Publications by authors named "Chris J Myers"

39 Publications

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

Front Bioeng Biotechnol 2020 11;8:1009. Epub 2020 Sep 11.

Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO, United States.

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.
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http://dx.doi.org/10.3389/fbioe.2020.01009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516281PMC
September 2020

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Mol Syst Biol 2020 08;16(8):e9110

Management & IT Consulting Division, Mizuho Information & Research Institute, Inc., Tokyo, Japan.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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http://dx.doi.org/10.15252/msb.20199110DOI Listing
August 2020

Systems Biology Markup Language (SBML) Level 3 Package: Distributions, Version 1, Release 1.

J Integr Bioinform 2020 Jul 20;17(2-3). Epub 2020 Jul 20.

California Institute of Technology, Pasadena, USA.

Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not include an explicit mechanism to include inexact or stochastic values in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Distributions package for SBML Level 3 adds the necessary features to allow models to encode information about the distribution and uncertainty of values underlying a quantity.
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http://dx.doi.org/10.1515/jib-2020-0018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756622PMC
July 2020

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE).

J Integr Bioinform 2020 Jun 29;17(2-3). Epub 2020 Jun 29.

Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK.

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.
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http://dx.doi.org/10.1515/jib-2020-0005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756615PMC
June 2020

Synthetic biology open language (SBOL) version 3.0.0.

J Integr Bioinform 2020 Jun 25;17(2-3). Epub 2020 Jun 25.

University of Utah, Salt Lake City, USA.

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.
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http://dx.doi.org/10.1515/jib-2020-0017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756618PMC
June 2020

SBOL Visual 2 Ontology.

ACS Synth Biol 2020 04 8;9(4):972-977. Epub 2020 Apr 8.

Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States.

Standardizing the visual representation of genetic parts and circuits is essential for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has up to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology.
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http://dx.doi.org/10.1021/acssynbio.0c00046DOI Listing
April 2020

SBOLExplorer: Data Infrastructure and Data Mining for Genetic Design Repositories.

ACS Synth Biol 2019 10 27;8(10):2287-2294. Epub 2019 Sep 27.

Department of Electrical and Computer Engineering , University of Utah , Salt Lake City , Utah 84112 , United States.

This paper describes SBOLExplorer, a system that is used to provide intuitive searching within the SynBioHub genetic design repository. SynBioHub stores genetic constructs encoded in the SBOL data format. These constructs can represent genetic parts, circuits, and sequences. These constructs are often numerous, exist in various states of completeness and documentation, and do not lend themselves to simple searching and discovery. In particular, this paper focuses on improving the search capabilities of SynBioHub. Inspiration is drawn from the techniques used to organize and search over the World Wide Web, a linked data set with many of the same properties of the SBOL data in SynBioHub. SBOLExplorer integrates these methods into SynBioHub's data representation and search, providing significant improvement over the previous search implementation based on pattern-matching.
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http://dx.doi.org/10.1021/acssynbio.9b00089DOI Listing
October 2019

Specifying Combinatorial Designs with the Synthetic Biology Open Language (SBOL).

ACS Synth Biol 2019 07 1;8(7):1519-1523. Epub 2019 Jul 1.

University of Utah , Salt Lake City , Utah 84112 , United States.

As improvements in DNA synthesis technology and assembly methods make combinatorial assembly of genetic constructs increasingly accessible, methods for representing genetic constructs likewise need to improve to handle the exponential growth of combinatorial design space. To this end, we present a community accepted extension of the SBOL data standard that allows for the efficient and flexible encoding of combinatorial designs. This extension includes data structures for representing genetic designs with "variable" components that can be implemented by choosing one of many linked designs for existing genetic parts or constructs. We demonstrate the representational power of the SBOL combinatorial design extension through case studies on metabolic pathway design and genetic circuit design, and we report the expansion of the SBOLDesigner software tool to support users in creating and modifying combinatorial designs in SBOL.
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http://dx.doi.org/10.1021/acssynbio.9b00092DOI Listing
July 2019

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2.

J Integr Bioinform 2019 Jun 20;16(2). Epub 2019 Jun 20.

NIAID/NIH, Bethesda, MD, USA.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/.
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http://dx.doi.org/10.1515/jib-2019-0021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798823PMC
June 2019

pySBOL: A Python Package for Genetic Design Automation and Standardization.

ACS Synth Biol 2019 07 6;8(7):1515-1518. Epub 2018 Dec 6.

Department of Bioengineering , University of Washington , Seattle , Washington 98195 , United States.

This paper presents pySBOL, a software library for computer-aided design of synthetic biological systems in the Python scripting language. This library provides an easy-to-use, object-oriented, application programming interface (API) with low barrier of entry for synthetic biology application developers. The pySBOL library enables reuse of genetic parts and designs through standardized data exchange with biological parts repositories and software tools that communicate using the Synthetic Biology Open Language (SBOL). In addition, pySBOL supports data management of design-build-test-learn workflows for individual laboratories as well as large, distributed teams of synthetic biologists. PySBOL also lets users add custom data to SBOL files to support the specific data requirements of their research. This extensibility helps users integrate software tool chains and develop workflows for new applications. These features and others make the pySBOL library a valuable tool for supporting engineering practices in synthetic biology. Documentation and installation instructions can be found at pysbol2.readthedocs.io .
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http://dx.doi.org/10.1021/acssynbio.8b00336DOI Listing
July 2019

sboljs: Bringing the Synthetic Biology Open Language to the Web Browser.

ACS Synth Biol 2019 01 8;8(1):191-193. Epub 2019 Jan 8.

School of Computing , Newcastle University , Newcastle upon Tyne NE4 5TG , U.K.

The Synthetic Biology Open Language (SBOL) is a data standard for the representation of engineered biological systems. SBOL is implemented in the form of software libraries which can be used to add SBOL support to both new and existing software tools. While existing libraries allow for software to be developed that runs on a server or is installed locally, they lack the capability to create SBOL software that runs directly in a Web browser. Here, we address this issue by presenting sboljs, a JavaScript software library for SBOL that is capable of being used both on the server and in the Web browser.
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http://dx.doi.org/10.1021/acssynbio.8b00338DOI Listing
January 2019

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core.

J Integr Bioinform 2018 Mar 9;15(1). Epub 2018 Mar 9.

Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.
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http://dx.doi.org/10.1515/jib-2017-0081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167032PMC
March 2018

SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology.

ACS Synth Biol 2018 02 30;7(2):682-688. Epub 2018 Jan 30.

School of Computing, Newcastle University , Newcastle upon Tyne, NE1 7RU, U.K.

The SynBioHub repository ( https://synbiohub.org ) is an open-source software project that facilitates the sharing of information about engineered biological systems. SynBioHub provides computational access for software and data integration, and a graphical user interface that enables users to search for and share designs in a Web browser. By connecting to relevant repositories (e.g., the iGEM repository, JBEI ICE, and other instances of SynBioHub), the software allows users to browse, upload, and download data in various standard formats, regardless of their location or representation. SynBioHub also provides a central reference point for other resources to link to, delivering design information in a standardized format using the Synthetic Biology Open Language (SBOL). The adoption and use of SynBioHub, a community-driven effort, has the potential to overcome the reproducibility challenge across laboratories by helping to address the current lack of information about published designs.
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http://dx.doi.org/10.1021/acssynbio.7b00403DOI Listing
February 2018

A standard-enabled workflow for synthetic biology.

Biochem Soc Trans 2017 06;45(3):793-803

Department of Electrical and Computer Engineering, University of Utah, 50 S. Central Campus Drive, Rm. 2110, Salt Lake City, UT 84112, U.S.A.

A synthetic biology workflow is composed of data repositories that provide information about genetic parts, sequence-level design tools to compose these parts into circuits, visualization tools to depict these designs, genetic design tools to select parts to create systems, and modeling and simulation tools to evaluate alternative design choices. Data standards enable the ready exchange of information within such a workflow, allowing repositories and tools to be connected from a diversity of sources. The present paper describes one such workflow that utilizes, among others, the Synthetic Biology Open Language (SBOL) to describe genetic designs, the Systems Biology Markup Language to model these designs, and SBOL Visual to visualize these designs. We describe how a standard-enabled workflow can be used to produce types of design information, including multiple repositories and software tools exchanging information using a variety of data standards. Recently, the journal has recommended the use of SBOL in their publications.
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http://dx.doi.org/10.1042/BST20160347DOI Listing
June 2017

SBOLDesigner 2: An Intuitive Tool for Structural Genetic Design.

ACS Synth Biol 2017 07 12;6(7):1150-1160. Epub 2017 May 12.

Department of Electrical and Computer Engineering, University of Utah , Salt Lake City, Utah 84112, United States.

As the Synthetic Biology Open Language (SBOL) data and visual standards gain acceptance for describing genetic designs in a detailed and reproducible way, there is an increasing need for an intuitive sequence editor tool that biologists can use that supports these standards. This paper describes SBOLDesigner 2, a genetic design automation (GDA) tool that natively supports both the SBOL data model (Version 2) and SBOL Visual (Version 1). This software is enabled to fetch and store parts and designs from SBOL repositories, such as SynBioHub. It can also import and export data about parts and designs in FASTA, GenBank, and SBOL 1 data format. Finally, it possesses a simple and intuitive user interface. This paper describes the design process using SBOLDesigner 2, highlighting new features over the earlier prototype versions. SBOLDesigner 2 is released freely and open source under the Apache 2.0 license.
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http://dx.doi.org/10.1021/acssynbio.6b00275DOI Listing
July 2017

SBOLme: a Repository of SBOL Parts for Metabolic Engineering.

ACS Synth Biol 2017 04 25;6(4):732-736. Epub 2017 Jan 25.

Department of Electrical and Computer Engineering, University of Utah , Salt Lake City, Utah 84112, United States.

The Synthetic Biology Open Language (SBOL) is a community-driven open language to promote standardization in synthetic biology. To support the use of SBOL in metabolic engineering, we developed SBOLme, the first open-access repository of SBOL 2-compliant biochemical parts for a wide range of metabolic engineering applications. The URL of our repository is http://www.cbrc.kaust.edu.sa/sbolme .
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http://dx.doi.org/10.1021/acssynbio.6b00278DOI Listing
April 2017

A Validator and Converter for the Synthetic Biology Open Language.

ACS Synth Biol 2017 07 23;6(7):1161-1168. Epub 2017 Feb 23.

Department of Electrical and Computer Engineering, University of Utah , 50 S. Central Campus Drive, MEB Room 2110, Salt Lake City, Utah 84112, United States.

This paper presents a new validation and conversion utility for the Synthetic Biology Open Language (SBOL). This utility can be accessed directly in software using the libSBOLj library, through a web interface, or using a web service via RESTful API calls. The validator checks all required and best practice rules set forth in the SBOL specification document, and it reports back to the user the location within the document of any errors found. The converter is capable of translating from/to SBOL 1, GenBank, and FASTA formats to/from SBOL 2. The SBOL Validator/Converter utility is released freely and open source under the Apache 2.0 license. The online version of the validator/converter utility can be found here: http://www.async.ece.utah.edu/sbol-validator/ . The source code for the validator/converter can be found here: http://github.com/SynBioDex/SBOL-Validator/ .
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http://dx.doi.org/10.1021/acssynbio.6b00277DOI Listing
July 2017

Toward Community Standards and Software for Whole-Cell Modeling.

IEEE Trans Biomed Eng 2016 10 10;63(10):2007-14. Epub 2016 Jun 10.

Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells.

Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language.

Results: Our analysis revealed several challenges to representing WC models using the current standards.

Conclusion: We, therefore, propose several new WC modeling standards, software, and databases.

Significance: We anticipate that these new standards and software will enable more comprehensive models.
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http://dx.doi.org/10.1109/TBME.2016.2560762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451320PMC
October 2016

Sharing Structure and Function in Biological Design with SBOL 2.0.

ACS Synth Biol 2016 06 4;5(6):498-506. Epub 2016 May 4.

Department of Electrical and Computer Engineering, University of Utah , Salt Lake City, Utah 84112, United States.

The Synthetic Biology Open Language (SBOL) is a standard that enables collaborative engineering of biological systems across different institutions and tools. SBOL is developed through careful consideration of recent synthetic biology trends, real use cases, and consensus among leading researchers in the field and members of commercial biotechnology enterprises. We demonstrate and discuss how a set of SBOL-enabled software tools can form an integrated, cross-organizational workflow to recapitulate the design of one of the largest published genetic circuits to date, a 4-input AND sensor. This design encompasses the structural components of the system, such as its DNA, RNA, small molecules, and proteins, as well as the interactions between these components that determine the system's behavior/function. The demonstrated workflow and resulting circuit design illustrate the utility of SBOL 2.0 in automating the exchange of structural and functional specifications for genetic parts, devices, and the biological systems in which they operate.
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http://dx.doi.org/10.1021/acssynbio.5b00215DOI Listing
June 2016

Efficient Analysis of Systems Biology Markup Language Models of Cellular Populations Using Arrays.

ACS Synth Biol 2016 08 8;5(8):835-41. Epub 2016 Mar 8.

Department of Electrical and Computer Engineering, University of Utah , Salt Lake City, Utah 84112, United States.

The Systems Biology Markup Language (SBML) has been widely used for modeling biological systems. Although SBML has been successful in representing a wide variety of biochemical models, the core standard lacks the structure for representing large complex regular systems in a standard way, such as whole-cell and cellular population models. These models require a large number of variables to represent certain aspects of these types of models, such as the chromosome in the whole-cell model and the many identical cell models in a cellular population. While SBML core is not designed to handle these types of models efficiently, the proposed SBML arrays package can represent such regular structures more easily. However, in order to take full advantage of the package, analysis needs to be aware of the arrays structure. When expanding the array constructs within a model, some of the advantages of using arrays are lost. This paper describes a more efficient way to simulate arrayed models. To illustrate the proposed method, this paper uses a population of repressilator and genetic toggle switch circuits as examples. Results show that there are memory benefits using this approach with a modest cost in runtime.
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http://dx.doi.org/10.1021/acssynbio.5b00242DOI Listing
August 2016

A Converter from the Systems Biology Markup Language to the Synthetic Biology Open Language.

ACS Synth Biol 2016 06 12;5(6):479-86. Epub 2016 Jan 12.

Department of Electrical and Computer Engineering, Boston University , Boston, Massachusetts 02215, United States.

Standards are important to synthetic biology because they enable exchange and reproducibility of genetic designs. This paper describes a procedure for converting between two standards: the Systems Biology Markup Language (SBML) and the Synthetic Biology Open Language (SBOL). SBML is a standard for behavioral models of biological systems at the molecular level. SBOL describes structural and basic qualitative behavioral aspects of a biological design. Converting SBML to SBOL enables a consistent connection between behavioral and structural information for a biological design. The conversion process described in this paper leverages Systems Biology Ontology (SBO) annotations to enable inference of a designs qualitative function.
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http://dx.doi.org/10.1021/acssynbio.5b00212DOI Listing
June 2016

SBOL Visual: A Graphical Language for Genetic Designs.

PLoS Biol 2015 Dec 3;13(12):e1002310. Epub 2015 Dec 3.

Bioengineering, University of Washington, Seattle, Washington, United States of America.

Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual.
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http://dx.doi.org/10.1371/journal.pbio.1002310DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4669170PMC
December 2015

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

J Integr Bioinform 2015 Sep 4;12(2):271. Epub 2015 Sep 4.

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.
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http://dx.doi.org/10.2390/biecoll-jib-2015-271DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5457286PMC
September 2015

SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3.

J Integr Bioinform 2015 Sep 4;12(2):268. Epub 2015 Sep 4.

Constructing a model in a hierarchical fashion is a natural approach to managing model complexity, and offers additional opportunities such as the potential to re-use model components. The SBML Level 3 Version 1 Core specification does not directly provide a mechanism for defining hierarchical models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Hierarchical Model Composition package for SBML Level 3 adds the necessary features to SBML to support hierarchical modeling. The package enables a modeler to include submodels within an enclosing SBML model, delete unneeded or redundant elements of that submodel, replace elements of that submodel with element of the containing model, and replace elements of the containing model with elements of the submodel. In addition, the package defines an optional "port" construct, allowing a model to be defined with suggested interfaces between hierarchical components; modelers can chose to use these interfaces, but they are not required to do so and can still interact directly with model elements if they so chose. Finally, the SBML Hierarchical Model Composition package is defined in such a way that a hierarchical model can be "flattened" to an equivalent, non-hierarchical version that uses only plain SBML constructs, thus enabling software tools that do not yet support hierarchy to nevertheless work with SBML hierarchical models.
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http://dx.doi.org/10.2390/biecoll-jib-2015-268DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451323PMC
September 2015

JSBML 1.0: providing a smorgasbord of options to encode systems biology models.

Bioinformatics 2015 Oct 16;31(20):3383-6. Epub 2015 Jun 16.

University of California, San Diego, La Jolla, CA, USA, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.

Unlabelled: JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active open-source project with contributions from a growing, international team of developers who not only maintain compatibility with SBML, but also drive steady improvements to the Java interface and promote ease-of-use with end users.

Availability And Implementation: Source code, binaries and documentation for JSBML can be freely obtained under the terms of the LGPL 2.1 from the website http://sbml.org/Software/JSBML. More information about JSBML can be found in the user guide at http://sbml.org/Software/JSBML/docs/.

Contact: jsbml-development@googlegroups.com or andraeger@eng.ucsd.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595895PMC
October 2015

Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

ACS Synth Biol 2015 Aug 9;4(8):873-9. Epub 2015 Apr 9.

‡Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States.

In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).
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http://dx.doi.org/10.1021/sb5003289DOI Listing
August 2015

Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative.

Front Bioeng Biotechnol 2015 24;3:19. Epub 2015 Feb 24.

Babraham Institute , Cambridge , UK ; European Molecular Biology Laboratory-European Bioinformatics Institute , Cambridge , UK.

The Computational Modeling in Biology Network (COMBINE) is a consortium of groups involved in the development of open community standards and formats used in computational modeling in biology. COMBINE's aim is to act as a coordinator, facilitator, and resource for different standardization efforts whose domains of use cover related areas of the computational biology space. In this perspective article, we summarize COMBINE, its general organization, and the community standards and other efforts involved in it. Our goals are to help guide readers toward standards that may be suitable for their research activities, as well as to direct interested readers to relevant communities where they can best expect to receive assistance in how to develop interoperable computational models.
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http://dx.doi.org/10.3389/fbioe.2015.00019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4338824PMC
March 2015

Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits.

Front Bioeng Biotechnol 2014 28;2:55. Epub 2014 Nov 28.

Department of Electrical and Computer Engineering, The University of Utah , Salt Lake City, UT , USA.

This paper describes a hierarchical stochastic simulation algorithm, which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.
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http://dx.doi.org/10.3389/fbioe.2014.00055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4246920PMC
December 2014

The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology.

Nat Biotechnol 2014 Jun 6;32(6):545-50. Epub 2014 Jun 6.

Biomedical and Health Informatics, University of Washington, Seattle, Washington, USA.

The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-driven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.
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http://dx.doi.org/10.1038/nbt.2891DOI Listing
June 2014