Abstract
The Synthetic Biology Open Language (SBOL) is a community-developed data standard that allows knowledge about biological designs to be captured using a machine-tractable, ontology-backed representation that is built using Semantic Web technologies. While early versions of SBOL focused only on the description of DNA-based components and their sub-components, SBOL can now be used to represent knowledge across multiple scales and throughout the entire synthetic biology workflow, from the specification of a single molecule or DNA fragment through to multicellular systems containing multiple interacting genetic circuits. The third major iteration of the SBOL standard, SBOL3, is an effort to streamline and simplify the underlying data model with a focus on real-world applications, based on experience from the deployment of SBOL in a variety of scientific and industrial settings. Here, we introduce the SBOL3 specification both in comparison to previous versions of SBOL and through practical examples of its use.
Highlights
Synthetic biology builds upon advances in genetics, molecular biology, metabolic engineering, and other related disciplines by applying principles such as modularization, standardization, and a design-build-test-learn workflow to enable the engineering of biological systems, just as software engineering does to the design of computer programs (Endy, 2005)
SBOL version 3 (SBOL3) contains ten main top-level classes to support the various aspects of the design-build-test-learn workflow (Figure 2)
The Implementation class corresponds to the build stage of the synthetic biology lifecycle and is used to represent physical entities such as a sample of plasmid, a stab of transformed bacteria, or an aliquot of liquid culture
Summary
Synthetic biology builds upon advances in genetics, molecular biology, metabolic engineering, and other related disciplines by applying principles such as modularization, standardization, and a design-build-test-learn workflow to enable the engineering of biological systems, just as software engineering does to the design of computer programs (Endy, 2005). Information being exchanged often covers multiple aspects of a design, including nucleic acid sequences (e.g., the sequence that encodes an enzyme or transcription factor), molecular interactions that a designer intends to result from the introduction of a chosen sequence (e.g., chemical modification of metabolites or regulation of gene expression), as well as details regarding the construction of the final engineered strain (e.g., nucleic acid synthesis, assembly, and the transformation of a chosen cell type) and associated experiments and data All of these diverse perspectives need to be effectively integrated to facilitate the effective engineering of biological systems
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