1
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Mante J, Sents Z, Britt D, Mo W, Liao C, Greer R, Myers CJ. SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information. ACS Synth Biol 2024; 13:3051-3055. [PMID: 39230953 DOI: 10.1021/acssynbio.4c00392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encourage author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, called entity normalization, and sequence matching, SeqImprove creates machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file. SeqImprove makes it easier for authors to submit sequence data that is FAIR (findable, accessible, interoperable, and reusable).
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Zach Sents
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Duncan Britt
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - William Mo
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Chunxiao Liao
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Ryan Greer
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Chris J Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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2
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Roehner N, Roberts J, Lapets A, Gould D, Akavoor V, Qin L, Gordon DB, Voigt C, Densmore D. GOLDBAR: A Framework for Combinatorial Biological Design. ACS Synth Biol 2024; 13:2899-2911. [PMID: 39162314 DOI: 10.1021/acssynbio.4c00296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
With the rise of new DNA part libraries and technologies for assembling DNA, synthetic biologists are increasingly constructing and screening combinatorial libraries to optimize their biological designs. As combinatorial libraries are used to generate data on design performance, new rules for composing biological designs will emerge. Most formal frameworks for combinatorial design, however, do not yet support formal comparison of design composition, which is needed to facilitate automated analysis and machine learning in massive biological design spaces. To address this need, we introduce a combinatorial design framework called GOLDBAR. Compared with existing frameworks, GOLDBAR enables synthetic biologists to intersect and merge the rules for entire classes of biological designs to extract common design motifs and infer new ones. Here, we demonstrate the application of GOLDBAR to refine/validate design spaces for TetR-homologue transcriptional logic circuits, verify the assembly of a partial nif gene cluster, and infer novel gene clusters for the biosynthesis of rebeccamycin. We also discuss how GOLDBAR could be used to facilitate grammar-based machine learning in synthetic biology.
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Affiliation(s)
- Nicholas Roehner
- RTX BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - James Roberts
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
| | | | - Dany Gould
- Hariri Institute for Computing, Boston University, Boston, Massachusetts 02215, United States
| | - Vidya Akavoor
- Hariri Institute for Computing, Boston University, Boston, Massachusetts 02215, United States
| | - Lucy Qin
- Hariri Institute for Computing, Boston University, Boston, Massachusetts 02215, United States
| | - D Benjamin Gordon
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Christopher Voigt
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, Massachusetts 02215, United States
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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3
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Nava A, Fear AL, Lee N, Mellinger P, Lan G, McCauley J, Tan S, Kaplan N, Goyal G, Coates RC, Roberts J, Johnson Z, Hu R, Wu B, Ahn J, Kim WE, Wan Y, Yin K, Hillson N, Haushalter RW, Keasling JD. Automated Platform for the Plasmid Construction Process. ACS Synth Biol 2023; 12:3506-3513. [PMID: 37948662 PMCID: PMC10729297 DOI: 10.1021/acssynbio.3c00292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Indexed: 11/12/2023]
Abstract
There is a growing need for applications capable of handling large synthesis biology experiments. At the core of synthetic biology is the process of cloning and manipulating DNA as plasmids. Here, we report the development of an application named DNAda capable of writing automation instructions for any given DNA construct design generated by the J5 DNA assembly program. We also describe the automation pipeline and several useful features. The pipeline is particularly useful for the construction of combinatorial DNA assemblies. Furthermore, we demonstrate the platform by constructing a library of polyketide synthase parts, which includes 120 plasmids ranging in size from 7 to 14 kb from 4 to 7 DNA fragments.
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Affiliation(s)
- Alberto
A. Nava
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Anna Lisa Fear
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Namil Lee
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Peter Mellinger
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Guangxu Lan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Joshua McCauley
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Stephen Tan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Nurgul Kaplan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Garima Goyal
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - R. Cameron Coates
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Jacob Roberts
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Zahmiria Johnson
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Romina Hu
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Bryan Wu
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Jared Ahn
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Woojoo E. Kim
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Yao Wan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Kevin Yin
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Plant and Microbial Biology, University
of California, Berkeley, Berkeley, California 94720, United States
| | - Nathan Hillson
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Robert W. Haushalter
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Jay D. Keasling
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
- Center
for Synthetic Biochemistry, Shenzhen Institutes
for Advanced Technologies, Shenzhen 518055, P.R. China
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, Kemitorvet, Building 220, Kongens Lyngby 2800, Denmark
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4
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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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5
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Mante J, Abam J, Samineni SP, Pötzsch IM, Beal J, Myers CJ. Excel-SBOL Converter: Creating SBOL from Excel Templates and Vice Versa. ACS Synth Biol 2023; 12:340-346. [PMID: 36595709 DOI: 10.1021/acssynbio.2c00521] [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: 01/05/2023]
Abstract
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.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Julian Abam
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Sai P Samineni
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Chris J Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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6
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Aldulijan I, Beal J, Billerbeck S, Bouffard J, Chambonnier G, Ntelkis N, Guerreiro I, Holub M, Ross P, Selvarajah V, Sprent N, Vidal G, Vignoni A. Functional Synthetic Biology. Synth Biol (Oxf) 2023; 8:ysad006. [PMID: 37073284 PMCID: PMC10105873 DOI: 10.1093/synbio/ysad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 02/17/2023] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
Synthetic biologists have made great progress over the past decade in developing methods for modular assembly of genetic sequences and in engineering biological systems with a wide variety of functions in various contexts and organisms. However, current paradigms in the field entangle sequence and functionality in a manner that makes abstraction difficult, reduces engineering flexibility and impairs predictability and design reuse. Functional Synthetic Biology aims to overcome these impediments by focusing the design of biological systems on function, rather than on sequence. This reorientation will decouple the engineering of biological devices from the specifics of how those devices are put to use, requiring both conceptual and organizational change, as well as supporting software tooling. Realizing this vision of Functional Synthetic Biology will allow more flexibility in how devices are used, more opportunity for reuse of devices and data, improvements in predictability and reductions in technical risk and cost.
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Affiliation(s)
- Ibrahim Aldulijan
- Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, 07030, NJ, USA
| | | | - Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AG, Groningen, The Netherlands
| | - Jeff Bouffard
- Centre for Applied Synthetic Biology, and Department of Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, H4B 1R6, Québec, Canada
| | - Gaël Chambonnier
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA
| | - Nikolaos Ntelkis
- Specialized Metabolism research group, Center for Plant Systems Biology, VIB-Ghent University, Technologiepark 71, Zwijnaarde, 9052, Belgium
| | - Isaac Guerreiro
- iGEM Foundation, 45 Prospect Street, Cambridge, 02139, MA, USA
| | - Martin Holub
- Delft University of Technology, Van der Maasweg 9, 2629 HZ, The Netherlands
| | - Paul Ross
- BioStrat Marketing, 9965 Harbour Lake Circle, Boynton Beach, FL, 33437, USA
| | | | - Noah Sprent
- Department of Chemical Engineering, Imperial College London, South Kensington Campus, Exhibition Road, SW7 2AZ, UK
| | - Gonzalo Vidal
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Devonshire Building, Devonshire Terrace, NE1 7RU, Newcastle Upon Tyne, UK
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, Instituto de Automatica e Informatica Industrial, Universitat Politecnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain
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7
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Mitchell T, Beal J, Bartley B. pySBOL3: SBOL3 for Python Programmers. ACS Synth Biol 2022; 11:2523-2526. [PMID: 35767721 DOI: 10.1021/acssynbio.2c00249] [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: 11/28/2022]
Abstract
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.
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Affiliation(s)
- Tom Mitchell
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
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8
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Jones TS, Oliveira SMD, Myers CJ, Voigt CA, Densmore D. Genetic circuit design automation with Cello 2.0. Nat Protoc 2022; 17:1097-1113. [PMID: 35197606 DOI: 10.1038/s41596-021-00675-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 11/29/2021] [Indexed: 11/09/2022]
Abstract
Cells interact with their environment, communicate among themselves, track time and make decisions through functions controlled by natural regulatory genetic circuits consisting of interacting biological components. Synthetic programmable circuits used in therapeutics and other applications can be automatically designed by computer-aided tools. The Cello software designs the DNA sequences for programmable circuits based on a high-level software description and a library of characterized DNA parts representing Boolean logic gates. This process allows for design specification reuse, modular DNA part library curation and formalized circuit transformations based on experimental data. This protocol describes Cello 2.0, a freely available cross-platform software written in Java. Cello 2.0 enables flexible descriptions of the logic gates' structure and their mathematical models representing dynamic behavior, new formal rules for describing the placement of gates in a genome, a new graphical user interface, support for Verilog 2005 syntax and a connection to the SynBioHub parts repository software environment. Collectively, these features expand Cello's capabilities beyond Escherichia coli plasmids to new organisms and broader genetic contexts, including the genome. Designing circuits with Cello 2.0 produces an abstract Boolean network from a Verilog file, assigns biological parts to each node in the Boolean network, constructs a DNA sequence and generates highly structured and annotated sequence representations suitable for downstream processing and fabrication, respectively. The result is a sequence implementing the specified Boolean function in the organism and predictions of circuit performance. Depending on the size of the design space and users' expertise, jobs may take minutes or hours to complete.
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Affiliation(s)
- Timothy S Jones
- Biological Design Center, Boston University, Boston, MA, USA.,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Samuel M D Oliveira
- Biological Design Center, Boston University, Boston, MA, USA.,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Chris J Myers
- Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA. .,Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
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9
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Bryce D, Goldman RP, DeHaven M, Beal J, Bartley B, Nguyen TT, Walczak N, Weston M, Zheng G, Nowak J, Lee P, Stubbs J, Gaffney N, Vaughn MW, Myers CJ, Moseley RC, Haase S, Deckard A, Cummins B, Leiby N. Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis. ACS Synth Biol 2022; 11:608-622. [PMID: 35099189 DOI: 10.1021/acssynbio.1c00305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.
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Affiliation(s)
- Daniel Bryce
- SIFT, LLC., Minneapolis, Minnesota 55401, United States
| | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Tramy T. Nguyen
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Nicholas Walczak
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Mark Weston
- Netrias, Inc., Annapolis, Maryland 21409, United States
| | - George Zheng
- Netrias, Inc., Annapolis, Maryland 21409, United States
| | - Josh Nowak
- Strateos, Inc., Menlo Park, California 94025, United States
| | - Peter Lee
- Ginkgo Bioworks, Inc., Boston, Massachusetts 02210, United States
| | - Joe Stubbs
- Texas Advanced Computing Center, Austin, Texas 78758, United States
| | - Niall Gaffney
- Texas Advanced Computing Center, Austin, Texas 78758, United States
| | | | | | | | - Steven Haase
- Duke University, Durham, North Carolina 27708, United States
| | - Anastasia Deckard
- Geometric Data Analytics, Inc., Durham, North Carolina 27701, United States
| | - Bree Cummins
- Montana State University, Bozeman, Montana 59717, United States
| | - Nick Leiby
- Two Six Technologies, Inc., Arlington, Virginia 22203, United States
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10
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Nguyen T, Walczak N, Sumorok D, Weston M, Beal J. Intent Parser: A Tool for Codification and Sharing of Experimental Design. ACS Synth Biol 2022; 11:502-507. [PMID: 34882380 DOI: 10.1021/acssynbio.1c00285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Communicating information about experimental design among a team of collaborators is challenging because different people tend to describe experiments in different ways and with different levels of detail. Sometimes, humans can interpret missing information by making assumptions and drawing inferences from information already provided. Doing so, however, is error-prone and typically requires a high level of interpersonal communication. In this paper, we present a tool that addresses this challenge by providing a simple interface for incremental formal codification of experiment designs. Users interact with a Google Docs word-processing interface with structured tables, backed by assisted linking to machine-readable definitions in a data repository (SynBioHub) and specification of available protocols and requests for execution in the Open Protocol Interface Language (OPIL). The result is an easy-to-use tool for generating machine-readable descriptions of experiment designs with which users in the DARPA SD2 program have collected data from 80 208 samples using a variety of protocols and instruments over the course of 181 experiment runs.
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Affiliation(s)
- Tramy Nguyen
- Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
| | - Nicholas Walczak
- Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
| | - Daniel Sumorok
- Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
| | - Mark Weston
- Netrias, 3100 Clarendon Blvd, Ste 200, Arlington, Virginia 22201, United States
| | - Jacob Beal
- Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
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11
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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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12
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Abstract
Much progress has been made in developing tools to generate component-based design representations of biological systems from standard libraries of parts. Most biological designs, however, are still specified at the sequence level. Consequently, there exists a need for a tool that can be used to automatically infer component-based design representations from sequences, particularly in cases when those sequences have minimal levels of annotation. Such a tool would assist computational synthetic biologists in bridging the gap between the outputs of sequence editors and the inputs to more sophisticated design tools, and it would facilitate their development of automated workflows for design curation and quality control. Accordingly, we introduce Synthetic Biology Curation Tools (SYNBICT), a Python tool suite for automation-assisted annotation, curation, and functional inference for genetic designs. We have validated SYNBICT by applying it to genetic designs in the DARPA Synergistic Discovery & Design (SD2) program and the International Genetically Engineered Machines (iGEM) 2018 distribution. Most notably, SYNBICT is more automated and parallelizable than manual design editors, and it can be applied to interpret existing designs instead of only generating new ones.
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Affiliation(s)
- Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jeanet Mante
- Department of Biomedical 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
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
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13
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Buecherl L, Roberts R, Fontanarrosa P, Thomas PJ, Mante J, Zhang Z, Myers CJ. Stochastic Hazard Analysis of Genetic Circuits in iBioSim and STAMINA. ACS Synth Biol 2021; 10:2532-2540. [PMID: 34606710 DOI: 10.1021/acssynbio.1c00159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In synthetic biology, combinational circuits are used to program cells for various new applications like biosensors, drug delivery systems, and biofuels. Similar to asynchronous electronic circuits, some combinational genetic circuits may show unwanted switching variations (glitches) caused by multiple input changes. Depending on the biological circuit, glitches can cause irreversible effects and jeopardize the circuit's functionality. This paper presents a stochastic analysis to predict glitch propensities for three implementations of a genetic circuit with known glitching behavior. The analysis uses STochastic Approximate Model-checker for INfinite-state Analysis (STAMINA), a tool for stochastic verification. The STAMINA results were validated by comparison to stochastic simulation in iBioSim resulting in further improvements of STAMINA. This paper demonstrates that stochastic verification can be utilized by genetic designers to evaluate design choices and input restrictions to achieve a desired reliability of operation.
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Affiliation(s)
- Lukas Buecherl
- Department of Biomedical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Riley Roberts
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, United States
| | - Pedro Fontanarrosa
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Payton J. Thomas
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Jeanet Mante
- Department of Biomedical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Zhen Zhang
- Department of Electrical and Computer Engineering, Utah State University, Logan, Utah 84322, 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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14
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Hatch B, Meng L, Mante J, McLaughlin JA, Scott-Brown J, Myers CJ. VisBOL2-Improving Web-Based Visualization for Synthetic Biology Designs. ACS Synth Biol 2021; 10:2111-2115. [PMID: 34324811 DOI: 10.1021/acssynbio.1c00147] [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: 02/06/2023]
Abstract
VisBOL is a web-based visualization tool used to depict genetic circuit designs. This tool depicts simple DNA circuits adequately, but it has become increasingly outdated as new versions of SBOL Visual were released. This paper introduces VisBOL2, a heavily redesigned version of VisBOL that makes a number of improvements to the original VisBOL, including proper functional interaction rendering, dynamic viewing, a more maintainable code base, and modularity that facilitates compatibility with other software tools. This modularity is demonstrated by incorporating VisBOL2 into a sequence visualization plugin for SynBioHub.
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Affiliation(s)
- Benjamin Hatch
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Linhao Meng
- Eindhoven University of Technology, Eindhoven, 5612 AZ, Netherlands
| | - Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | | | | | - Chris J. Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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15
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McCarthy J. Engineering and standardization of posttranscriptional biocircuitry in Saccharomyces cerevisiae. Integr Biol (Camb) 2021; 13:210-220. [PMID: 34270725 DOI: 10.1093/intbio/zyab013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/24/2021] [Accepted: 06/25/2021] [Indexed: 11/14/2022]
Abstract
This short review considers to what extent posttranscriptional steps of gene expression can provide the basis for novel control mechanisms and procedures in synthetic biology and biotechnology. The term biocircuitry is used here to refer to functionally connected components comprising DNA, RNA or proteins. The review begins with an overview of the diversity of devices being developed and then considers the challenges presented by trying to engineer more scaled-up systems. While the engineering of RNA-based and protein-based circuitry poses new challenges, the resulting 'toolsets' of components and novel mechanisms of operation will open up multiple new opportunities for synthetic biology. However, agreed procedures for standardization will need to be placed at the heart of this expanding field if the full potential benefits are to be realized.
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Affiliation(s)
- John McCarthy
- Warwick Integrative Synthetic Biology Centre (WISB) and School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
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16
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Terry L, Earl J, Thayer S, Bridge S, Myers CJ. SBOLCanvas: A Visual Editor for Genetic Designs. ACS Synth Biol 2021; 10:1792-1796. [PMID: 34152132 DOI: 10.1021/acssynbio.1c00096] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
SBOLCanvas is a web-based application that can create and edit genetic constructs using the SBOL data and visual standards. SBOLCanvas allows a user to create a genetic design visually and structurally from start to finish. It also allows users to incorporate existing SBOL data from a SynBioHub repository. By the nature of being a web-based application, SBOLCanvas is readily accessible and easy to use. A live version of the latest release can be found at https://sbolcanvas.org.
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Affiliation(s)
- Logan Terry
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Jared Earl
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Sam Thayer
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Samuel Bridge
- University of Utah, Salt Lake City, 84132 Utah, United States
| | - Chris J. Myers
- University of Colorado Boulder, Boulder, 80309 Colorado, United States
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17
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Comparative analysis of three studies measuring fluorescence from engineered bacterial genetic constructs. PLoS One 2021; 16:e0252263. [PMID: 34097703 PMCID: PMC8183995 DOI: 10.1371/journal.pone.0252263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/11/2021] [Indexed: 11/19/2022] Open
Abstract
Reproducibility is a key challenge of synthetic biology, but the foundation of reproducibility is only as solid as the reference materials it is built upon. Here we focus on the reproducibility of fluorescence measurements from bacteria transformed with engineered genetic constructs. This comparative analysis comprises three large interlaboratory studies using flow cytometry and plate readers, identical genetic constructs, and compatible unit calibration protocols. Across all three studies, we find similarly high precision in the calibrants used for plate readers. We also find that fluorescence measurements agree closely across the flow cytometry results and two years of plate reader results, with an average standard deviation of 1.52-fold, while the third year of plate reader results are consistently shifted by more than an order of magnitude, with an average shift of 28.9-fold. Analyzing possible sources of error indicates this shift is due to incorrect preparation of the fluorescein calibrant. These findings suggest that measuring fluorescence from engineered constructs is highly reproducible, but also that there is a critical need for access to quality controlled fluorescent calibrants for plate readers.
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18
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Wang B, Yang H, Sun J, Dou C, Huang J, Guo FB. BioMaster: An Integrated Database and Analytic Platform to Provide Comprehensive Information About BioBrick Parts. Front Microbiol 2021; 12:593979. [PMID: 33552037 PMCID: PMC7858672 DOI: 10.3389/fmicb.2021.593979] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/04/2021] [Indexed: 01/25/2023] Open
Abstract
Synthetic biology seeks to create new biological parts, devices, and systems, and to reconfigure existing natural biological systems for custom-designed purposes. The standardized BioBrick parts are the foundation of synthetic biology. The incomplete and flawed metadata of BioBrick parts, however, are a major obstacle for designing genetic circuit easily, quickly, and accurately. Here, a database termed BioMaster http://www.biomaster-uestc.cn was developed to extensively complement information about BioBrick parts, which includes 47,934 items of BioBrick parts from the international Genetically Engineered Machine (iGEM) Registry with more comprehensive information integrated from 10 databases, providing corresponding information about functions, activities, interactions, and related literature. Moreover, BioMaster is also a user-friendly platform for retrieval and analyses of relevant information on BioBrick parts.
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Affiliation(s)
- Beibei Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huayi Yang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jianan Sun
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuhao Dou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng-Biao Guo
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Centre for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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19
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20
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Fontanarrosa P, Doosthosseini H, Borujeni AE, Dorfan Y, Voigt CA, Myers C. Genetic Circuit Dynamics: Hazard and Glitch Analysis. ACS Synth Biol 2020; 9:2324-2338. [PMID: 32786351 DOI: 10.1021/acssynbio.0c00055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Multiple input changes can cause unwanted switching variations, or glitches, in the output of genetic combinational circuits. These glitches can have drastic effects if the output of the circuit causes irreversible changes within or with other cells such as a cascade of responses, apoptosis, or the release of a pharmaceutical in an off-target tissue. Therefore, avoiding unwanted variation of a circuit's output can be crucial for the safe operation of a genetic circuit. This paper investigates what causes unwanted switching variations in combinational genetic circuits using hazard analysis and a new dynamic model generator. The analysis is done in previously built and modeled genetic circuits with known glitching behavior. The dynamic models generated not only predict the same steady states as previous models but can also predict the unwanted switching variations that have been observed experimentally. Multiple input changes may cause glitches due to propagation delays within the circuit. Modifying the circuit's layout to alter these delays may change the likelihood of certain glitches, but it cannot eliminate the possibility that the glitch may occur. In other words, function hazards cannot be eliminated. Instead, they must be avoided by restricting the allowed input changes to the system. Logic hazards, on the other hand, can be avoided using hazard-free logic synthesis. This paper demonstrates this by showing how a circuit designed using a popular genetic design automation tool can be redesigned to eliminate logic hazards.
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Affiliation(s)
- Pedro Fontanarrosa
- Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Hamid Doosthosseini
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amin Espah Borujeni
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Yuval Dorfan
- Synthetic Biology Center and Department of Biological Engineering , Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Christopher A. Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02478, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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Brown B, Bartley B, Beal J, Bird JE, Goñi-Moreno Á, McLaughlin JA, Mısırlı G, Roehner N, Skelton DJ, Poh CL, Ofiteru ID, James K, Wipat A. Capturing Multicellular System Designs Using Synthetic Biology Open Language (SBOL). ACS Synth Biol 2020; 9:2410-2417. [PMID: 32786354 DOI: 10.1021/acssynbio.0c00176] [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: 11/28/2022]
Abstract
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.
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Affiliation(s)
- Bradley Brown
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jasmine E. Bird
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Ángel Goñi-Moreno
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politénica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcon, Madrid, Spain
| | | | - Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Newcastle ST5 5BG, United Kingdom
| | - Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - David James Skelton
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
| | - Chueh Loo Poh
- Department of Biomedical Engineering and NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore
| | - Irina Dana Ofiteru
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| | - Katherine James
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, United Kingdom
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22
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McLaughlin JA, Beal J, Mısırlı G, Grünberg R, Bartley BA, Scott-Brown J, Vaidyanathan P, Fontanarrosa P, Oberortner E, Wipat A, Gorochowski TE, Myers CJ. The Synthetic Biology Open Language (SBOL) Version 3: Simplified Data Exchange for Bioengineering. Front Bioeng Biotechnol 2020; 8:1009. [PMID: 33015004 PMCID: PMC7516281 DOI: 10.3389/fbioe.2020.01009] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/31/2020] [Indexed: 12/17/2022] Open
Abstract
The Synthetic Biology Open Language (SBOL) is a community-developed data standard that allows knowledge about biological designs to be captured using a machine-tractable, ontology-backed representation that is built using Semantic Web technologies. While early versions of SBOL focused only on the description of DNA-based components and their sub-components, SBOL can now be used to represent knowledge across multiple scales and throughout the entire synthetic biology workflow, from the specification of a single molecule or DNA fragment through to multicellular systems containing multiple interacting genetic circuits. The third major iteration of the SBOL standard, SBOL3, is an effort to streamline and simplify the underlying data model with a focus on real-world applications, based on experience from the deployment of SBOL in a variety of scientific and industrial settings. Here, we introduce the SBOL3 specification both in comparison to previous versions of SBOL and through practical examples of its use.
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Affiliation(s)
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, United States
| | - Göksel Mısırlı
- School of Mathematics and Computing, Keele University, Keele, United Kingdom
| | - Raik Grünberg
- Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | | | - James Scott-Brown
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | | | - Pedro Fontanarrosa
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Ernst Oberortner
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle-upon-Tyne, United Kingdom
| | | | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO, United States
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Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik‐Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier‐Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol 2020; 16:e9110. [PMID: 32845085 PMCID: PMC8411907 DOI: 10.15252/msb.20199110] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 06/24/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022] Open
Abstract
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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24
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Lakin MR, Phillips A. Domain-Specific Programming Languages for Computational Nucleic Acid Systems. ACS Synth Biol 2020; 9:1499-1513. [PMID: 32589838 DOI: 10.1021/acssynbio.0c00050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The construction of models of system behavior is of great importance throughout science and engineering. In bioengineering and bionanotechnology, these often take the form of dynamic models that specify the evolution of different species over time. To ensure that scientific observations and conclusions are consistent and that systems can be reliably engineered on the basis of model predictions, it is important that models of biomolecular systems can be constructed in a reliable, principled, and efficient manner. This review focuses on efforts to address this need by using domain-specific programming languages as the basis for custom design tools for researchers working on computational nucleic acid devices, where a domain-specific language is simply a programming language tailored to a particular application domain. The underlying thesis of our review is that there is a continuum of practical implementation strategies for computational nucleic acid systems, which can all benefit from appropriate domain-specific languages and software design tools. We emphasize the need for specialized yet flexible tools that can be realized using domain-specific languages that compile to more general-purpose representations.
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Affiliation(s)
- Matthew R. Lakin
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico 87131, United States
- Department of Chemical & Biological Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
- Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
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25
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Abstract
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.
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Affiliation(s)
- Jeanet Mante
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zach Zundel
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
- School of Computing, University of Utah, Salt Lake City, Utah 84112, United States
| | - Chris Myers
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah 84112, United States
- School of Computing, University of Utah, Salt Lake City, Utah 84112, United States
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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26
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Abstract
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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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, Staffordshire ST5 5BG, U.K
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | | | | | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, 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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27
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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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de Carpentier F, Le Peillet J, Boisset ND, Crozet P, Lemaire SD, Danon A. Blasticidin S Deaminase: A New Efficient Selectable Marker for Chlamydomonas reinhardtii. FRONTIERS IN PLANT SCIENCE 2020; 11:242. [PMID: 32211000 PMCID: PMC7066984 DOI: 10.3389/fpls.2020.00242] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/17/2020] [Indexed: 05/21/2023]
Abstract
Chlamydomonas reinhardtii is a model unicellular organism for basic or biotechnological research, such as the production of high-value molecules or biofuels thanks to its photosynthetic ability. To enable rapid construction and optimization of multiple designs and strains, our team and collaborators have developed a versatile Chlamydomonas Modular Cloning toolkit comprising 119 biobricks. Having the ability to use a wide range of selectable markers is an important benefit for forward and reverse genetics in Chlamydomonas. We report here the development of a new selectable marker based on the resistance to the antibiotic blasticidin S, using the Bacillus cereus blasticidin S deaminase (BSR) gene. The optimal concentration of blasticidin S for effective selection was determined in both liquid and solid media and tested for multiple laboratory strains. In addition, we have shown that our new selectable marker does not interfere with other common antibiotic resistances: zeocin, hygromycin, kanamycin, paromomycin, and spectinomycin. The blasticidin resistance biobrick has been added to the Chlamydomonas Modular Cloning toolkit and is now available to the entire scientific community.
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Affiliation(s)
- Félix de Carpentier
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
- Université Paris-Saclay, Saint-Aubin, France
| | - Jeanne Le Peillet
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Nicolas D. Boisset
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
- Université Paris-Saclay, Saint-Aubin, France
| | - Pierre Crozet
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Stéphane D. Lemaire
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Antoine Danon
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
- *Correspondence: Antoine Danon,
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Calles B, Goñi‐Moreno Á, de Lorenzo V. Digitalizing heterologous gene expression in Gram-negative bacteria with a portable ON/OFF module. Mol Syst Biol 2019; 15:e8777. [PMID: 31885200 PMCID: PMC6920698 DOI: 10.15252/msb.20188777] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 10/16/2019] [Accepted: 10/24/2019] [Indexed: 01/24/2023] Open
Abstract
While prokaryotic promoters controlled by signal-responding regulators typically display a range of input/output ratios when exposed to cognate inducers, virtually no naturally occurring cases are known to have an OFF state of zero transcription-as ideally needed for synthetic circuits. To overcome this problem, we have modelled and implemented a simple digitalizer module that completely suppresses the basal level of otherwise strong promoters in such a way that expression in the absence of induction is entirely impeded. The circuit involves the interplay of a translation-inhibitory sRNA with the translational coupling of the gene of interest to a repressor such as LacI. The digitalizer module was validated with the strong inducible promoters Pm (induced by XylS in the presence of benzoate) and PalkB (induced by AlkS/dicyclopropyl ketone) and shown to perform effectively in both Escherichia coli and the soil bacterium Pseudomonas putida. The distinct expression architecture allowed cloning and conditional expression of, e.g. colicin E3, one molecule of which per cell suffices to kill the host bacterium. Revertants that escaped ColE3 killing were not found in hosts devoid of insertion sequences, suggesting that mobile elements are a major source of circuit inactivation in vivo.
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Affiliation(s)
- Belén Calles
- Systems Biology ProgramCentro Nacional de Biotecnología‐CSICMadridSpain
| | - Ángel Goñi‐Moreno
- Systems Biology ProgramCentro Nacional de Biotecnología‐CSICMadridSpain
- Present address:
School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Víctor de Lorenzo
- Systems Biology ProgramCentro Nacional de Biotecnología‐CSICMadridSpain
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HamediRad M, Chao R, Weisberg S, Lian J, Sinha S, Zhao H. Towards a fully automated algorithm driven platform for biosystems design. Nat Commun 2019; 10:5150. [PMID: 31723141 PMCID: PMC6853954 DOI: 10.1038/s41467-019-13189-z] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 10/24/2019] [Indexed: 12/16/2022] Open
Abstract
Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms. Existing efforts have been focused on one of the elements in the automation of the design, build, test, and learn (DBTL) cycle for biosystems design. Here, the authors integrate a robotic system with machine learning algorithms to fully automate the DBTL cycle and apply it in optimizing the lycopene biosynthetic pathway.
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Affiliation(s)
- Mohammad HamediRad
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.,LifeFoundry Inc., 60 Hazelwood Dr., Champaign, IL, 61820, USA
| | - Ran Chao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.,LifeFoundry Inc., 60 Hazelwood Dr., Champaign, IL, 61820, USA
| | - Scott Weisberg
- Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Jiazhang Lian
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.,Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, 310027, Hangzhou, China
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. .,Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. .,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. .,Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. .,Departments of Chemistry and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
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31
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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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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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33
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Yeoh JW, Ng KBI, Teh AY, Zhang J, Chee WKD, Poh CL. An Automated Biomodel Selection System (BMSS) for Gene Circuit Designs. ACS Synth Biol 2019; 8:1484-1497. [PMID: 31035759 DOI: 10.1021/acssynbio.8b00523] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Constructing a complex functional gene circuit composed of different modular biological parts to achieve the desired performance remains challenging without a proper understanding of how the individual module behaves. To address this, mathematical models serve as an important tool toward better interpretation by quantifying the performance of the overall gene circuit, providing insights, and guiding the experimental designs. As different gene circuits might require exclusively different mathematical representations in the form of ordinary differential equations to capture their transient dynamic behaviors, a recurring challenge in model development is the selection of the appropriate model. Here, we developed an automated biomodel selection system (BMSS) which includes a library of pre-established models with intuitive or unintuitive features derived from a vast array of expression profiles. Selection of models is built upon the Akaike information criteria (AIC). We tested the automated platform using characterization data of routinely used inducible systems, constitutive expression systems, and several different logic gate systems (NOT, AND, and OR gates). The BMSS achieved a good agreement for all the different characterization data sets and managed to select the most appropriate model accordingly. To enable exchange and reproducibility of gene circuit design models, the BMSS platform also generates Synthetic Biology Open Language (SBOL)-compliant gene circuit diagrams and Systems Biology Markup Language (SBML) output files. All aspects of the algorithm were programmed in a modular manner to ease the efforts on model extensions or customizations by users. Taken together, the BMSS which is implemented in Python supports users in deriving the best mathematical model candidate in a fast, efficient, and automated way using part/circuit characterization data.
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Affiliation(s)
- Jing Wui Yeoh
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 119077
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 119077
| | - Kai Boon Ivan Ng
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 119077
| | - Ai Ying Teh
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 119077
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 119077
| | - JingYun Zhang
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 119077
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 119077
| | - Wai Kit David Chee
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 119077
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 119077
| | - Chueh Loo Poh
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore 119077
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, Singapore 119077
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34
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Roehner N, Bartley B, Beal J, McLaughlin J, Pocock M, Zhang M, Zundel Z, Myers CJ. Specifying Combinatorial Designs with the Synthetic Biology Open Language (SBOL). ACS Synth Biol 2019; 8:1519-1523. [PMID: 31260271 DOI: 10.1021/acssynbio.9b00092] [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: 11/29/2022]
Abstract
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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Affiliation(s)
- Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | | | - Matthew Pocock
- Turing Ate My Hamster, Ltd., Tyne and Wear, NE27 0RT, UK
| | - Michael Zhang
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Zach Zundel
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Chris J. Myers
- University of Utah, Salt Lake City, Utah 84112, United States
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35
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Farny NG. A Vision for Teaching the Values of Synthetic Biology. Trends Biotechnol 2018; 36:1097-1100. [DOI: 10.1016/j.tibtech.2018.07.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 07/26/2018] [Accepted: 07/30/2018] [Indexed: 01/01/2023]
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36
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Xiang Y, Dalchau N, Wang B. Scaling up genetic circuit design for cellular computing: advances and prospects. NATURAL COMPUTING 2018; 17:833-853. [PMID: 30524216 PMCID: PMC6244767 DOI: 10.1007/s11047-018-9715-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.
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Affiliation(s)
- Yiyu Xiang
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF UK
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, EH9 3JR UK
| | | | - Baojun Wang
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF UK
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh, EH9 3JR UK
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37
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Crozet P, Navarro FJ, Willmund F, Mehrshahi P, Bakowski K, Lauersen KJ, Pérez-Pérez ME, Auroy P, Gorchs Rovira A, Sauret-Gueto S, Niemeyer J, Spaniol B, Theis J, Trösch R, Westrich LD, Vavitsas K, Baier T, Hübner W, de Carpentier F, Cassarini M, Danon A, Henri J, Marchand CH, de Mia M, Sarkissian K, Baulcombe DC, Peltier G, Crespo JL, Kruse O, Jensen PE, Schroda M, Smith AG, Lemaire SD. Birth of a Photosynthetic Chassis: A MoClo Toolkit Enabling Synthetic Biology in the Microalga Chlamydomonas reinhardtii. ACS Synth Biol 2018; 7:2074-2086. [PMID: 30165733 DOI: 10.1021/acssynbio.8b00251] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Microalgae are regarded as promising organisms to develop innovative concepts based on their photosynthetic capacity that offers more sustainable production than heterotrophic hosts. However, to realize their potential as green cell factories, a major challenge is to make microalgae easier to engineer. A promising approach for rapid and predictable genetic manipulation is to use standardized synthetic biology tools and workflows. To this end we have developed a Modular Cloning toolkit for the green microalga Chlamydomonas reinhardtii. It is based on Golden Gate cloning with standard syntax, and comprises 119 openly distributed genetic parts, most of which have been functionally validated in several strains. It contains promoters, UTRs, terminators, tags, reporters, antibiotic resistance genes, and introns cloned in various positions to allow maximum modularity. The toolkit enables rapid building of engineered cells for both fundamental research and algal biotechnology. This work will make Chlamydomonas the next chassis for sustainable synthetic biology.
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Affiliation(s)
- Pierre Crozet
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | | | - Felix Willmund
- Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Payam Mehrshahi
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, U.K
| | - Kamil Bakowski
- Copenhagen Plant Science Centre, Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kyle J. Lauersen
- Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, 33615, Germany
| | - Maria-Esther Pérez-Pérez
- Instituto de Bioquímica Vegetal y Fotosíntesis, CSIC-Universidad de Sevilla, Sevilla, 41092, Spain
| | - Pascaline Auroy
- Laboratoire de Bioénergétique et Biotechnologie des Bactéries et Microalgues Cadarache, Aix Marseille University, CEA, CNRS, BIAM, Saint Paul-Lez-Durance, France
| | - Aleix Gorchs Rovira
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, U.K
| | - Susana Sauret-Gueto
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, U.K
| | - Justus Niemeyer
- Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Benjamin Spaniol
- Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Jasmine Theis
- Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Raphael Trösch
- Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Lisa-Desiree Westrich
- Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Konstantinos Vavitsas
- Copenhagen Plant Science Centre, Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Baier
- Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, 33615, Germany
| | - Wolfgang Hübner
- Biomolecular Photonics, Department of Physics, Bielefeld University, Bielefeld, 33615, Germany
| | - Felix de Carpentier
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Mathieu Cassarini
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Antoine Danon
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Julien Henri
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Christophe H. Marchand
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Marcello de Mia
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - Kevin Sarkissian
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
| | - David C. Baulcombe
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, U.K
| | - Gilles Peltier
- Laboratoire de Bioénergétique et Biotechnologie des Bactéries et Microalgues Cadarache, Aix Marseille University, CEA, CNRS, BIAM, Saint Paul-Lez-Durance, France
| | - José-Luis Crespo
- Instituto de Bioquímica Vegetal y Fotosíntesis, CSIC-Universidad de Sevilla, Sevilla, 41092, Spain
| | - Olaf Kruse
- Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld University, Bielefeld, 33615, Germany
| | - Poul-Erik Jensen
- Copenhagen Plant Science Centre, Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Schroda
- Department of Biology, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany
| | - Alison G. Smith
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, U.K
| | - Stéphane D. Lemaire
- Institut de Biologie Physico-Chimique, UMR 8226, CNRS, Sorbonne Université, Paris, France
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Beal J, Haddock-Angelli T, Baldwin G, Gershater M, Dwijayanti A, Storch M, de Mora K, Lizarazo M, Rettberg R. Quantification of bacterial fluorescence using independent calibrants. PLoS One 2018; 13:e0199432. [PMID: 29928012 PMCID: PMC6013168 DOI: 10.1371/journal.pone.0199432] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 06/07/2018] [Indexed: 11/25/2022] Open
Abstract
Fluorescent reporters are commonly used to quantify activities or properties of both natural and engineered cells. Fluorescence is still typically reported only in arbitrary or normalized units, however, rather than in units defined using an independent calibrant, which is problematic for scientific reproducibility and even more so when it comes to effective engineering. In this paper, we report an interlaboratory study showing that simple, low-cost unit calibration protocols can remedy this situation, producing comparable units and dramatic improvements in precision over both arbitrary and normalized units. Participants at 92 institutions around the world measured fluorescence from E. coli transformed with three engineered test plasmids, plus positive and negative controls, using simple, low-cost unit calibration protocols designed for use with a plate reader and/or flow cytometer. In addition to providing comparable units, use of an independent calibrant allows quantitative use of positive and negative controls to identify likely instances of protocol failure. The use of independent calibrants thus allows order of magnitude improvements in precision, narrowing the 95% confidence interval of measurements in our study up to 600-fold compared to normalized units.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, United States of America
- * E-mail: (JB); (TH-A); (GB)
| | - Traci Haddock-Angelli
- iGEM Foundation, Cambridge, MA, United States of America
- * E-mail: (JB); (TH-A); (GB)
| | - Geoff Baldwin
- Department of Life Sciences, Imperial College London, London, United Kingdom
- * E-mail: (JB); (TH-A); (GB)
| | | | - Ari Dwijayanti
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Marko Storch
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Kim de Mora
- iGEM Foundation, Cambridge, MA, United States of America
| | | | - Randy Rettberg
- iGEM Foundation, Cambridge, MA, United States of America
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39
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Swainston N, Dunstan M, Jervis AJ, Robinson CJ, Carbonell P, Williams AR, Faulon JL, Scrutton NS, Kell DB. PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts. Bioinformatics 2018; 34:2327-2329. [DOI: 10.1093/bioinformatics/bty105] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 02/22/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Mark Dunstan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Adrian J Jervis
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Alan R Williams
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Jean-Loup Faulon
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- MICALIS, INRA-AgroParisTech, Domaine de Vilvert, Jouy en Josas Cedex, France
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
| | - Douglas B Kell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- School of Chemistry, The University of Manchester, Manchester, UK
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40
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McLaughlin JA, Myers CJ, Zundel Z, Mısırlı G, Zhang M, Ofiteru ID, Goñi-Moreno A, Wipat A. SynBioHub: A Standards-Enabled Design Repository for Synthetic Biology. ACS Synth Biol 2018; 7:682-688. [PMID: 29316788 DOI: 10.1021/acssynbio.7b00403] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
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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Affiliation(s)
| | - Chris J. Myers
- 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
| | - Göksel Mısırlı
- School
of Computing and Mathematics, Keele University, Newcastle, ST5 5BG, U.K
| | - Michael Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Irina Dana Ofiteru
- School
of Engineering, Newcastle University, Newcastle upon Tyne, NE1
7RU, U.K
| | - Angel Goñi-Moreno
- School
of Computing, Newcastle University, Newcastle upon Tyne, NE1
7RU, U.K
| | - Anil Wipat
- School
of Computing, Newcastle University, Newcastle upon Tyne, NE1
7RU, U.K
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41
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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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42
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Abstract
Visualization of complex genetic systems can help efficiently communicate important design features and clearly illustrate overall structures. To aid in the creation of such diagrams, standards such as the Synthetic Biology Open Language Visual (SBOLv) have been established to ensure that specific symbols and shapes convey the same meaning for genetic parts across the field. Here, we describe several ways that the computational tool DNAplotlib can be used to automate the generation of SBOLv standard-compliant diagrams covering simple genetic designs to large libraries of genetic constructs.
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Affiliation(s)
- Vittorio Bartoli
- BrisSynBio, University of Bristol, Bristol, UK
- Department of Engineering Mathematics, University of Bristol, Bristol, UK
| | - Daniel O R Dixon
- BrisSynBio, University of Bristol, Bristol, UK
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Thomas E Gorochowski
- BrisSynBio, University of Bristol, Bristol, UK.
- School of Biological Sciences, University of Bristol, Bristol, UK.
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43
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Needs and opportunities in bio-design automation: four areas for focus. Curr Opin Chem Biol 2017; 40:111-118. [PMID: 28923279 DOI: 10.1016/j.cbpa.2017.08.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 08/19/2017] [Accepted: 08/22/2017] [Indexed: 11/21/2022]
Abstract
Bio-design automation (BDA) is an emerging field focused on computer-aided design, engineering principles, and automated manufacturing of biological systems. Here we discuss some outstanding challenges for bio-design that can be addressed by developing new tools for combinatorial engineering, equipment interfacing, next-generation sequencing, and workflow integration. These four areas, while not an exhaustive list of those that need to be addressed, could yield advances in bio-design, laboratory automation, and biometrology.
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44
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Gutiérrez M, Gregorio-Godoy P, Pérez del Pulgar G, Muñoz LE, Sáez S, Rodríguez-Patón A. A New Improved and Extended Version of the Multicell Bacterial Simulator gro. ACS Synth Biol 2017; 6:1496-1508. [PMID: 28438021 DOI: 10.1021/acssynbio.7b00003] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.
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Affiliation(s)
- Martín Gutiérrez
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Escuela
de Informática y Telecomunicaciones, Universidad Diego Portales, 8370190 Santiago, Chile
| | - Paula Gregorio-Godoy
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Guillermo Pérez del Pulgar
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Luis E. Muñoz
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Sandra Sáez
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Alfonso Rodríguez-Patón
- Departamento
de Inteligencia Artificial, ETSIINF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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45
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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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46
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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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47
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Scott-Brown J, Papachristodoulou A. sbml-diff: A Tool for Visually Comparing SBML Models in Synthetic Biology. ACS Synth Biol 2017; 6:1225-1229. [PMID: 28035814 DOI: 10.1021/acssynbio.6b00273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present sbml-diff, a tool that is able to read a model of a biochemical reaction network in SBML format and produce a range of diagrams showing different levels of detail. Each diagram type can be used to visualize a single model or to visually compare two or more models. The default view depicts species as ellipses, reactions as rectangles, rules as parallelograms, and events as diamonds. A cartoon view replaces the symbols used for reactions on the basis of the associated Systems Biology Ontology terms. An abstract view represents species as ellipses and draws edges between them to indicate whether a species increases or decreases the production or degradation of another species. sbml-diff is freely licensed under the three-clause BSD license and can be downloaded from https://github.com/jamesscottbrown/sbml-diff and used as a python package called from other software, as a free-standing command-line application, or online using the form at http://sysos.eng.ox.ac.uk/tebio/upload.
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Affiliation(s)
- James Scott-Brown
- Department of Engineering
Science, University of Oxford, Parks Road, Oxford OX1 3PJ, U.K
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48
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Der BS, Glassey E, Bartley BA, Enghuus C, Goodman DB, Gordon DB, Voigt CA, Gorochowski TE. DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data. ACS Synth Biol 2017; 6:1115-1119. [PMID: 27744689 DOI: 10.1021/acssynbio.6b00252] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
DNAplotlib ( www.dnaplotlib.org ) is a computational toolkit for the programmable visualization of highly customizable, standards-compliant genetic designs. Functions are provided to aid with both visualization tasks and to extract and overlay associated experimental data. High-quality output is produced in the form of vector-based PDFs, rasterized images, and animated movies. All aspects of the rendering process can be easily customized or extended by the user to cover new forms of genetic part or regulation. DNAplotlib supports improved communication of genetic design information and offers new avenues for static, interactive and dynamic visualizations that map and explore the links between the structure and function of genetic parts, devices and systems; including metabolic pathways and genetic circuits. DNAplotlib is cross-platform software developed using Python.
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Affiliation(s)
- Bryan S. Der
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Emerson Glassey
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Bryan A. Bartley
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Casper Enghuus
- MIT Microbiology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Daniel B. Goodman
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, United States
- Wyss Institute for Biologically Inspired
Engineering, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - D. Benjamin Gordon
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Christopher A. Voigt
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Thomas E. Gorochowski
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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49
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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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50
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Mısırlı G, Madsen C, Murieta IS, Bultelle M, Flanagan K, Pocock M, Hallinan J, McLaughlin JA, Clark‐Casey J, Lyne M, Micklem G, Stan G, Kitney R, Wipat A. Constructing synthetic biology workflows in the cloud. ENGINEERING BIOLOGY 2017. [DOI: 10.1049/enb.2017.0001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Göksel Mısırlı
- School of Computing Science Newcastle University Newcastle upon Tyne UK
| | - Curtis Madsen
- Electrical & Computer Engineering Department Boston University Boston USA
| | | | | | - Keith Flanagan
- School of Computing Science Newcastle University Newcastle upon Tyne UK
| | | | | | | | - Justin Clark‐Casey
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Mike Lyne
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Gos Micklem
- Department of Genetics, Cambridge Systems Biology Centre University of Cambridge Cambridge UK
| | - Guy‐Bart Stan
- Department of Bioengineering Imperial College London London UK
| | - Richard Kitney
- Department of Bioengineering Imperial College London London UK
| | - Anil Wipat
- School of Computing Science Newcastle University Newcastle upon Tyne UK
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