Skip to content
BY-NC-ND 4.0 license Open Access Published by De Gruyter April 2, 2018

Synthetic Biology Open Language (SBOL) Version 2.2.0

  • Robert Sidney Cox , Curtis Madsen , James Alastair McLaughlin , Tramy Nguyen , Nicholas Roehner , Bryan Bartley , Jacob Beal , Michael Bissell , Kiri Choi , Kevin Clancy , Raik Grünberg , Chris Macklin , Goksel Misirli , Ernst Oberortner , Matthew Pocock , Meher Samineni , Michael Zhang , Zhen Zhang , Zach Zundel , John H. Gennari , Chris Myers EMAIL logo , Herbert Sauro and Anil Wipat


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.

Received: 2018-1-1
Accepted: 2018-2-1
Published Online: 2018-4-2

©2018, Robert Sidney Cox et al., published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Downloaded on 4.10.2023 from
Scroll to top button