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The synthetic biology research community describes a standard language for exchanging designs of biological 'parts'. The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-driven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.

Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.

Jean Peccoud1, J Christopher Anderson2, Deepak Chandran3, Douglas Densmore4, Michal Galdzicki5, Matthew W Lux1, Cesar A Rodriguez6, Guy-Bart Stan7 & Herbert M Sauro3 1Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA. 2Department of Bioengineering, QB3: California Institute for Quantitative Biological Research, University of California, Berkeley, California, USA. 3Department of Bioengineering, University of Washington, Seattle, Washington, USA. 4Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA.5Biomedical and Health Informatics, University of Washington, Seattle, Washington, USA. 6BIOFAB, Emeryville, California, USA. 7Department of Bioengineering and Centre for Synthetic Biology and Innovation, Imperial College London, London, UK. e-mail: peccoud@vt.edu

Nat. Biotechnol. 27 735–741 (2009); published online 7 August 2009; corrected after print 11 August 2009 In the version of this article initially published, the wrong versions of Figures 1, 2 and 3 were used. The error has been corrected in the HTML and PDF versions of the article.