Synthetic Biology Open Language (SBOL) Version 2.3

Abstract

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 is 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 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year’s JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

BioMaster: An Integrated Database and Analytic Platform to Provide Comprehensive Information About BioBrick Parts

“…Several tools for analyses and visualization of BioBrick parts were integrated in BioMaster. We integrated a JAVA-based visual aiding design software, SBOL designer, into BioMaster to provide visualization and designing of biological components ( Figure 3A ) ( Roehner et al, 2016 ; Madsen et al, 2019 ). On the other hand, concerning the limit amount of BioBrick entries, BioMaster provides two prediction tools to conduct bioinformatic exploration on unknown sequences: the promoter prediction ( Umarov and Solovyev, 2017 ) and the enzyme commission (EC) number prediction ( Dalkiran et al, 2018 ).…”

The Synthetic Biology Open Language (SBOL) Version 3: Simplified Data Exchange for Bioengineering

“…Since the publication of SBOL2 in 2015, 46 SEPs have been opened, as community experience in deployment of SBOL revealed some of the practical challenges and opportunities for enhancement. Of these SEPs, twelve were implemented as incremental updates to SBOL2, resulting in significant milestones in SBOL version 2.1.0 (Beal et al, 2016 ), which introduced feature annotation and the encoding of provenance information to trace the history of designs; SBOL version 2.2.0 (Cox et al, 2018 ), which introduced support for combinatorial designs; and SBOL version 2.3.0 (Madsen et al, 2019b ), which introduced extensions to support measurements, parameters, and the organization and attachment of experimental data.…”

“…In particular, the previous major revision, SBOL2 (Bartley et al, 2015 ; Roehner et al, 2016 ), generalized the data model to allow for designs to include not only DNA components, but also other molecular species such as RNAs, proteins, larger components of a system such as whole cells, and links to models encoded using complementary standards such as SBML (Hucka et al, 2003 ). The standard was also incrementally expanded with several minor revisions (Beal et al, 2016 ; Cox et al, 2018 ; Madsen et al, 2019b ) to capture information about combinatorial design libraries, external file attachments, sequence construction, experimental tests, and measurements. Furthermore, by leveraging the Provenance Ontology (PROV-O) (Lebo et al, 2013 ), SBOL2 can capture provenance information to link and trace information and processes throughout the entire design-build-test-learn cycle.…”

Biological Materials: The Next Frontier for Cell-Free Synthetic Biology

“…These data sets also enable accompanying quantitative modeling that can potentially predict unknown model parameters, such as transcription factor binding affinities or cell-free energy utilization (Bujara and Panke, 2012;Moore et al, 2018). These data can potentially be entered into biopart data repositories (Ham et al, 2012;Bultelle et al, 2016;McLaughlin et al, 2018) for use in machine learning-enhanced design of experiments (DoE) approaches, to speed up materials development (Liu et al, 2017;Exley et al, 2019;Madsen et al, 2019). High-throughput cell-free experiments can also tease apart where cell-free reactions are fundamentally different to native intracellular environments.…”