Synthetic Biology Open Language (SBOL) Version 2.1.0

SummarySynthetic 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 2.1 of SBOL that builds upon version 2.0 published in last year’s JIB special issue. In particular, SBOL 2.1 includes improved rules for what constitutes a valid SBOL document, new role fields to simplify the expression of sequence features and how components are used in context, and new best practices descriptions to improve the exchange of basic sequence topology information and the description of genetic design provenance, as well as miscellaneous other minor improvements.

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Synthetic Biology Open Language (SBOL) Version 2.0.0

Journal of Integrative Bioinformatics ◽

10.1515/jib-2015-272 ◽

2015 ◽

Vol 12 (2) ◽

pp. 902-991 ◽

Cited By ~ 14

Author(s):

Bryan Bartley ◽

Jacob Beal ◽

Kevin Clancy ◽

Goksel Misirli ◽

Nicholas Roehner ◽

...

Keyword(s):

Metabolic Engineering ◽

Synthetic Biology ◽

Molecular Biology ◽

Best Practices ◽

Data Model ◽

Biological Design ◽

Exchange Of Information ◽

Functional Aspects ◽

Version 2.0 ◽

Synthetic Biology Open Language

Summary 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 2.0 of SBOL, introducing a standardized format for the electronic exchange of information on the structural and functional aspects of biological designs. The standard has been designed to support the explicit and unambiguous description of biological designs by means of a well defined data model. The standard also includes rules and best practices on how to use this data model and populate it with relevant design details. The publication of this specification is intended to make these capabilities more widely accessible to potential developers and users in the synthetic biology community and beyond.

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Synthetic Biology Open Language (SBOL) Version 2.3

Journal of Integrative Bioinformatics ◽

10.1515/jib-2019-0025 ◽

2019 ◽

Vol 16 (2) ◽

Cited By ~ 6

Author(s):

Curtis Madsen ◽

Angel Goñi Moreno ◽

Umesh P ◽

Zachary Palchick ◽

Nicholas Roehner ◽

...

Keyword(s):

Experimental Data ◽

Metabolic Engineering ◽

Systems Biology ◽

Synthetic Biology ◽

Molecular Biology ◽

Special Issue ◽

Design Elements ◽

Biological Design ◽

Support Organization ◽

Synthetic Biology Open Language

AbstractSynthetic 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.

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Synthetic Biology Open Language (SBOL) Version 2.2.0

Journal of Integrative Bioinformatics ◽

10.1515/jib-2018-0001 ◽

2018 ◽

Vol 15 (1) ◽

Cited By ~ 10

Author(s):

Robert Sidney Cox ◽

Curtis Madsen ◽

James Alastair McLaughlin ◽

Tramy Nguyen ◽

Nicholas Roehner ◽

...

Keyword(s):

Metabolic Engineering ◽

Synthetic Biology ◽

Molecular Biology ◽

Data Model ◽

Special Issue ◽

Biological Design ◽

New Class ◽

Exchange Of Information ◽

Design Build ◽

Synthetic Biology Open Language

AbstractSynthetic 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.

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Synthetic Biology Open Language Visual (SBOL Visual) Version 2.0

Journal of Integrative Bioinformatics ◽

10.1515/jib-2017-0074 ◽

2018 ◽

Vol 15 (1) ◽

Cited By ~ 8

Author(s):

Robert Sidney Cox ◽

Curtis Madsen ◽

James McLaughlin ◽

Tramy Nguyen ◽

Nicholas Roehner ◽

...

Keyword(s):

Synthetic Biology ◽

Best Practices ◽

Molecular Species ◽

Data Model ◽

Functional Relationships ◽

Version 2.0 ◽

Synthetic Biology Open Language ◽

And Function ◽

The Relationship ◽

Biological Organisms

AbstractPeople who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.

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Special Issue “Metabolic Engineering and Synthetic Biology Volume 2”

Metabolites ◽

10.3390/metabo11010035 ◽

2021 ◽

Vol 11 (1) ◽

pp. 35

Author(s):

An N. T. Phan ◽

Lars M. Blank

Keyword(s):

Metabolic Engineering ◽

Synthetic Biology ◽

Special Issue ◽

Subsequent Increase ◽

Increasing Demand

In times of ever-increasing demand for chemicals and the subsequent increase in CO2 in the atmosphere, we have to intensify our efforts to establish a circular (bio) economy [...]

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Synthetic DNA and RNA Programming

Genes ◽

10.3390/genes10070523 ◽

2019 ◽

Vol 10 (7) ◽

pp. 523

Author(s):

O’Donoghue ◽

Heinemann

Keyword(s):

Synthetic Biology ◽

Molecular Biology ◽

Molecular Basis ◽

Molecular Mechanisms ◽

Research Articles ◽

Original Research ◽

Special Issue ◽

Synthetic Dna ◽

Dna And Rna ◽

Rna And Dna

Synthetic biology is a broad and emerging discipline that capitalizes on recent advances in molecular biology, genetics, protein and RNA engineering as well as omics technologies. Together these technologies have transformed our ability to reveal the biology of the cell and the molecular basis of disease. This Special Issue on “Synthetic RNA and DNA Programming” features original research articles and reviews, highlighting novel aspects of basic molecular biology and the molecular mechanisms of disease that were uncovered by the application and development of novel synthetic biology-driven approaches.

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Specifications of standards in systems and synthetic biology: status and developments in 2021

Journal of Integrative Bioinformatics ◽

10.1515/jib-2021-0026 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Falk Schreiber ◽

Padraig Gleeson ◽

Martin Golebiewski ◽

Thomas E. Gorochowski ◽

Michael Hucka ◽

...

Keyword(s):

Synthetic Biology ◽

Simulation Experiment ◽

Markup Language ◽

Special Issue ◽

Language Level ◽

Experiment Description ◽

Level 1 ◽

Synthetic Biology Open Language

Abstract This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.

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Synthetic biology open language (SBOL) version 3.0.0

Journal of Integrative Bioinformatics ◽

10.1515/jib-2020-0017 ◽

2020 ◽

Vol 17 (2-3) ◽

Cited By ~ 1

Author(s):

Hasan Baig ◽

Pedro Fontanarrosa ◽

Vishwesh Kulkarni ◽

James Alastair McLaughlin ◽

Prashant Vaidyanathan ◽

...

Keyword(s):

Synthetic Biology ◽

Data Model ◽

Experimental Measurements ◽

Structural Constraint ◽

Software Developers ◽

Biological Design ◽

Intended Behavior ◽

Exchange Information ◽

Synthetic Biology Open Language ◽

Sequence Constraints

AbstractSynthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. 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, following an open community process involving both wet bench scientists and dry scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.0.0, which condenses and simplifies previous versions of SBOL based on experiences in deployment across a variety of scientific and industrial settings. In particular, SBOL 3.0.0, (1) separates sequence features from part/sub-part relationships, (2) renames Component Definition/Component to Component/Sub-Component, (3) merges Component and Module classes, (4) ensures consistency between data model and ontology terms, (5) extends the means to define and reference Sub-Components, (6) refines requirements on object URIs, (7) enables graph-based serialization, (8) moves Systems Biology Ontology (SBO) for Component types, (9) makes all sequence associations explicit, (10) makes interfaces explicit, (11) generalizes Sequence Constraints into a general structural Constraint class, and (12) expands the set of allowed constraints.

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