Publications by authors named "Michael Hucka"

51 Publications

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Mol Syst Biol 2020 08;16(8):e9110

Management & IT Consulting Division, Mizuho Information & Research Institute, Inc., Tokyo, Japan.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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http://dx.doi.org/10.15252/msb.20199110DOI Listing
August 2020

Systems Biology Markup Language (SBML) Level 3 Package: Distributions, Version 1, Release 1.

J Integr Bioinform 2020 Jul 20;17(2-3). Epub 2020 Jul 20.

California Institute of Technology, Pasadena, USA.

Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not include an explicit mechanism to include inexact or stochastic values in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Distributions package for SBML Level 3 adds the necessary features to allow models to encode information about the distribution and uncertainty of values underlying a quantity.
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http://dx.doi.org/10.1515/jib-2020-0018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756622PMC
July 2020

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2.

J Integr Bioinform 2020 Jul 6;17(2-3). Epub 2020 Jul 6.

NIAID/NIH, Bethesda, USA.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through "wildcards" representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the "type" concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.
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http://dx.doi.org/10.1515/jib-2020-0015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756619PMC
July 2020

Specifications of standards in systems and synthetic biology: status and developments in 2020.

J Integr Bioinform 2020 Jun 29;17(2-3). Epub 2020 Jun 29.

University Medicine Greifswald, Greifswald, Germany.

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.
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http://dx.doi.org/10.1515/jib-2020-0022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756620PMC
June 2020

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE).

J Integr Bioinform 2020 Jun 29;17(2-3). Epub 2020 Jun 29.

Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK.

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.
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http://dx.doi.org/10.1515/jib-2020-0005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756615PMC
June 2020

BioModels-15 years of sharing computational models in life science.

Nucleic Acids Res 2020 01;48(D1):D407-D415

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

Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world's largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.
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http://dx.doi.org/10.1093/nar/gkz1055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145643PMC
January 2020

Data Management in Computational Systems Biology: Exploring Standards, Tools, Databases, and Packaging Best Practices.

Methods Mol Biol 2019 ;2049:285-314

School of Computer Science, University of Manchester, Manchester, UK.

Computational systems biology involves integrating heterogeneous datasets in order to generate models. These models can assist with understanding and prediction of biological phenomena. Generating datasets and integrating them into models involves a wide range of scientific expertise. As a result these datasets are often collected by one set of researchers, and exchanged with others researchers for constructing the models. For this process to run smoothly the data and models must be FAIR-findable, accessible, interoperable, and reusable. In order for data and models to be FAIR they must be structured in consistent and predictable ways, and described sufficiently for other researchers to understand them. Furthermore, these data and models must be shared with other researchers, with appropriately controlled sharing permissions, before and after publication. In this chapter we explore the different data and model standards that assist with structuring, describing, and sharing. We also highlight the popular standards and sharing databases within computational systems biology.
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http://dx.doi.org/10.1007/978-1-4939-9736-7_17DOI Listing
June 2020

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019.

J Integr Bioinform 2019 Jul 13;16(2). Epub 2019 Jul 13.

University Medicine Greifswald, Greifswald, Germany.

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.
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http://dx.doi.org/10.1515/jib-2019-0035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798822PMC
July 2019

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2.

J Integr Bioinform 2019 Jun 20;16(2). Epub 2019 Jun 20.

NIAID/NIH, Bethesda, MD, USA.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/.
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http://dx.doi.org/10.1515/jib-2019-0021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798823PMC
June 2019

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Nat Protoc 2019 03;14(3):639-702

Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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http://dx.doi.org/10.1038/s41596-018-0098-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635304PMC
March 2019

Harmonizing semantic annotations for computational models in biology.

Brief Bioinform 2019 03;20(2):540-550

Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.
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http://dx.doi.org/10.1093/bib/bby087DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433895PMC
March 2019

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2017.

J Integr Bioinform 2018 Mar 29;15(1). Epub 2018 Mar 29.

University of Rostock, Rostock, Germany.

Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year.
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http://dx.doi.org/10.1515/jib-2018-0013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167034PMC
March 2018

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core.

J Integr Bioinform 2018 Mar 9;15(1). Epub 2018 Mar 9.

Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.
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http://dx.doi.org/10.1515/jib-2017-0081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167032PMC
March 2018

SBMLPkgSpec: a LaTeX style file for SBML package specification documents.

Authors:
Michael Hucka

BMC Res Notes 2017 Sep 6;10(1):451. Epub 2017 Sep 6.

Department of Computing and Mathematical Sciences, California Institute of Technology, 1200 E. California Blvd., Pasadena, California, 91125, USA.

Objective: The Systems Biology Markup Language (SBML) is a popular open format for storing and exchanging computational models in biology. The definition of SBML is captured in formal specification documents. SBMLPkgSpec is a LaTeX document style intended to fill the need for a standard format for writing such specification documents.

Results: Specification documents for SBML Level 3 extensions (known as packages in SBML) are made more uniform with the use of a standard template. SBMLPkgSpec is a LaTeX class that provides a document framework for SBML Level 3 package specifications, to simplify the work of document authors while improving the overall quality of the family of SBML specifications.
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http://dx.doi.org/10.1186/s13104-017-2788-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5588604PMC
September 2017

SBML Level 3 package: Groups, Version 1 Release 1.

J Integr Bioinform 2016 Dec 18;13(3):290. Epub 2016 Dec 18.

Biological models often contain components that have relationships with each other, or that modelers want to treat as belonging to groups with common characteristics or shared metadata. The SBML Level 3 Version 1 Core specification does not provide an explicit mechanism for expressing such relationships, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Groups package for SBML Level 3 adds the necessary features to SBML to allow grouping of model components to be expressed. Such groups do not affect the mathematical interpretation of a model, but they do provide a way to add information that can be useful for modelers and software tools. The SBML Groups package enables a modeler to include definitions of groups and nested groups, each of which may be annotated to convey why that group was created, and what it represents.
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http://dx.doi.org/10.2390/biecoll-jib-2016-290DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451322PMC
December 2016

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016.

J Integr Bioinform 2016 Dec 18;13(3):289. Epub 2016 Dec 18.

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.
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http://dx.doi.org/10.2390/biecoll-jib-2016-289DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5431574PMC
December 2016

Toward Community Standards and Software for Whole-Cell Modeling.

IEEE Trans Biomed Eng 2016 10 10;63(10):2007-14. Epub 2016 Jun 10.

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells.

Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language.

Results: Our analysis revealed several challenges to representing WC models using the current standards.

Conclusion: We, therefore, propose several new WC modeling standards, software, and databases.

Significance: We anticipate that these new standards and software will enable more comprehensive models.
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http://dx.doi.org/10.1109/TBME.2016.2560762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451320PMC
October 2016

MOCCASIN: converting MATLAB ODE models to SBML.

Bioinformatics 2016 06 9;32(12):1905-6. Epub 2016 Feb 9.

Department of Neurology, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center and School of Medicine, New York, NY 10029, USA.

Unlabelled: MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs). However, sharing or using these models outside of MATLAB is often problematic. A community standard such as Systems Biology Markup Language (SBML) can serve as a neutral exchange format, but translating models from MATLAB to SBML can be challenging-especially for legacy models not written with translation in mind. We developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability) to help. MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks into the SBML format.

Availability And Implementation: MOCCASIN is available under the terms of the LGPL 2.1 license (http://www.gnu.org/licenses/lgpl-2.1.html). Source code, binaries and test cases can be freely obtained from https://github.com/sbmlteam/moccasin

Contact: : mhucka@caltech.edu

Supplementary Information: More information is available at https://github.com/sbmlteam/moccasin.
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http://dx.doi.org/10.1093/bioinformatics/btw056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908318PMC
June 2016

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

J Integr Bioinform 2015 Sep 4;12(2):271. Epub 2015 Sep 4.

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.
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http://dx.doi.org/10.2390/biecoll-jib-2015-271DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5457286PMC
September 2015

SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3.

J Integr Bioinform 2015 Sep 4;12(2):268. Epub 2015 Sep 4.

Constructing a model in a hierarchical fashion is a natural approach to managing model complexity, and offers additional opportunities such as the potential to re-use model components. The SBML Level 3 Version 1 Core specification does not directly provide a mechanism for defining hierarchical models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Hierarchical Model Composition package for SBML Level 3 adds the necessary features to SBML to support hierarchical modeling. The package enables a modeler to include submodels within an enclosing SBML model, delete unneeded or redundant elements of that submodel, replace elements of that submodel with element of the containing model, and replace elements of the containing model with elements of the submodel. In addition, the package defines an optional "port" construct, allowing a model to be defined with suggested interfaces between hierarchical components; modelers can chose to use these interfaces, but they are not required to do so and can still interact directly with model elements if they so chose. Finally, the SBML Hierarchical Model Composition package is defined in such a way that a hierarchical model can be "flattened" to an equivalent, non-hierarchical version that uses only plain SBML constructs, thus enabling software tools that do not yet support hierarchy to nevertheless work with SBML hierarchical models.
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http://dx.doi.org/10.2390/biecoll-jib-2015-268DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451323PMC
September 2015

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core.

J Integr Bioinform 2015 Sep 4;12(2):266. Epub 2015 Sep 4.

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
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http://dx.doi.org/10.2390/biecoll-jib-2015-266DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451324PMC
September 2015

Specifications of Standards in Systems and Synthetic Biology.

J Integr Bioinform 2015 Sep 4;12(2):258. Epub 2015 Sep 4.

Standards shape our everyday life. From nuts and bolts to electronic devices and technological processes, standardised products and processes are all around us. Standards have technological and economic benefits, such as making information exchange, production, and services more efficient. However, novel, innovative areas often either lack proper standards, or documents about standards in these areas are not available from a centralised platform or formal body (such as the International Standardisation Organisation). Systems and synthetic biology is a relatively novel area, and it is only in the last decade that the standardisation of data, information, and models related to systems and synthetic biology has become a community-wide effort. Several open standards have been established and are under continuous development as a community initiative. COMBINE, the ‘COmputational Modeling in BIology’ NEtwork has been established as an umbrella initiative to coordinate and promote the development of the various community standards and formats for computational models. There are yearly two meeting, HARMONY (Hackathons on Resources for Modeling in Biology), Hackathon-type meetings with a focus on development of the support for standards, and COMBINE forums, workshop-style events with oral presentations, discussion, poster, and breakout sessions for further developing the standards. For more information see http://co.mbine.org/. So far the different standards were published and made accessible through the standards’ web- pages or preprint services. The aim of this special issue is to provide a single, easily accessible and citable platform for the publication of standards in systems and synthetic biology. This special issue is intended to serve as a central access point to standards and related initiatives in systems and synthetic biology, it will be published annually to provide an opportunity for standard development groups to communicate updated specifications.
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http://dx.doi.org/10.2390/biecoll-jib-2015-258DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5431569PMC
September 2015

JSBML 1.0: providing a smorgasbord of options to encode systems biology models.

Bioinformatics 2015 Oct 16;31(20):3383-6. Epub 2015 Jun 16.

University of California, San Diego, La Jolla, CA, USA, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.

Unlabelled: JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active open-source project with contributions from a growing, international team of developers who not only maintain compatibility with SBML, but also drive steady improvements to the Java interface and promote ease-of-use with end users.

Availability And Implementation: Source code, binaries and documentation for JSBML can be freely obtained under the terms of the LGPL 2.1 from the website http://sbml.org/Software/JSBML. More information about JSBML can be found in the user guide at http://sbml.org/Software/JSBML/docs/.

Contact: jsbml-development@googlegroups.com or andraeger@eng.ucsd.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595895PMC
October 2015

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative.

Front Bioeng Biotechnol 2015 24;3:19. Epub 2015 Feb 24.

Babraham Institute , Cambridge , UK ; European Molecular Biology Laboratory-European Bioinformatics Institute , Cambridge , UK.

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.
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http://dx.doi.org/10.3389/fbioe.2015.00019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4338824PMC
March 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

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.

BMC Syst Biol 2013 Dec 10;7:135. Epub 2013 Dec 10.

Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal.

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.

Results: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.

Conclusions: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.
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http://dx.doi.org/10.1186/1752-0509-7-135DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892043PMC
December 2013

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