1
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Sents Z, Stoughton TE, Buecherl L, Thomas PJ, Fontanarrosa P, Myers CJ. SynBioSuite: A Tool for Improving the Workflow for Genetic Design and Modeling. ACS Synth Biol 2023; 12:892-897. [PMID: 36888740 DOI: 10.1021/acssynbio.2c00597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Synthetic biology research has led to the development of many software tools for designing, constructing, editing, simulating, and sharing genetic parts and circuits. Among these tools are SBOLCanvas, iBioSim, and SynBioHub, which can be used in conjunction to create a genetic circuit design following the design-build-test-learn process. However, although automation works within these tools, most of these software tools are not integrated, and the process of transferring information between them is a very manual, error-prone process. To address this problem, this work automates some of these processes and presents SynBioSuite, a cloud-based tool that eliminates many of the drawbacks of the current approach by automating the setup and reception of results for simulating a designed genetic circuit via an application programming interface.
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Affiliation(s)
- Zachary Sents
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Thomas E Stoughton
- Department of Computer Science, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Payton J Thomas
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Pedro Fontanarrosa
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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2
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Mısırlı G, Yang B, James K, Wipat A. Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits. ACS Synth Biol 2021; 10:3304-3315. [PMID: 34762797 DOI: 10.1021/acssynbio.1c00157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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.
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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, ST5 5BG, U.K
| | - Bill Yang
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
| | - Katherine James
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, U.K
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
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3
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Bartley BA, Beal J, Karr JR, Strychalski EA. Organizing genome engineering for the gigabase scale. Nat Commun 2020; 11:689. [PMID: 32019919 PMCID: PMC7000699 DOI: 10.1038/s41467-020-14314-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 12/18/2019] [Indexed: 12/11/2022] Open
Abstract
Genome-scale engineering holds great potential to impact science, industry, medicine, and society, and recent improvements in DNA synthesis have enabled the manipulation of megabase genomes. However, coordinating and integrating the workflows and large teams necessary for gigabase genome engineering remains a considerable challenge. We examine this issue and recommend a path forward by: 1) adopting and extending existing representations for designs, assembly plans, samples, data, and workflows; 2) developing new technologies for data curation and quality control; 3) conducting fundamental research on genome-scale modeling and design; and 4) developing new legal and contractual infrastructure to facilitate collaboration.
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Affiliation(s)
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, 02138, USA.
| | - Jonathan R Karr
- Icahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10128, USA
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4
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Misirli G, Nguyen T, McLaughlin JA, Vaidyanathan P, Jones TS, Densmore D, Myers C, Wipat A. A Computational Workflow for the Automated Generation of Models of Genetic Designs. ACS Synth Biol 2019; 8:1548-1559. [PMID: 29782151 DOI: 10.1021/acssynbio.7b00459] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.
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Affiliation(s)
- Göksel Misirli
- School of Computing and Mathematics, Keele University, Staffordshire, U.K
| | - Tramy Nguyen
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | | | - Prashant Vaidyanathan
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Timothy S. Jones
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Douglas Densmore
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Anil Wipat
- ICOS, School of Computing, Newcastle University, Newcastle upon Tyne, U.K
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5
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Watanabe L, Nguyen T, Zhang M, Zundel Z, Zhang Z, Madsen C, Roehner N, Myers C. iBioSim 3: A Tool for Model-Based Genetic Circuit Design. ACS Synth Biol 2019; 8:1560-1563. [PMID: 29944839 DOI: 10.1021/acssynbio.8b00078] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The iBioSim tool has been developed to facilitate the design of genetic circuits via a model-based design strategy. This paper illustrates the new features incorporated into the tool for DNA circuit design, design analysis, and design synthesis, all of which can be used in a workflow for the systematic construction of new genetic circuits.
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Affiliation(s)
- Leandro Watanabe
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Tramy Nguyen
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Michael Zhang
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zach Zundel
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, United States
| | - Curtis Madsen
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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6
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Neal ML, König M, Nickerson D, Mısırlı G, Kalbasi R, Dräger A, Atalag K, Chelliah V, Cooling MT, Cook DL, Crook S, de Alba M, Friedman SH, Garny A, Gennari JH, Gleeson P, Golebiewski M, Hucka M, Juty N, Myers C, Olivier BG, Sauro HM, Scharm M, Snoep JL, Touré V, Wipat A, Wolkenhauer O, Waltemath D. Harmonizing semantic annotations for computational models in biology. Brief Bioinform 2019; 20:540-550. [PMID: 30462164 PMCID: PMC6433895 DOI: 10.1093/bib/bby087] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/08/2018] [Accepted: 08/17/2018] [Indexed: 02/06/2023] Open
Abstract
Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.
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Affiliation(s)
- Maxwell Lewis Neal
- Seattle Children’s Research Institute, Center for Global Infectious Disease Research, Seattle, USA
| | - Matthias König
- Department of Biology, Humboldt-University Berlin, Institute for Theoretical Biology, Berlin, Germany
| | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, UK
| | - Reza Kalbasi
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Andreas Dräger
- Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Koray Atalag
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Vijayalakshmi Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael T Cooling
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - Daniel L Cook
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA
| | - Miguel de Alba
- German Federal Institute for Risk Assessment, Berlin, Germany
| | | | - Alan Garny
- Auckland Bioengineering Institute, University of Auckland, Auckland, NZ
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Nick Juty
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
| | - Brett G Olivier
- Systems Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Modelling of Biological Processes, BioQUANT/COS, Heidelberg University, Germany
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Jacky L Snoep
- Department of Biochemistry, Stellenbosch University, Matieland, South Africa
- Department of Molecular Cell Physiology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Manchester Institute for Biotechnology, University of Manchester, Manchester, UK
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa
| | - Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
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7
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Waites W, Mısırlı G, Cavaliere M, Danos V, Wipat A. A Genetic Circuit Compiler: Generating Combinatorial Genetic Circuits with Web Semantics and Inference. ACS Synth Biol 2018; 7:2812-2823. [PMID: 30408409 PMCID: PMC6305556 DOI: 10.1021/acssynbio.8b00201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice, and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating κ-language simulations from semantic descriptions of genetic circuits.
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Affiliation(s)
- William Waites
- School
of Informatics, University of Edinburgh, Edinburgh EH8 9YL, U.K.,E-mail:
| | - Göksel Mısırlı
- School
of Computing and Mathematics, Keele University, Newcastle ST5 5BG, U.K.
| | - Matteo Cavaliere
- School
of Computing & Mathematics, Manchester
Metropolitan University, Manchester M15 6BH, U.K.
| | - Vincent Danos
- School
of Informatics, University of Edinburgh, Edinburgh EH8 9YL, U.K.,École
Normale Supérieure, Paris, CNRS, 75005 Paris, France
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K.
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8
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Szymanski EA. Who are the users of synthetic DNA? Using metaphors to activate microorganisms at the center of synthetic biology. LIFE SCIENCES, SOCIETY AND POLICY 2018; 14:15. [PMID: 30006902 PMCID: PMC6045561 DOI: 10.1186/s40504-018-0080-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 05/31/2018] [Indexed: 06/08/2023]
Abstract
Synthetic biology, a multidisciplinary field involving designing and building with DNA, often designs and builds in microorganisms. The role of these microorganisms tends to be understood through metaphors making the microbial cell like a machine and emphasizing its passivity: cells are described as platforms, chassis, and computers. Here, I point to the efficacy of such metaphors in enacting the microorganism as a particular kind of (non-)participant in the research process, and I suggest the utility of employing metaphors that make microorganisms a different kind of thing-active participants, contributors, and even collaborators in scientific research. This suggestion is worth making, I argue, because enabling the activity of the microorganism generates opportunities for learning from microorganisms in ways that may help explain currently unexplained phenomena in synthetic biology and suggest new experimental directions. Moreover, "activating the microorganism" reorients relationships between human scientists and nonhuman experimental participants away from control over nonhuman creatures and toward respect for and listening to them, generating conditions of possibility for exploring what responsible research means when humans try to be responsible toward and even with creatures across species boundaries.
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Affiliation(s)
- Erika Amethyst Szymanski
- Science, Technology, and Innovation Studies, University of Edinburgh, Edinburgh, UK.
- Chisholm House, High School Yards, Edinburgh, EH1 1LZ, UK.
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9
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de Lorenzo V, Schmidt M. Biological standards for the Knowledge-Based BioEconomy: What is at stake. N Biotechnol 2018; 40:170-180. [DOI: 10.1016/j.nbt.2017.05.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Accepted: 05/03/2017] [Indexed: 02/07/2023]
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10
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A standard-enabled workflow for synthetic biology. Biochem Soc Trans 2017; 45:793-803. [PMID: 28620041 DOI: 10.1042/bst20160347] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 03/29/2017] [Accepted: 03/31/2017] [Indexed: 11/17/2022]
Abstract
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 ACS Synthetic Biology journal has recommended the use of SBOL in their publications.
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11
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Zundel Z, Samineni M, Zhang Z, Myers CJ. A Validator and Converter for the Synthetic Biology Open Language. ACS Synth Biol 2017; 6:1161-1168. [PMID: 28033703 DOI: 10.1021/acssynbio.6b00277] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
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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Affiliation(s)
- Zach Zundel
- Department of Bioengineering, University of Utah, 36 S. Wasatch Drive, SMBB Room 3100, Salt
Lake City, Utah 84112, United States
| | - Meher Samineni
- Department of Electrical and Computer Engineering, University of Utah, 50 S. Central Campus Drive, MEB Room 2110, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department of Computer Science and Engineering, University of South Florida, 4202 E. Fowler Avenue, ENB 118, Tampa, Florida 33620, United States
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, 50 S. Central Campus Drive, MEB Room 2110, Salt Lake City, Utah 84112, United States
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12
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Zhang M, McLaughlin JA, Wipat A, Myers CJ. SBOLDesigner 2: An Intuitive Tool for Structural Genetic Design. ACS Synth Biol 2017; 6:1150-1160. [PMID: 28441476 DOI: 10.1021/acssynbio.6b00275] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
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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Affiliation(s)
- Michael Zhang
- Department of Electrical
and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | | | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Chris J. Myers
- Department of Electrical
and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
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13
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Madsen C, McLaughlin JA, Mısırlı G, Pocock M, Flanagan K, Hallinan J, Wipat A. The SBOL Stack: A Platform for Storing, Publishing, and Sharing Synthetic Biology Designs. ACS Synth Biol 2016; 5:487-97. [PMID: 27268205 DOI: 10.1021/acssynbio.5b00210] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Recently, synthetic biologists have developed the Synthetic Biology Open Language (SBOL), a data exchange standard for descriptions of genetic parts, devices, modules, and systems. The goals of this standard are to allow scientists to exchange designs of biological parts and systems, to facilitate the storage of genetic designs in repositories, and to facilitate the description of genetic designs in publications. In order to achieve these goals, the development of an infrastructure to store, retrieve, and exchange SBOL data is necessary. To address this problem, we have developed the SBOL Stack, a Resource Description Framework (RDF) database specifically designed for the storage, integration, and publication of SBOL data. This database allows users to define a library of synthetic parts and designs as a service, to share SBOL data with collaborators, and to store designs of biological systems locally. The database also allows external data sources to be integrated by mapping them to the SBOL data model. The SBOL Stack includes two Web interfaces: the SBOL Stack API and SynBioHub. While the former is designed for developers, the latter allows users to upload new SBOL biological designs, download SBOL documents, search by keyword, and visualize SBOL data. Since the SBOL Stack is based on semantic Web technology, the inherent distributed querying functionality of RDF databases can be used to allow different SBOL stack databases to be queried simultaneously, and therefore, data can be shared between different institutes, centers, or other users.
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Affiliation(s)
- Curtis Madsen
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
- Department of Electrical & Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | | | - Göksel Mısırlı
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Matthew Pocock
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
- Turing Ate My Hamster, LTD, Newcastle
upon Tyne NE27 0RT, U.K
| | - Keith Flanagan
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Jennifer Hallinan
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
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14
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Roehner N, Beal J, Clancy K, Bartley B, Misirli G, Grünberg R, Oberortner E, Pocock M, Bissell M, Madsen C, Nguyen T, Zhang M, Zhang Z, Zundel Z, Densmore D, Gennari JH, Wipat A, Sauro HM, Myers CJ. Sharing Structure and Function in Biological Design with SBOL 2.0. ACS Synth Biol 2016; 5:498-506. [PMID: 27111421 DOI: 10.1021/acssynbio.5b00215] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
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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Affiliation(s)
- Nicholas Roehner
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Kevin Clancy
- Thermo Fisher Scientific, Carlsbad, California 92008, United States
| | - Bryan Bartley
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Goksel Misirli
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Raik Grünberg
- Institute
for Research in Immunology and Cancer, University of Montreal, Montreal, Quebec H3T 1J4, Canada
| | - Ernst Oberortner
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, California 94598, United States
| | - Matthew Pocock
- Turing Ate My Hamster, Ltd., Newcastle
upon Tyne NE27 0RT, U.K
| | | | - Curtis Madsen
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Tramy Nguyen
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Michael Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Zhen Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Zach Zundel
- Department
of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Douglas Densmore
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - John H. Gennari
- Department
of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington 98195, United States
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Herbert M. Sauro
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| |
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