse modelling approaches and tools, a new infrastructure for BioModels has been developed that is available at http://www.ebi.ac.uk/biomodels. This new system allows submitting and sharing of a wide range of models with improved support for formats other than SBML. It also offers a version-control backed environment in which authors and curators can work collaboratively to curate models. This article summarises the features available in the current system and discusses the potential benefit they offer to the users over the previous system. In summary, the new portal broadens the scope of models accepted in BioModels and supports collaborative model curation which is crucial for model reproducibility and sharing.
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http://dx.doi.org/10.1093/nar/gkx1023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753244PMC
January 2018

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

PLoS Biol 2017 Jun 29;15(6):e2001414. Epub 2017 Jun 29.

Oxford e-Research Centre, University of Oxford, Oxford, United Kingdom.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.
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http://dx.doi.org/10.1371/journal.pbio.2001414DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490878PMC
June 2017

The health care and life sciences community profile for dataset descriptions.

PeerJ 2016 16;4:e2331. Epub 2016 Aug 16.

Database Center for Life Science, Kashiwa, Japan.

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.
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http://dx.doi.org/10.7717/peerj.2331DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991880PMC
September 2016

Tools and data services registry: a community effort to document bioinformatics resources.

Nucleic Acids Res 2016 Jan 3;44(D1):D38-47. Epub 2015 Nov 3.

Bioinformatics Centre, Department of Biology, University of Copenhagen, Denmark.

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.
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http://dx.doi.org/10.1093/nar/gkv1116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702812PMC
January 2016

SPARQL-enabled identifier conversion with Identifiers.org.

Bioinformatics 2015 Jun 31;31(11):1875-7. Epub 2015 Jan 31.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Swiss-Prot group, Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland, Database Center for Life Science (DCLS), Research Organization of Information and Systems, 178-4-4 Wakashiba, Kashiwa, Chiba 277-0871, Japan, Stanford Center for Biomedical Informatics Research, Stanford University, CA 94305-5479, USA and Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, UK.

Motivation: On the semantic web, in life sciences in particular, data is often distributed via multiple resources. Each of these sources is likely to use their own International Resource Identifier for conceptually the same resource or database record. The lack of correspondence between identifiers introduces a barrier when executing federated SPARQL queries across life science data.

Results: We introduce a novel SPARQL-based service to enable on-the-fly integration of life science data. This service uses the identifier patterns defined in the Identifiers.org Registry to generate a plurality of identifier variants, which can then be used to match source identifiers with target identifiers. We demonstrate the utility of this identifier integration approach by answering queries across major producers of life science Linked Data.

Availability And Implementation: The SPARQL-based identifier conversion service is available without restriction at http://identifiers.org/services/sparql.
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http://dx.doi.org/10.1093/bioinformatics/btv064DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443684PMC
June 2015

COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project.

BMC Bioinformatics 2014 Dec 14;15:369. Epub 2014 Dec 14.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

Background: With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result.

Results: We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software.

Conclusions: The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering.
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http://dx.doi.org/10.1186/s12859-014-0369-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4272562PMC
December 2014

BioModels: ten-year anniversary.

Nucleic Acids Res 2015 Jan 20;43(Database issue):D542-8. Epub 2014 Nov 20.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

BioModels (http://www.ebi.ac.uk/biomodels/) is a repository of mathematical models of biological processes. A large set of models is curated to verify both correspondence to the biological process that the model seeks to represent, and reproducibility of the simulation results as described in the corresponding peer-reviewed publication. Many models submitted to the database are annotated, cross-referencing its components to external resources such as database records, and terms from controlled vocabularies and ontologies. BioModels comprises two main branches: one is composed of models derived from literature, while the second is generated through automated processes. BioModels currently hosts over 1200 models derived directly from the literature, as well as in excess of 140,000 models automatically generated from pathway resources. This represents an approximate 60-fold growth for literature-based model numbers alone, since BioModels' first release a decade ago. This article describes updates to the resource over this period, which include changes to the user interface, the annotation profiles of models in the curation pipeline, major infrastructure changes, ability to perform online simulations and the availability of model content in Linked Data form. We also outline planned improvements to cope with a diverse array of new challenges.
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http://dx.doi.org/10.1093/nar/gku1181DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383975PMC
January 2015

BioModels linked dataset.

BMC Syst Biol 2014 Aug 15;8:91. Epub 2014 Aug 15.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

Background: BioModels Database is a reference repository of mathematical models used in biology. Models are stored as SBML files on a file system and metadata is provided in a relational database. Models can be retrieved through a web interface and programmatically via web services. In addition to those more traditional ways to access information, Linked Data using Semantic Web technologies (such as the Resource Description Framework, RDF), is becoming an increasingly popular means to describe and expose biological relevant data.

Results: We present the BioModels Linked Dataset, which exposes the models’ content as a dereferencable interlinked dataset. BioModels Linked Dataset makes use of the wealth of annotations available within a large number of manually curated models to link and integrate data and models from other resources.

Conclusions: The BioModels Linked Dataset provides users with a dataset interoperable with other semantic web resources. It supports powerful search queries, some of which were not previously available to users and allow integration of data from multiple resources. This provides a distributed platform to find similar models for comparison, processing and enrichment.
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http://dx.doi.org/10.1186/s12918-014-0091-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423647PMC
August 2014

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains.

J Biomed Semantics 2014 Feb 5;5(1). Epub 2014 Feb 5.

Database Center for Life Science, Research Organization of Information and Systems, 2-11-16, Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan.

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed.
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http://dx.doi.org/10.1186/2041-1480-5-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3978116PMC
February 2014

The EBI RDF platform: linked open data for the life sciences.

Bioinformatics 2014 May 11;30(9):1338-9. Epub 2014 Jan 11.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK and SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1211 Geneve, Switzerland.

Motivation: Resource description framework (RDF) is an emerging technology for describing, publishing and linking life science data. As a major provider of bioinformatics data and services, the European Bioinformatics Institute (EBI) is committed to making data readily accessible to the community in ways that meet existing demand. The EBI RDF platform has been developed to meet an increasing demand to coordinate RDF activities across the institute and provides a new entry point to querying and exploring integrated resources available at the EBI.
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http://dx.doi.org/10.1093/bioinformatics/btt765DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3998127PMC
May 2014

Path2Models: large-scale generation of computational models from biochemical pathway maps.

BMC Syst Biol 2013 Nov 1;7:116. Epub 2013 Nov 1.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.

Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data.

Results: To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps.

Conclusions: To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized.
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http://dx.doi.org/10.1186/1752-0509-7-116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228421PMC
November 2013

Controlled annotations for systems biology.

Methods Mol Biol 2013 ;1021:227-45

The EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus Hinxton, Cambridge, UK.

The aim of this chapter is to provide sufficient information to enable a reader, new to the subject of Systems Biology, to create and use effectively controlled annotations, using resolvable Identifiers.org Uniform Resource Identifiers (URIs). The text details the underlying requirements that have led to the development of such an identification scheme and infrastructure, the principles that underpin its syntax and the benefits derived through its use. It also places into context the relationship with other standardization efforts, how it differs from other pre-existing identification schemes, recent improvements to the system, as well as those that are planned in the future. Throughout, the reader is provided with explicit examples of use and directed to supplementary information where necessary.
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http://dx.doi.org/10.1007/978-1-62703-450-0_12DOI Listing
December 2013

BioModels Database: a repository of mathematical models of biological processes.

Methods Mol Biol 2013 ;1021:189-99

EMBL Outstation-European Bioinformatics Institute, Cambridge, UK.

BioModels Database is a public online resource that allows storing and sharing of published, peer-reviewed quantitative, dynamic models of biological processes. The model components and behaviour are thoroughly checked to correspond the original publication and manually curated to ensure reliability. Furthermore, the model elements are annotated with terms from controlled vocabularies as well as linked to relevant external data resources. This greatly helps in model interpretation and reuse. Models are stored in SBML format, accepted in SBML and CellML formats, and are available for download in various other common formats such as BioPAX, Octave, SciLab, VCML, XPP and PDF, in addition to SBML. The reaction network diagram of the models is also available in several formats. BioModels Database features a search engine, which provides simple and more advanced searches. Features such as online simulation and creation of smaller models (submodels) from the selected model elements of a larger one are provided. BioModels Database can be accessed both via a web interface and programmatically via web services. New models are available in BioModels Database at regular releases, about every 4 months.
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http://dx.doi.org/10.1007/978-1-62703-450-0_10DOI Listing
December 2013

Towards the collaborative curation of the registry underlying Identifiers.org.

Database (Oxford) 2013 12;2013:bat017. Epub 2013 Apr 12.

Proteomics Services, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

The MIRIAM Registry (http://www.ebi.ac.uk/miriam/) records information about collections of data in the life sciences, as well as where it can be obtained. This information is used, in combination with the resolving infrastructure of Identifiers.org (http://identifiers.org/), to generate globally unique identifiers, in the form of Uniform Resource Identifier. These identifiers are now widely used to provide perennial cross-references and annotations. The growing demand for these identifiers results in a significant increase in curational efforts to maintain the underlying registry. This requires the design and implementation of an economically viable and sustainable solution able to cope with such expansion. We briefly describe the Registry, the current curation duties entailed, and our plans to extend and distribute this workload through collaborative and community efforts.
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http://dx.doi.org/10.1093/database/bat017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3625955PMC
June 2013

LibKiSAO: a Java library for Querying KiSAO.

BMC Res Notes 2012 Sep 24;5:520. Epub 2012 Sep 24.

EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, United Kingdom.

Background: The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of Systems Biology models, their characteristics, parameters and inter-relationships. KiSAO enables the unambiguous identification of algorithms from simulation descriptions. Information about analogous methods having similar characteristics and about algorithm parameters incorporated into KiSAO is desirable for simulation tools. To retrieve this information programmatically an application programming interface (API) for KiSAO is needed.

Findings: We developed libKiSAO, a Java library to enable querying of the KiSA Ontology. It implements methods to retrieve information about simulation algorithms stored in KiSAO, their characteristics and parameters, and methods to query the algorithm hierarchy and search for similar algorithms providing comparable results for the same simulation set-up. Using libKiSAO, simulation tools can make logical inferences based on this knowledge and choose the most appropriate algorithm to perform a simulation. LibKiSAO also enables simulation tools to handle a wider range of simulation descriptions by determining which of the available methods are similar and can be used instead of the one indicated in the simulation description if that one is not implemented.

Conclusions: LibKiSAO enables Java applications to easily access information about simulation algorithms, their characteristics and parameters stored in the OWL-encoded Kinetic Simulation Algorithm Ontology. LibKiSAO can be used by simulation description editors and simulation tools to improve reproducibility of computational simulation tasks and facilitate model re-use.
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http://dx.doi.org/10.1186/1756-0500-5-520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3532180PMC
September 2012

Identifiers.org and MIRIAM Registry: community resources to provide persistent identification.

Nucleic Acids Res 2012 Jan 2;40(Database issue):D580-6. Epub 2011 Dec 2.

European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK.

The Minimum Information Required in the Annotation of Models Registry (http://www.ebi.ac.uk/miriam) provides unique, perennial and location-independent identifiers for data used in the biomedical domain. At its core is a shared catalogue of data collections, for each of which an individual namespace is created, and extensive metadata recorded. This namespace allows the generation of Uniform Resource Identifiers (URIs) to uniquely identify any record in a collection. Moreover, various services are provided to facilitate the creation and resolution of the identifiers. Since its launch in 2005, the system has evolved in terms of the structure of the identifiers provided, the software infrastructure, the number of data collections recorded, as well as the scope of the Registry itself. We describe here the new parallel identification scheme and the updated supporting software infrastructure. We also introduce the new Identifiers.org service (http://identifiers.org) that is built upon the information stored in the Registry and which provides directly resolvable identifiers, in the form of Uniform Resource Locators (URLs). The flexibility of the identification scheme and resolving system allows its use in many different fields, where unambiguous and perennial identification of data entities are necessary.
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http://dx.doi.org/10.1093/nar/gkr1097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245029PMC
January 2012

Controlled vocabularies and semantics in systems biology.

Mol Syst Biol 2011 Oct 25;7:543. Epub 2011 Oct 25.

Terry Fox Laboratory, Vancouver, Canada.

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.
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http://dx.doi.org/10.1038/msb.2011.77DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261705PMC
October 2011

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

BMC Syst Biol 2010 Jun 29;4:92. Epub 2010 Jun 29.

European Bioinformatics Institute, Hinxton, UK.

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.
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http://dx.doi.org/10.1186/1752-0509-4-92DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2909940PMC
June 2010

BioModels.net Web Services, a free and integrated toolkit for computational modelling software.

Brief Bioinform 2010 May 25;11(3):270-7. Epub 2009 Nov 25.

Computational Neurobiology Group, at the European Bioinformatics Institute, Hinxton, UK.

Exchanging and sharing scientific results are essential for researchers in the field of computational modelling. BioModels.net defines agreed-upon standards for model curation. A fundamental one, MIRIAM (Minimum Information Requested in the Annotation of Models), standardises the annotation and curation process of quantitative models in biology. To support this standard, MIRIAM Resources maintains a set of standard data types for annotating models, and provides services for manipulating these annotations. Furthermore, BioModels.net creates controlled vocabularies, such as SBO (Systems Biology Ontology) which strictly indexes, defines and links terms used in Systems Biology. Finally, BioModels Database provides a free, centralised, publicly accessible database for storing, searching and retrieving curated and annotated computational models. Each resource provides a web interface to submit, search, retrieve and display its data. In addition, the BioModels.net team provides a set of Web Services which allows the community to programmatically access the resources. A user is then able to perform remote queries, such as retrieving a model and resolving all its MIRIAM Annotations, as well as getting the details about the associated SBO terms. These web services use established standards. Communications rely on SOAP (Simple Object Access Protocol) messages and the available queries are described in a WSDL (Web Services Description Language) file. Several libraries are provided in order to simplify the development of client software. BioModels.net Web Services make one step further for the researchers to simulate and understand the entirety of a biological system, by allowing them to retrieve biological models in their own tool, combine queries in workflows and efficiently analyse models.
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http://dx.doi.org/10.1093/bib/bbp056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2913671PMC
May 2010

Designing and encoding models for synthetic biology.

J R Soc Interface 2009 Aug 1;6 Suppl 4:S405-17. Epub 2009 Apr 1.

EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.

A key component of any synthetic biology effort is the use of quantitative models. These models and their corresponding simulations allow optimization of a system design, as well as guiding their subsequent analysis. Once a domain mostly reserved for experts, dynamical modelling of gene regulatory and reaction networks has been an area of growth over the last decade. There has been a concomitant increase in the number of software tools and standards, thereby facilitating model exchange and reuse. We give here an overview of the model creation and analysis processes as well as some software tools in common use. Using markup language to encode the model and associated annotation, we describe the mining of components, their integration in relational models, formularization and parametrization. Evaluation of simulation results and validation of the model close the systems biology 'loop'.
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http://dx.doi.org/10.1098/rsif.2009.0035.focusDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2843962PMC
August 2009

MIRIAM Resources: tools to generate and resolve robust cross-references in Systems Biology.

BMC Syst Biol 2007 Dec 13;1:58. Epub 2007 Dec 13.

European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.

Background: The Minimal Information Requested In the Annotation of biochemical Models (MIRIAM) is a set of guidelines for the annotation and curation processes of computational models, in order to facilitate their exchange and reuse. An important part of the standard consists in the controlled annotation of model components, based on Uniform Resource Identifiers. In order to enable interoperability of this annotation, the community has to agree on a set of standard URIs, corresponding to recognised data types. MIRIAM Resources are being developed to support the use of those URIs.

Results: MIRIAM Resources are a set of on-line services created to catalogue data types, their URIs and the corresponding physical URLs (or resources), whether data types are controlled vocabularies or primary data resources. MIRIAM Resources are composed of several components: MIRIAM Database stores the information, MIRIAM Web Services allows to programmatically access the database, MIRIAM Library provides an access to the Web Services and MIRIAM Web Application is a way to access the data (human browsing) and also to edit or add entries.

Conclusions: The project MIRIAM Resources allows an easy access to MIRIAM URIs and the associated information and is therefore crucial to foster a general use of MIRIAM annotations in computational models of biological processes.
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http://dx.doi.org/10.1186/1752-0509-1-58DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2259379PMC
December 2007