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Synthetic Biology Open Language (SBOL) Version 2.2.0

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor...

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Detalles Bibliográficos
Autores principales: Cox, Robert Sidney, Madsen, Curtis, McLaughlin, James Alastair, Nguyen, Tramy, Roehner, Nicholas, Bartley, Bryan, Beal, Jacob, Bissell, Michael, Choi, Kiri, Clancy, Kevin, Grünberg, Raik, Macklin, Chris, Misirli, Goksel, Oberortner, Ernst, Pocock, Matthew, Samineni, Meher, Zhang, Michael, Zhang, Zhen, Zundel, Zach, Gennari, John H., Myers, Chris, Sauro, Herbert, Wipat, Anil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167039/
https://www.ncbi.nlm.nih.gov/pubmed/29605823
http://dx.doi.org/10.1515/jib-2018-0001
Descripción
Sumario:Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.2.0 of SBOL that builds upon version 2.1.0 published in last year’s JIB special issue. In particular, SBOL 2.2.0 includes improved description and validation rules for genetic design provenance, an extension to support combinatorial genetic designs, a new class to add non-SBOL data as attachments, a new class for genetic design implementations, and a description of a methodology to describe the entire design-build-test-learn cycle within the SBOL data model.