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. 2015 Jun 16;31(20):3383–3386. doi: 10.1093/bioinformatics/btv341

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

Nicolas Rodriguez 1,2,*,†, Alex Thomas 3,4,*,†, Leandro Watanabe 5, Ibrahim Y Vazirabad 6, Victor Kofia 7, Harold F Gómez 8, Florian Mittag 1,9, Jakob Matthes 9, Jan Rudolph 9,10, Finja Wrzodek 1,9, Eugen Netz 9, Alexander Diamantikos 9, Johannes Eichner 9, Roland Keller 9, Clemens Wrzodek 9,11, Sebastian Fröhlich 12,13, Nathan E Lewis 3,4, Chris J Myers 5, Nicolas Le Novère 1,2, Bernhard Ø Palsson 3,4, Michael Hucka 14, Andreas Dräger 3,9,*
PMCID: PMC4595895  PMID: 26079347

Abstract

Summary: 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.

1 Introduction

The Systems Biology Markup Language (SBML) is a widely used format that enables easy distribution of systems biology data, models and diagrams, and it allows the easy exchange of data and models between a variety of software systems (Hucka et al., 2003; Dräger and Palsson, 2014). Given the wide coverage of the latest SBML version, it is unsurprising that the standard is relatively complex. An appropriate computational architecture greatly simplifies the work that software developers need to do in order to support importing and storing SBML-based information for computational analysis. JSBML (Dräger et al., 2011) is the official, pure Java-based application programming interface (API) library for SBML. It enables systems biology information to be expressed in Java data structures patterned after the SBML format for fast access. JSBML 1.0 implements the ability to encode, exchange and use all parts of SBML, up to the current release, SBML Level 3 (L3), including all community-approved (and several prototype) SBML packages for L3, as described later.

One important aspect of SBML is its ability to provide additional capabilities to encode specific types of systems biology models. These capabilities are extensions of the core SBML L3 format and are known as ‘packages’. Although many models can be fully represented using only the core set of SBML constructs, these extensions support (i) other model features that cannot be formulated with the SBML core standards and (ii) additional constructs that enable users in specific fields to formulate, interface and use the SBML framework more easily within their modeling approaches. SBML packages enable the ability to build models that encompass several formalisms within one overall framework. Several tools today use JSBML 1.0 in this way and encode multiple types of systems biology models; examples include iBioSim (Madsen et al., 2012), KEGGtranslator (Wrzodek et al., 2011) and GINSim (Gonzalez Gonzales et al., 2006). In addition, because JSBML is built to be an interpreter for SBML, this functionality, represented by a single data structure, can easily be embedded into existing Java programs.

Since its inception, JSBML has fostered a community of active developers who aim to provide regular code updates, provide major and minor bug fixes to releases, and partake in discussions on standards for the systems biology modeling community (COMBINE, Waltemath et al., 2011a). These community interactions have helped improve JSBML substantially since its launch.

2 Results

2.1 Improvements

JSBML’s first public release was in 2011 (Dräger et al., 2011) and it has since undergone considerable change and expansion. A major goal of the initial release was to present a software package which differentiated itself from libSBML, JSBML’s C++ language counterpart (Bornstein et al., 2008), and its Java-language bindings. Since then, JSBML has maintained compatibility with SBML and libSBML and has introduced extra functionalities.

In addition to support for SBML L3 core and packages, JSBML 1.0 has incorporated improvements to software efficiency. For instance, improvements to internal interfaces now speed up model input/output operations. The different identifier namespaces in SBML are now managed with an IdManager interface which is able to reconcile redundant identifiers among packages. A new Math infix parser is able to mimic the same behavior as the libSBML L3 Math parser and can handle complex mathematical formulas. The manipulation and merging of Units in SBML has also been greatly improved.

Also, several features have been added to JSBML 1.0 to improve end-user convenience and accessibility. For instance, various utility methods have been added that allow users to manipulate JSBML’s in-memory data object. Furthermore, user-defined objects can be temporarily added to the JSBML data structure for any model component. XML annotations in SBML are read as XMLNode object instead of Strings, making it easier to manipulate non-standard annotations in SBML. Advanced logging functionalities via the Apache log4j project allows users to monitor JSBML actions.

Finally, JSBML has been better integrated with other software such as Apache Maven, a dependency management tool, BioJava 3 (Prlić et al., 2012), a bioinformatics toolbox, and CellDesigner (Funahashi et al., 2008), a biochemical network modeling and visualization tool.

2.2 Support for SBML packages

2.2.1 Approved packages

At the time of writing, all approved SBML L3 extensions are supported by JSBML 1.0. We describe the support below, and provide insights into the mapping between modeling formalisms and the corresponding JSBML data objects. An abbreviated version of the JSBML class hierarchies are presented in Supplementary Figure S1. The JSBML User Guide has figures that lay out the JSBML class hierarchy for each SBML package, displaying the full capabilities of the JSBML data objects that encode each package. In Table 1, approved SBML packages constitute the first four rows. The Qualitative Models package (qual, for short) allows species in a model to have non-quantitative or non-continuous levels (Chaouiya et al., 2013). This may manifest as Boolean or discrete values, and is primarily employed in modeling gene regulation, signaling pathways, logical/Boolean networks (Schmulevich et al., 2002), and Petri nets (Breitling et al., 2008). Flux Balance Constraints (fbc, Olivier and Bergmann, 2013) encodes components for constraints-based modeling (Lewis et al., 2012), which employs a class of models in which the canonical stoichiometric relations between reactions and metabolites are specified as constraints for mathematical optimization. Layout provides the ability to encode graphical information for model diagrams. The structure for this extension mirrors the SBML core hierarchy by introducing graphical counterparts to reactions and species. The fourth approved package, Hierarchical Model Composition (comp) provides a generic framework to encode models as hierarchical entities in SBML (Smith et al., 2013). JSBML’s comp implementation provides access to elements within sub-models and interfaces with other models.

Table 1.

SBML Package status

Name Id Description JSBML support
Qualitative models qual Qualitative values for species Full
Flux balance constraints fbc Constraints based parameters Full
Layout layout Network layout topology Full
Hierarchical model composition comp Modular, hierarchical entities Full
Spatial processes spatial Location and geometries Full
Groups groups Grouping elements Full
Arrays arrays Values and entities in arrays Full
Required elements req Required model elements Full
Distributions distrib Model values as statistical distributions Full
Dynamic structures dyn Dynamic model entities Full
Rendering render Network layout style Full
Multistate and multicomponent species multi Rule based modeling Partial
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2.2.2 Draft packages

Draft specifications are available for the remaining SBML packages; they are encoded in JSBML with varying maturity. JSBML fully supports the current specifications of seven packages whose community approvals are pending: Spatial Processes (spatial, Schaff et al., 2014) specifies geometric descriptions of biochemical models’ components using a cellular coordinate system that can describe non-uniform molecular distributions, diffusive transport and spatially localized reactions; Groups (groups, Hucka and Smith, 2013) agglomerates SBML model elements and can be linked to annotations and SBO terms (Courtot et al., 2011) to contextualize sets of objects for other programmers and modelers; Arrays (arrays, Watanabe et al., 2013) extends SBML variables to include arrays of values, thereby representing repeated or regular model structures more efficiently; Required Elements (req, Smith and Hucka, 2013) allows a model to indicate which components have had their mathematical meanings changed by (e.g.) the use of another SBML package; Distributions (distrib, Moodie and Smith, 2013) encodes statistical distributions and their sampling; Dynamic Structures (dyn, Gomez et al., 2014), which supports the definition of dynamical behaviors for model entities; and Rendering (render, Gauges et al., 2011), used in conjunction with layout to provide symbol and style information for diagrams. The last package, Multistate and Multicomponent Species (Zhang and Meier-Schellersheim, 2013) is still under development. The JSBML project is committed to support all SBML packages as their specifications come out and are deemed stable by the community.

2.2.3 JSBML and package formation

Some package specifications have been influenced by JSBML development. For example, protocols for validation and flattening of array constructs in the arrays package. Prior to the development of arrays in JSBML, the specification lacked important validation rules that serve as quality controls for math operations done with arrays data structures. JSBML also provided the environment to ensure arrays was compatible with other SBML L3 packages, helping to build a more robust specification. Finally, JSBML development of the arrays package was used to generate simulatable examples for the SBML L3 arrays specification. Therefore, JSBML provides a means to test and implement new package development for future versions of SBML.

3 Conclusions

JSBML version 1.0 marks the maturation of this software library as an essential component for any systems biology pipeline that runs in the Java Virtual Machine, and joins other biological exchange format interpreters (Paxtools, Demir et al., 2013; CellML API, Miller et al., 2010; SED-ML, Waltemath et al., 2011b; SBOL, Galdzicki et al., 2014; libSBGN, van Iersel et al., 2012) to support users’ ability to disseminate models in a diverse array of modeling formalisms. In addition, as SBML is updated and novel modeling techniques arise, the active, open-source community behind JSBML will continue to provide a comprehensive, computable interface for systems biology models.

Supplementary Material

Supplementary Data
supp_31_20_3383__index.html (915B, html)

Acknowledgements

The authors are grateful to Meike Aichele, Alexander Dörr, Marine Dumousseau, Akira Funahshi, Stephanie Hoffmann, Sarah R. Müller vom Hagen, Sebastian Nagel, Alexander Peltzer, Simon Schäfer and Andreas Zell.

Funding

A National Institute of Health grant for the continued development of essential SBML software support (NIH, United States, award number GM070923), European Union projects SynSys (FP7 242167) and BBSRC Signalling Institute Strategic Programme (BBS/E/B/000C0419), a National Science Foundation grant (CCF-1218095), a Marie Curie International Outgoing Fellowship within the EU 7th Framework Programme for Research and Technological Development (project AMBiCon, 332020), Google Inc. (Summer of Code 2014), the Federal Ministry of Education and Research (BMBF, Germany) as part of the Virtual Liver Network (grant number 0315756). AT, NEL and BØP acknowledge generous support from the Novo Nordisk Foundation that has been provided to the Center for Biosustainability at the Technical University of Denmark. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of our funding agencies. This study was funded in part by NIH HHSN272201000053C.

Conflict of interest: none declared.

References

  1. Bornstein B.J., et al. (2008) LibSBML: an API library for SBML. Bioinformatics, 24, 880–881. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Breitling R., et al. (2008) A structured approach for the engineering of biochemical network models, illustrated for signalling pathways. Brief. Bioinform., 9, 404–421. [DOI] [PubMed] [Google Scholar]
  3. Chaouiya C., et al. (2013) SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC Syst. Biol., 7, 135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Courtot M., et al. (2011) Controlled vocabularies and semantics in systems biology. Mol. Syst. Biol., 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Demir E., et al. (2013) Using biological pathway data with Paxtools. PLoS Comput. Biol., 9, e1003194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Dräger A., Palsson BØ. (2014) Improving collaboration by standardization efforts in systems biology. Frontiers in Bioengineering, 2, http://journal.frontiersin.org/article/10.3389/fbioe.2014.00061/. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dräger A., et al. (2011) JSBML: a flexible Java library for working with SBML. Bioinformatics, 27, 2167–2168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Funahashi A., et al. (2008) CellDesigner 3.5: a versatile modeling tool for biochemical networks. Proc. IEEE, 96, 1254–1265. [Google Scholar]
  9. Galdzicki M., et al. (2014) The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology. Nature biotechnology, 32, 545–550. [DOI] [PubMed] [Google Scholar]
  10. Gauges R., et al. (2011) Complementing Layout Information with Render Information in SBML Files. SBML.org (1 January 2015, date last accessed). [Google Scholar]
  11. Gomez H., et al. (2014) Dynamic Structures, Version 1 Release .1. SBML.org (5 March 2015, date last accessed). [Google Scholar]
  12. Gonzalez Gonzales A., et al. (2006) GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks. BioSystems, 84, 91–100. [DOI] [PubMed] [Google Scholar]
  13. Hucka M., Smith L.P. (2013) Groups, Version 1 Release .4. SBML.org (1 January 2015, date last accessed). [Google Scholar]
  14. Hucka M., et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19, 524–531. [DOI] [PubMed] [Google Scholar]
  15. Lewis N.E., et al. (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol., 10, 291–305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Madsen C., et al. (2012) Design and test of genetic circuits using iBioSim. Design Test Comput. IEEE, 29, 32–39. [Google Scholar]
  17. Miller A.K., et al. (2010) An overview of the CellML API and its implementation. BMC Bioinformatics, 11, 178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Moodie S.L., Smith L.P.(2013) Distributions, Version .13. SBML.org (5 March 2015, date last accessed). [Google Scholar]
  19. Olivier B.G., Bergmann F.T. (2013) Flux Balance Constraints, Version 1 Release 1. CO.MBINE.org (1 January 2015, date last accessed). [Google Scholar]
  20. Prlić A., et al. (2012) BioJava: an open-source framework for bioinformatics in 2012. Bioinformatics, 28, 2693–2695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Schaff J.C., et al. (2014) Spatial Processes, Version 1 Release .88. SBML.org (5 March 2015, date last accessed). [Google Scholar]
  22. Shmulevich I., et al. (2002) From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Proc. IEEE, 90, 1778–1792. [Google Scholar]
  23. Smith L.P., Hucka M. (2013) Required Elements. SBML.org (1 January 2015, date last accessed). [Google Scholar]
  24. Smith L.P., et al. (2013) Hierarchical Model Composition, Version 1 Release 3. CO.MBINE.org (1 January 2015, date last accessed). [Google Scholar]
  25. van Iersel M.P., et al. (2012) Software support for SBGN maps: SBGN-ML and LibSBGN. Bioinformatics, 28, 2016–2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Waltemath D., et al. (2011a) Meeting report from the fourth meeting of the computational modeling in biology network (COMBINE). Stand. Genomic Sci., 9, 3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Waltemath D., et al. (2011b) Reproducible computational biology experiments with SED-ML-the simulation experiment description markup language. BMC Syst. Biol., 5, 198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Watanabe L., et al. (2013) Arrays, Version 1 Release .1a. SBML.org (1 January 2015, date last accessed). [Google Scholar]
  29. Wrzodek C., et al. (2011) KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats. Bioinformatics, 27, 2314–2315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Zhang F., Meier-Schellersheim M. (2013) Multistate, Multicomponent, and Multicompartment Species, Version 1 Release .1. SBML.org (1 January 2015, date last accessed). [Google Scholar]

Associated Data

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Supplementary Materials

Supplementary Data
supp_31_20_3383__index.html (915B, html)
supp_btv341_sbml_lvl3_extensions.pdf (1.7MB, pdf)

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