Synthetic Biology Open Language Research Articles

Genetic Network Design Automation with LOICA.

Genetic design automation (GDA) is the use of computer-aided design (CAD) in designing genetic networks. GDA tools are necessary to create more complex synthetic genetic networks in a high-throughput fashion. At the core of these tools is the abstraction of a hierarchy of standardized components. The components' input, output, and interactions must be captured and parametrized from relevant experimental data. Simulations of genetic networks should use those parameters and include the experimental context to be compared with the experimental results.This chapter introduces Logical Operators for Integrated Cell Algorithms (LOICA), a Python package used for designing, modeling, and characterizing genetic networks using a simple object-oriented design abstraction. LOICA represents different biological and experimental components as classes that interact to generate models. These models can be parametrized by direct connection to the Flapjack experimental data management platform to characterize abstracted components with experimental data. The models can be simulated using stochastic simulation algorithms or ordinary differential equations with varying noise levels. The simulated data can be managed and published using Flapjack alongside experimental data for comparison. LOICA genetic network designs can be represented as graphs and plotted as networks for visual inspection and serialized as Python objects or in the Synthetic Biology Open Language (SBOL) format for sharing and use in other designs.

LibSBOLj3: A graph-based library for design and data exchange in synthetic biology.

The Synthetic Biology Open Language version 3 data standard provides a graph-based approach to exchange information about biological designs. The new data model has major updates and offers several features for software tools. Here, we present libSBOLj3 to facilitate data exchange and provide interoperability between computer-aided design and automation tools using this standard. The library adopts a graph-based approach. Tool developers can extend these graphs with application-specific information and use detailed validation reports to identify errors and interoperability issues and apply best practice rules. The libSBOLj3 library is implemented in Java and can be downloaded or used as a Maven dependency. The open-source project, code examples and documentation about accessing and using the library are available via GitHub at https://github.com/SynBioDex/libSBOLj3.

Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022

This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2022 special issue presents three updates to the standards: CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This document can also be used to identify the latest specifications for all COMBINE standards. In addition, this editorial provides a brief overview of the COMBINE 2022 meeting in Berlin.

Synthetic biology open language (SBOL) version 3.1.0

Abstract Synthetic 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 bench scientists and scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.1.0, which improves on version 3.0.0 by including a number of corrections and clarifications as well as several other updates and enhancements. First, this version includes a complete set of validation rules for checking whether documents are valid SBOL 3. Second, the best practices section has been moved to an online repository that allows for more rapid and interactive of sharing these conventions. Third, it includes updates based upon six community approved enhancement proposals. Two enhancement proposals are related to the representation of an object’s namespace. In particular, the Namespace class has been removed and replaced with a namespace property on each class. Another enhancement is the generalization of the CombinatorialDeriviation class to allow direct use of Features and Measures. Next, the Participation class now allow Interactions to be participants to describe higher-order interactions. Another change is the use of Sequence Ontology terms for Feature orientation. Finally, this version of SBOL has generalized from using Unique Reference Identifiers (URIs) to Internationalized Resource Identifiers (IRIs) to support international character sets.

Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa.

Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel-SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.

Cryptococcus neoformans Database in Synthetic Biology Open Language.

ABSTRACTCryptococcus neoformans is the etiologic agent of cryptococcosis, a lethal worldwide disease. Synthetic biology could contribute to its better understanding through engineering genetic networks. However, its major challenge is the requirement of accessible genetic parts. The database presented here provides 23 biological parts for this organism in Synthetic Biology Open Language.

PySBOL3: SBOL3 for Python Programmers.

The Synthetic Biology Open Language version 3 (SBOL3) provides a data model for representation of synthetic biology information across multiple scales and throughout the design-build-test-learn workflow. To support practical use of this data model, we have developed pySBOL3, a Python library that allows programmers to create and edit SBOL3 documents. Here we describe this library and key engineering decisions in its design. The resulting implementation is a compact and maintainable core that provides both a familiar, pythonic interface for manipulating SBOL3 objects as well as mechanisms for building additional extensions and representations on this base.

SynBio2Easy-a biologist-friendly tool for batch operations on SBOL designs with Excel inputs.

Practical delivery of Findable, Accessible, Reusable and Interoperable principles for research data management requires expertise, time resource, (meta)data standards and formats, software tools and public repositories. The Synthetic Biology Open Language (SBOL2) metadata standard enables FAIR sharing of the designs of synthetic biology constructs, notably in the repository of the SynBioHub platform. Large libraries of such constructs are increasingly easy to produce in practice, for example, in DNA foundries. However, manual curation of the equivalent libraries of designs remains cumbersome for a typical lab researcher, creating a barrier to data sharing. Here, we present a simple tool SynBio2Easy, which streamlines and automates operations on multiple Synthetic Biology Open Language (SBOL) designs using Microsoft Excel® tables as metadata inputs. The tool provides several utilities for manipulation of SBOL documents and interaction with SynBioHub: for example, generation of a library of plasmids based on an original design template, bulk deposition into SynBioHub, or annotation of existing SBOL component definitions with notes and authorship information. The tool was used to generate and deposit a collection of 3661 cyanobacterium Synechocystis plasmids into the public SynBioHub repository. In the process of developing the software and uploading these data, we evaluated some aspects of the SynBioHub platform and SBOL ecosystem, and we discuss proposals for improvement that could benefit the user community. With software such as SynBio2Easy, we aim to deliver a user-driven tooling to make FAIR a reality at all stages of the project lifecycle in synthetic biology research. Graphical

Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits.

Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits for which manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications.

Curation Principles Derived from the Analysis of the SBOL iGEM Data Set.

As an engineering endeavor, synthetic biology requires effective sharing of genetic design information that can be reused in the construction of new designs. While there are a number of large community repositories of design information, curation of this information has been limited. This in turn limits the ways in which design information can be put to use. The aim of this work was to improve this situation by creating a curated library of parts from the International Genetically Engineered Machines (iGEM) registry data set. To this end, an analysis of the Synthetic Biology Open Language (SBOL) version of the iGEM registry was carried out using four different approaches-simple statistics, SnapGene autoannotation, SYNBICT autoannotation, and expert analysis-the results of which are presented herein. Key challenges encountered include the use of free text, insufficient part provenance, part duplication, lack of part removal, and insufficient continuous curation. On the basis of these analyses, the focus has shifted from the creation of a curated iGEM part library to instead the extraction of a set of lessons, which are presented here. These lessons can be exploited to facilitate the creation and curation of other part libraries using a simpler and less labor intensive process.

ParaSBOLv: a foundation for standard-compliant genetic design visualization tools.

Diagrams constructed from standardized glyphs are central to communicating complex design information in many engineering fields. For example, circuit diagrams are commonplace in electronics and allow for a suitable abstraction of the physical system that helps support the design process. With the development of the Synthetic Biology Open Language Visual (SBOLv), bioengineers are now positioned to better describe and share their biological designs visually. However, the development of computational tools to support the creation of these diagrams is currently hampered by an excessive burden in maintenance due to the large and expanding number of glyphs present in the standard. Here, we present a Python package called paraSBOLv that enables access to the full suite of SBOLv glyphs through the use of machine-readable parametric glyph definitions. These greatly simplify the rendering process while allowing extensive customization of the resulting diagrams. We demonstrate how the adoption of paraSBOLv can accelerate the development of highly specialized biodesign visualization tools or even form the basis for more complex software by removing the burden of maintaining glyph-specific rendering code. Looking forward, we suggest that incorporation of machine-readable parametric glyph definitions into the SBOLv standard could further simplify the development of tools to produce standard-compliant diagrams and the integration of visual standards across fields.

Synthetic biology open language visual (SBOL Visual) version 2.3.

People 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 2.3 of SBOL Visual, which builds on the prior SBOL Visual 2.2 in several ways. First, the specification now includes higher-level “interactions with interactions,” such as an inducer molecule stimulating a repression interaction. Second, binding with a nucleic acid backbone can be shown by overlapping glyphs, as with other molecular complexes. Finally, a new “unspecified interaction” glyph is added for visualizing interactions whose nature is unknown, the “insulator” glyph is deprecated in favor of a new “inert DNA spacer” glyph, and the polypeptide region glyph is recommended for showing 2A sequences.

Flapjack: Data Management and Analysis for Genetic Circuit Characterization.

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

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

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.

Capturing Multicellular System Designs Using Synthetic Biology Open Language (SBOL).

Synthetic biology aims to develop novel biological systems and increase their reproducibility using engineering principles such as standardization and modularization. It is important that these systems can be represented and shared in a standard way to ensure they can be easily understood, reproduced, and utilized by other researchers. The Synthetic Biology Open Language (SBOL) is a data standard for sharing biological designs and information about their implementation and characterization. Previously, this standard has only been used to represent designs in systems where the same design is implemented in every cell; however, there is also much interest in multicellular systems, in which designs involve a mixture of different types of cells with differing genotype and phenotype. Here, we show how the SBOL standard can be used to represent multicellular systems, and, hence, how researchers can better share designs with the community and reliably document intended system functionality.

Synthetic biology open language (SBOL) version 3.0.0.

Synthetic 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.

Synthetic biology open language visual (SBOL visual) version 2.2.

People 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 2.2 of SBOL Visual, which builds on the prior SBOL Visual 2.1 in several ways. First, the grounding of molecular species glyphs is changed from BioPAX to SBO, aligning with the use of SBO terms for interaction glyphs. Second, new glyphs are added for proteins, introns, and polypeptide regions (e. g., protein domains), the prior recommended macromolecule glyph is deprecated in favor of its alternative, and small polygons are introduced as alternative glyphs for simple chemicals.

Extending SynBioHub's Functionality with Plugins.

SynBioHub is a repository for synthetic genetic designs represented in the Synthetic Biology Open Language (SBOL). To integrate SynBioHub into more synthetic biology workflows, its data processing capabilities need to be expanded. To this end, a plugin interface has been developed. Plugins can be developed for data submission, visualization, and download. This framework was tested by the development of three example plugins, one of each type as follows: one allowing the submission of SnapGene files, one visualizing the course of different genetic parts, and one preparing plasmid maps for download.

ShortBOL: A Language for Scripting Designs for Engineered Biological Systems Using Synthetic Biology Open Language (SBOL).

The Synthetic Biology Open Language (SBOL) is an emerging synthetic biology data exchange standard, designed primarily for unambiguous and efficient machine-to-machine communication. However, manual editing of SBOL is generally difficult for nontrivial designs. Here, we describe ShortBOL, a lightweight SBOL scripting language that bridges the gap between manual editing, visual design tools, and direct programming. ShortBOL is a shorthand textual language developed to enable users to create SBOL designs quickly and easily, without requiring strong programming skills or visual design tools.

SEVA 3.0: an update of the Standard European Vector Architecture for enabling portability of genetic constructs among diverse bacterial hosts.

The Standard European Vector Architecture 3.0 database (SEVA-DB 3.0, http://seva.cnb.csic.es) is the update of the platform launched in 2013 both as a web-based resource and as a material repository of formatted genetic tools (mostly plasmids) for analysis, construction and deployment of complex bacterial phenotypes. The period between the first version of SEVA-DB and the present time has witnessed several technical, computational and conceptual advances in genetic/genomic engineering of prokaryotes that have enabled upgrading of the utilities of the updated database. Novelties include not only a more user-friendly web interface and many more plasmid vectors, but also new links of the plasmids to advanced bioinformatic tools. These provide an intuitive visualization of the constructs at stake and a range of virtual manipulations of DNA segments that were not possible before. Finally, the list of canonical SEVA plasmids is available in machine-readable SBOL (Synthetic Biology Open Language) format. This ensures interoperability with other platforms and affords simulations of their behaviour under different in vivo conditions. We argue that the SEVA-DB will remain a useful resource for extending Synthetic Biology approaches towards non-standard bacterial species as well as genetically programming new prokaryotic chassis for a suite of fundamental and biotechnological endeavours.