CSB model representations

Systems Model Representations

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As in any other applied computational discipline, systems models require their own computable abstractions and - hopefully - standards for data- and results exchange.

 

Introductory reading

Kohl (2011) Standards, databases, and modeling tools in systems biology. Methods Mol Biol 696:413-27. (pmid: 21063963)

[ PubMed ] [ DOI ] Modeling is a means for integrating the results from Genomics, Transcriptomics, Proteomics, and Metabolomics experiments and for gaining insights into the interaction of the constituents of biological systems. However, sharing such large amounts of frequently heterogeneous and distributed experimental data needs both standard data formats and public repositories. Standardization and a public storage system are also important for modeling due to the possibility of sharing models irrespective of the used software tools. Furthermore, rapid model development strongly benefits from available software packages that relieve the modeler of recurring tasks like numerical integration of rate equations or parameter estimation. In this chapter, the most common standard formats used for model encoding and some of the major public databases in this scientific field are presented. The main features of currently available modeling software are discussed and proposals for the application of such tools are given.

 

Contents

• Abstractions and Standards
• SBML
• CellML

   

Further reading and resources

Krause et al. (2011) Sustainable model building the role of standards and biological semantics. Meth Enzymol 500:371-95. (pmid: 21943907)

[ PubMed ] [ DOI ] Systems biology models can be reused within new simulation scenarios, as parts of more complex models or as sources of biochemical knowledge. Reusability does not come by itself but has to be ensured while creating a model. Most important, models should be designed to remain valid in different contexts-for example, for different experimental conditions-and be published in a standardized and well-documented form. Creating reusable models is worthwhile, but it requires some efforts when a model is developed, implemented, documented, and published. Minimum requirements for published systems biology models have been formulated by the MIRIAM initiative. Main criteria are completeness of information and documentation, availability of machine-readable models in standard formats, and semantic annotations connecting the model elements with entries in biological Web resources. In this chapter, we discuss the assumptions behind bottom-up modeling; present important standards like MIRIAM, the Systems Biology Markup Language (SBML), and the Systems Biology Graphical Notation (SBGN); and describe software tools and services for handling semantic annotations. Finally, we show how standards can facilitate the construction of large metabolic network models.

SBML The systems Biology Markup Language

Hucka et al. (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524-31. (pmid: 12611808)

[ PubMed ] [ DOI ] MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

CellML The CellML project at the Auckland Bioengineering Institute

Lloyd et al. (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85:433-50. (pmid: 15142756)

[ PubMed ] [ DOI ] Advances in biotechnology and experimental techniques have lead to the elucidation of vast amounts of biological data. Mathematical models provide a method of analysing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining cell models so they can, for example, be exchanged across the World Wide Web, and also read into simulation software in a consistent format and (2) eliminating the errors which arise with the current method of model publication. CellML has evolved to meet these needs of the modelling community. CellML is a free, open-source, eXtensible markup language based standard for defining mathematical models of cellular function. In this paper we summarise the structure of CellML, its current applications (including biological pathway and electrophysiological models), and its future development--in particular, the development of toolsets and the integration of ontologies.

BioPAX - Biological Pathway Exchange - a pathway description standard engineered for compatibility with SBML and CellML in areas where the descriptions overlap.

Demir et al. (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28:935-42. (pmid: 20829833)

[ PubMed ] [ DOI ] Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.