1
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Adler A, Bader JS, Basnight B, Booth BW, Cai J, Cho E, Collins JH, Ge Y, Grothendieck J, Keating K, Marshall T, Persikov A, Scott H, Siegelmann R, Singh M, Taggart A, Toll B, Wan KH, Wyschogrod D, Yaman F, Young EM, Celniker SE, Roehner N. Ensemble Detection of DNA Engineering Signatures. ACS Synth Biol 2024; 13:1105-1115. [PMID: 38468602 DOI: 10.1021/acssynbio.3c00398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Synthetic biology is creating genetically engineered organisms at an increasing rate for many potentially valuable applications, but this potential comes with the risk of misuse or accidental release. To begin to address this issue, we have developed a system called GUARDIAN that can automatically detect signatures of engineering in DNA sequencing data, and we have conducted a blinded test of this system using a curated Test and Evaluation (T&E) data set. GUARDIAN uses an ensemble approach based on the guiding principle that no single approach is likely to be able to detect engineering with perfect accuracy. Critically, ensembling enables GUARDIAN to detect sequence inserts in 13 target organisms with a high degree of specificity that requires no subject matter expert (SME) review.
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Affiliation(s)
- Aaron Adler
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Joel S Bader
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Brian Basnight
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Benjamin W Booth
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Jitong Cai
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Elizabeth Cho
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Joseph H Collins
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Yuchen Ge
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | | | - Kevin Keating
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Tyler Marshall
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Anton Persikov
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, United States
| | - Helen Scott
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Roy Siegelmann
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, New Jersey 08544, United States
| | | | - Benjamin Toll
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Kenneth H Wan
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | | | - Fusun Yaman
- Raytheon BBN, Cambridge, Massachusetts 02138, United States
| | - Eric M Young
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Susan E Celniker
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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2
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Beal J, Selvarajah V, Chambonnier G, Haddock T, Vignoni A, Vidal G, Roehner N. Standardized Representation of Parts and Assembly for Build Planning. ACS Synth Biol 2023; 12:3646-3655. [PMID: 37956262 DOI: 10.1021/acssynbio.3c00418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The design and construction of genetic systems, in silico, in vitro, or in vivo, often involve the handling of various pieces of DNA that exist in different forms across an assembly process: as a standalone "part" sequence, as an insert into a carrier vector, as a digested fragment, etc. Communication about these different forms of a part and their relationships is often confusing, however, because of a lack of standardized terms. Here, we present a systematic terminology and an associated set of practices for representing genetic parts at various stages of design, synthesis, and assembly. These practices are intended to represent any of the wide array of approaches based on embedding parts in carrier vectors, such as BioBricks or Type IIS methods (e.g., GoldenGate, MoClo, GoldenBraid, and PhytoBricks), and have been successfully used as a basis for cross-institutional coordination and software tooling in the iGEM Engineering Committee.
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Affiliation(s)
- Jacob Beal
- Intelligent Software & Systems, Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
| | - Vinoo Selvarajah
- iGEM Foundation, 45 Prospect Street, Cambridge, Massachusetts 02139, United States
| | - Gaël Chambonnier
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Traci Haddock
- Asimov, Inc., 201 Brookline Avenue, Suite 1201, Boston, Massachusetts 02215, United States
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain
| | - Gonzalo Vidal
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Nicholas Roehner
- Intelligent Software & Systems, Raytheon BBN Technologies, 10 Moulton Street, Cambridge, Massachusetts 02138, United States
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3
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Cummins B, Vrana J, Moseley RC, Eramian H, Deckard A, Fontanarrosa P, Bryce D, Weston M, Zheng G, Nowak J, Motta FC, Eslami M, Johnson KL, Goldman RP, Myers CJ, Johnson T, Vaughn MW, Gaffney N, Urrutia J, Gopaulakrishnan S, Biggers V, Higa TR, Mosqueda LA, Gameiro M, Gedeon T, Mischaikow K, Beal J, Bartley B, Mitchell T, Nguyen TT, Roehner N, Haase SB. Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop. Synth Biol (Oxf) 2023; 8:ysad005. [PMID: 37073283 PMCID: PMC10105856 DOI: 10.1093/synbio/ysad005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 02/22/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023] Open
Abstract
Abstract
Computational tools addressing various components of design–build–test–learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test–learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions.
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Affiliation(s)
| | | | | | | | | | - Pedro Fontanarrosa
- Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
| | | | | | | | | | - Francis C Motta
- Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA
| | | | - Kara Layne Johnson
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
| | | | - Chris J Myers
- Electrical, Computer & Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
| | | | | | | | | | | | | | | | | | - Marcio Gameiro
- Department of Mathematics, Rutgers University, New Brunswick, NJ, USA
| | - Tomáš Gedeon
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
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4
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Goldman RP, Moseley R, Roehner N, Cummins B, Vrana JD, Clowers KJ, Bryce D, Beal J, DeHaven M, Nowak J, Higa T, Biggers V, Lee P, Hunt JP, Mosqueda L, Haase SB, Weston M, Zheng G, Deckard A, Gopaulakrishnan S, Stubbs JF, Gaffney NI, Vaughn MW, Maheshri N, Mikhalev E, Bartley B, Markeloff R, Mitchell T, Nguyen T, Sumorok D, Walczak N, Myers C, Zundel Z, Hatch B, Scholz J, Colonna-Romano J. Highly-automated, high-throughput replication of yeast-based logic circuit design assessments. Synth Biol (Oxf) 2022; 7:ysac018. [PMID: 36285185 PMCID: PMC9583850 DOI: 10.1093/synbio/ysac018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 08/19/2022] [Accepted: 09/13/2022] [Indexed: 12/01/2023] Open
Abstract
We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency's Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned. Graphical Abstract.
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Affiliation(s)
- Robert P Goldman
- SIFT, LLC, 319 First Ave, North, Suite 400, Minneapolis, MN 55401, USA
| | - Robert Moseley
- Department of Biology, Box 90338, Duke University, Durham, NC 27708, USA
| | | | - Breschine Cummins
- Department of Mathematical Sciences, Montana State University, P.O. Box 172400, Bozeman, MT 59717-2400, USA
| | - Justin D Vrana
- Just – Evotec Biologics, 401 Terry Ave N, Seattle, WA 98109,USA
| | - Katie J Clowers
- Ginkgo Bioworks, 27 Drydock Ave 8th Floor, Boston, MA 02210,USA
| | - Daniel Bryce
- SIFT, LLC, 319 First Ave, North, Suite 400, Minneapolis, MN 55401, USA
| | - Jacob Beal
- BBN/Raytheon, 10 Moulton Street, Cambridge, MA 02138, USA
| | - Matthew DeHaven
- SIFT, LLC, 319 First Ave, North, Suite 400, Minneapolis, MN 55401, USA
| | | | | | | | - Peter Lee
- Ginkgo Bioworks, 27 Drydock Ave 8th Floor, Boston, MA 02210,USA
| | | | | | - Steven B Haase
- Department of Biology, Box 90338, Duke University, Durham, NC 27708, USA
| | - Mark Weston
- Texas Advanced Computer Center (TACC), University of Texas, 10100 Burnet Rd, Austin, TX 78758, USA
| | - George Zheng
- Texas Advanced Computer Center (TACC), University of Texas, 10100 Burnet Rd, Austin, TX 78758, USA
| | | | | | - Joseph F Stubbs
- Texas Advanced Computing Center, University of Texas at Austin
| | - Niall I Gaffney
- Texas Advanced Computing Center, University of Texas at Austin
| | | | | | | | - Bryan Bartley
- BBN/Raytheon, 10 Moulton Street, Cambridge, MA 02138, USA
| | | | - Tom Mitchell
- BBN/Raytheon, 10 Moulton Street, Cambridge, MA 02138, USA
| | - Tramy Nguyen
- BBN/Raytheon, 10 Moulton Street, Cambridge, MA 02138, USA
| | - Daniel Sumorok
- BBN/Raytheon, 10 Moulton Street, Cambridge, MA 02138, USA
| | | | - Chris Myers
- Department of Electrical, Computer & Energy Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, 425 UCB #1B55, Boulder, CO 80309, USA
| | - Zach Zundel
- Department of Electrical and Computer Engineering, University of Utah, 50 Central Campus Dr #2110, Salt Lake City, UT 84112, USA
| | - Benjamin Hatch
- Department of Electrical and Computer Engineering, University of Utah, 50 Central Campus Dr #2110, Salt Lake City, UT 84112, USA
| | - James Scholz
- Department of Electrical and Computer Engineering, University of Utah, 50 Central Campus Dr #2110, Salt Lake City, UT 84112, USA
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5
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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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6
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Mante J, Roehner N, Keating K, McLaughlin JA, Young E, Beal J, Myers CJ. Curation Principles Derived from the Analysis of the SBOL iGEM Data Set. ACS Synth Biol 2021; 10:2592-2606. [PMID: 34546707 DOI: 10.1021/acssynbio.1c00225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
As an engineering endeavor, synthetic biology requires effective sharing of genetic design information that can be reused in the construction of new designs. While there are a number of large community repositories of design information, curation of this information has been limited. This in turn limits the ways in which design information can be put to use. The aim of this work was to improve this situation by creating a curated library of parts from the International Genetically Engineered Machines (iGEM) registry data set. To this end, an analysis of the Synthetic Biology Open Language (SBOL) version of the iGEM registry was carried out using four different approaches-simple statistics, SnapGene autoannotation, SYNBICT autoannotation, and expert analysis-the results of which are presented herein. Key challenges encountered include the use of free text, insufficient part provenance, part duplication, lack of part removal, and insufficient continuous curation. On the basis of these analyses, the focus has shifted from the creation of a curated iGEM part library to instead the extraction of a set of lessons, which are presented here. These lessons can be exploited to facilitate the creation and curation of other part libraries using a simpler and less labor intensive process.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Nicholas Roehner
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Kevin Keating
- Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | | | - Eric Young
- Worcester Polytechnic Institute, Worcester, Massachusetts 01609, 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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7
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Baig H, Fontanarossa P, Kulkarni V, McLaughlin J, Vaidyanathan P, Bartley B, Bhakta S, Bhatia S, Bissell M, Clancy K, Cox RS, Goñi Moreno A, Gorochowski T, Grunberg R, Lee J, Luna A, Madsen C, Misirli G, Nguyen T, Le Novere N, Palchick Z, Pocock M, Roehner N, Sauro H, Scott-Brown J, Sexton JT, Stan GB, Tabor JJ, Terry L, Vazquez Vilar M, Voigt CA, Wipat A, Zong D, Zundel Z, Beal J, Myers C. Synthetic biology open language visual (SBOL Visual) version 2.3. J Integr Bioinform 2021; 18:jib-2020-0045. [PMID: 34098590 PMCID: PMC8560345 DOI: 10.1515/jib-2020-0045] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 01/11/2021] [Indexed: 11/16/2022] Open
Abstract
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.3 of SBOL Visual, which builds on the prior SBOL Visual 2.2 in several ways. First, the specification now includes higher-level “interactions with interactions,” such as an inducer molecule stimulating a repression interaction. Second, binding with a nucleic acid backbone can be shown by overlapping glyphs, as with other molecular complexes. Finally, a new “unspecified interaction” glyph is added for visualizing interactions whose nature is unknown, the “insulator” glyph is deprecated in favor of a new “inert DNA spacer” glyph, and the polypeptide region glyph is recommended for showing 2A sequences.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Anil Wipat
- Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, USA
| | - Chris Myers
- University of Colorado Boulder, Boulder, USA
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8
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Collins JH, Keating KW, Jones TR, Balaji S, Marsan CB, Çomo M, Newlon ZJ, Mitchell T, Bartley B, Adler A, Roehner N, Young EM. Engineered yeast genomes accurately assembled from pure and mixed samples. Nat Commun 2021; 12:1485. [PMID: 33674578 PMCID: PMC7935868 DOI: 10.1038/s41467-021-21656-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 02/04/2021] [Indexed: 01/31/2023] Open
Abstract
Yeast whole genome sequencing (WGS) lacks end-to-end workflows that identify genetic engineering. Here we present Prymetime, a tool that assembles yeast plasmids and chromosomes and annotates genetic engineering sequences. It is a hybrid workflow-it uses short and long reads as inputs to perform separate linear and circular assembly steps. This structure is necessary to accurately resolve genetic engineering sequences in plasmids and the genome. We show this by assembling diverse engineered yeasts, in some cases revealing unintended deletions and integrations. Furthermore, the resulting whole genomes are high quality, although the underlying assembly software does not consistently resolve highly repetitive genome features. Finally, we assemble plasmids and genome integrations from metagenomic sequencing, even with 1 engineered cell in 1000. This work is a blueprint for building WGS workflows and establishes WGS-based identification of yeast genetic engineering.
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Affiliation(s)
- Joseph H Collins
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Kevin W Keating
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Trent R Jones
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Shravani Balaji
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Celeste B Marsan
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Marina Çomo
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Zachary J Newlon
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Tom Mitchell
- Synthetic Biology, Raytheon BBN Technologies, Cambridge, MA, USA
| | - Bryan Bartley
- Synthetic Biology, Raytheon BBN Technologies, Cambridge, MA, USA
| | - Aaron Adler
- Synthetic Biology, Raytheon BBN Technologies, Cambridge, MA, USA
| | - Nicholas Roehner
- Synthetic Biology, Raytheon BBN Technologies, Cambridge, MA, USA
| | - Eric M Young
- Department of Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
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9
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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10
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Baig H, Fontanarrosa P, Kulkarni V, McLaughlin J, Vaidyanathan P, Bartley B, Bhatia S, Bhakta S, Bissell M, Clancy K, Cox RS, Moreno AG, Gorochowski T, Grunberg R, Luna A, Madsen C, Misirli G, Nguyen T, Le Novere N, Palchick Z, Pocock M, Roehner N, Sauro H, Scott-Brown J, Sexton JT, Stan GB, Tabor JJ, Vilar MV, Voigt CA, Wipat A, Zong D, Zundel Z, Beal J, Myers C. Synthetic biology open language visual (SBOL visual) version 2.2. J Integr Bioinform 2020; 17:jib-2020-0014. [PMID: 32543457 PMCID: PMC7756616 DOI: 10.1515/jib-2020-0014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 04/16/2020] [Indexed: 11/15/2022] Open
Abstract
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.2 of SBOL Visual, which builds on the prior SBOL Visual 2.1 in several ways. First, the grounding of molecular species glyphs is changed from BioPAX to SBO, aligning with the use of SBO terms for interaction glyphs. Second, new glyphs are added for proteins, introns, and polypeptide regions (e. g., protein domains), the prior recommended macromolecule glyph is deprecated in favor of its alternative, and small polygons are introduced as alternative glyphs for simple chemicals.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Anil Wipat
- Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, USA
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11
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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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12
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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13
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Madsen C, Goni Moreno A, Palchick Z, P U, Roehner N, Bartley B, Bhatia S, Bhakta S, Bissell M, Clancy K, Cox RS, Gorochowski T, Grunberg R, Luna A, McLaughlin J, Nguyen T, Le Novere N, Pocock M, Sauro H, Scott-Brown J, Sexton JT, Stan GB, Tabor JJ, Voigt CA, Zundel Z, Myers C, Beal J, Wipat A. Synthetic Biology Open Language Visual (SBOL Visual) Version 2.1. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2018-0101/jib-2018-0101.xml. [PMID: 31199768 PMCID: PMC6798824 DOI: 10.1515/jib-2018-0101] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 05/20/2019] [Indexed: 11/15/2022] Open
Abstract
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species . Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.1 of SBOL Visual, which builds on the prior SBOL Visual 2.0 standard by expanding diagram syntax to include methods for showing modular structure and mappings between elements of a system, interactions arrows that can split or join (with the glyph at the split or join indicating either superposition or a chemical process), and adding new glyphs for indicating genomic context (e.g., integration into a plasmid or genome) and for stop codons.
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Affiliation(s)
| | | | | | - Umesh P
- Kerala Technological University, Thiruvananthapuram, India
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA
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14
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Madsen C, Goñi Moreno A, P U, Palchick Z, Roehner N, Atallah C, Bartley B, Choi K, Cox RS, Gorochowski T, Grünberg R, Macklin C, McLaughlin J, Meng X, Nguyen T, Pocock M, Samineni M, Scott-Brown J, Tarter Y, Zhang M, Zhang Z, Zundel Z, Beal J, Bissell M, Clancy K, Gennari JH, Misirli G, Myers C, Oberortner E, Sauro H, Wipat A. Synthetic Biology Open Language (SBOL) Version 2.3. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2019-0025/jib-2019-0025.xml. [PMID: 31199770 PMCID: PMC6798821 DOI: 10.1515/jib-2019-0025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 05/20/2019] [Indexed: 11/16/2022] Open
Abstract
Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year’s JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.
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Affiliation(s)
| | | | - Umesh P
- Kerala Technological University, Thiruvananthapuram, India
| | | | | | | | | | - Kiri Choi
- University of Washington, Seattle, WA, USA
| | | | | | | | | | | | - Xianwei Meng
- DOE Joint Genome Institute, Walnut Creek, CA, USA
| | | | | | | | | | | | | | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA
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15
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Cox RS, Madsen C, McLaughlin JA, Nguyen T, Roehner N, Bartley B, Beal J, Bissell M, Choi K, Clancy K, Grünberg R, Macklin C, Misirli G, Oberortner E, Pocock M, Samineni M, Zhang M, Zhang Z, Zundel Z, Gennari JH, Myers C, Sauro H, Wipat A. Synthetic Biology Open Language (SBOL) Version 2.2.0. J Integr Bioinform 2018; 15:/j/jib.ahead-of-print/jib-2018-0001/jib-2018-0001.xml. [PMID: 29605823 PMCID: PMC6167039 DOI: 10.1515/jib-2018-0001] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Accepted: 02/01/2018] [Indexed: 12/03/2022] Open
Abstract
Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.2.0 of SBOL that builds upon version 2.1.0 published in last year’s JIB special issue. In particular, SBOL 2.2.0 includes improved description and validation rules for genetic design provenance, an extension to support combinatorial genetic designs, a new class to add non-SBOL data as attachments, a new class for genetic design implementations, and a description of a methodology to describe the entire design-build-test-learn cycle within the SBOL data model.
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Affiliation(s)
| | | | | | | | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA
| | | | - Kiri Choi
- University of Washington, Seattle, WA, USA
| | | | - Raik Grünberg
- King Abdullah University for Science and Technology, Thuwal, Saudi Arabia
| | | | - Goksel Misirli
- Keele University, Keele, Staffordshire, United Kingdom of Great Britain and Northern Ireland
| | | | | | | | | | | | | | | | | | | | - Anil Wipat
- Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland
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16
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Cox RS, Madsen C, McLaughlin J, Nguyen T, Roehner N, Bartley B, Bhatia S, Bissell M, Clancy K, Gorochowski T, Grünberg R, Luna A, Le Novère N, Pocock M, Sauro H, Sexton JT, Stan GB, Tabor JJ, Voigt CA, Zundel Z, Myers C, Beal J, Wipat A. Synthetic Biology Open Language Visual (SBOL Visual) Version 2.0. J Integr Bioinform 2018; 15:/j/jib.ahead-of-print/jib-2017-0074/jib-2017-0074.xml. [PMID: 29549707 PMCID: PMC6167035 DOI: 10.1515/jib-2017-0074] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 02/01/2018] [Indexed: 11/15/2022] Open
Abstract
People who are engineering biological organisms often find it useful to communicate in diagrams, both about the structure of the nucleic acid sequences that they are engineering and about the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. The Synthetic Biology Open Language Visual (SBOL Visual) has been developed as a standard for organizing and systematizing such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 2.0 of SBOL Visual, which builds on the prior SBOL Visual 1.0 standard by expanding diagram syntax to include functional interactions and molecular species, making the relationship between diagrams and the SBOL data model explicit, supporting families of symbol variants, clarifying a number of requirements and best practices, and significantly expanding the collection of diagram glyphs.
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Affiliation(s)
| | | | - James McLaughlin
- Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland
| | | | | | | | | | | | | | - Thomas Gorochowski
- University of Bristol, Bristol, United Kingdom of Great Britain and Northern Ireland
| | - Raik Grünberg
- King Abdullah University of Science and Technology (KAUST), BESE, Thuwal 23955 - 6900, Saudi Arabia
| | | | - Nicolas Le Novère
- Babraham Institute, Cambridge, Cambridgeshire, United Kingdom of Great Britain and Northern Ireland
| | - Matthew Pocock
- Turing Ate My Hamster, Ltd., Newcastle, United Kingdom of Great Britain and Northern Ireland
| | | | | | - Guy-Bart Stan
- Imperial College, London, United Kingdom of Great Britain and Northern Ireland
| | | | | | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA
| | - Anil Wipat
- Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland
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17
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Woodruff LBA, Gorochowski TE, Roehner N, Mikkelsen TS, Densmore D, Gordon DB, Nicol R, Voigt CA. Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration. Nucleic Acids Res 2017; 45:1553-1565. [PMID: 28007941 PMCID: PMC5388403 DOI: 10.1093/nar/gkw1226] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 11/22/2016] [Indexed: 11/14/2022] Open
Abstract
Genetic designs can consist of dozens of genes and hundreds of genetic parts. After evaluating a design, it is desirable to implement changes without the cost and burden of starting the construction process from scratch. Here, we report a two-step process where a large design space is divided into deep pools of composite parts, from which individuals are retrieved and assembled to build a final construct. The pools are built via multiplexed assembly and sequenced using next-generation sequencing. Each pool consists of ∼20 Mb of up to 5000 unique and sequence-verified composite parts that are barcoded for retrieval by PCR. This approach is applied to a 16-gene nitrogen fixation pathway, which is broken into pools containing a total of 55 848 composite parts (71.0 Mb). The pools encompass an enormous design space (1043 possible 23 kb constructs), from which an algorithm-guided 192-member 4.5 Mb library is built. Next, all 1030 possible genetic circuits based on 10 repressors (NOR/NOT gates) are encoded in pools where each repressor is fused to all permutations of input promoters. These demonstrate that multiplexing can be applied to encompass entire design spaces from which individuals can be accessed and evaluated.
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Affiliation(s)
- Lauren B A Woodruff
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Thomas E Gorochowski
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas Roehner
- Biological Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Tarjei S Mikkelsen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Douglas Densmore
- Biological Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - D Benjamin Gordon
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert Nicol
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Christopher A Voigt
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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18
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Abstract
Design automation refers to a category of software tools for designing systems that work together in a workflow for designing, building, testing, and analyzing systems with a target behavior. In synthetic biology, these tools are called bio-design automation (BDA) tools. In this review, we discuss the BDA tools areas-specify, design, build, test, and learn-and introduce the existing software tools designed to solve problems in these areas. We then detail the functionality of some of these tools and show how they can be used together to create the desired behavior of two types of modern synthetic genetic regulatory networks.
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Affiliation(s)
- Evan Appleton
- Department of Genetics, Harvard Medical School, Harvard University, Boston, Massachusetts 02115
| | - Curtis Madsen
- Biological Design Center, Boston University, Boston, Massachusetts 02215.,Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
| | - Nicholas Roehner
- Biological Design Center, Boston University, Boston, Massachusetts 02215.,Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, Massachusetts 02215.,Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215
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19
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Woodruff LBA, Gorochowski TE, Roehner N, Mikkelsen TS, Densmore D, Gordon DB, Nicol R, Voigt CA. Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration. Nucleic Acids Res 2017; 45:1567-1568. [PMID: 28100691 PMCID: PMC5388430 DOI: 10.1093/nar/gkx032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Lauren B A Woodruff
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Thomas E Gorochowski
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas Roehner
- Biological Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Tarjei S Mikkelsen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Douglas Densmore
- Biological Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - D Benjamin Gordon
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert Nicol
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Christopher A Voigt
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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20
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Beal J, Cox RS, Grünberg R, McLaughlin J, Nguyen T, Bartley B, Bissell M, Choi K, Clancy K, Macklin C, Madsen C, Misirli G, Oberortner E, Pocock M, Roehner N, Samineni M, Zhang M, Zhang Z, Zundel Z, Gennari JH, Myers C, Sauro H, Wipat A. Synthetic Biology Open Language (SBOL) Version 2.1.0. J Integr Bioinform 2016. [DOI: 10.1515/jib-2016-291] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
SummarySynthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.1 of SBOL that builds upon version 2.0 published in last year’s JIB special issue. In particular, SBOL 2.1 includes improved rules for what constitutes a valid SBOL document, new role fields to simplify the expression of sequence features and how components are used in context, and new best practices descriptions to improve the exchange of basic sequence topology information and the description of genetic design provenance, as well as miscellaneous other minor improvements.
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21
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Abstract
Standards are important to synthetic biology because they enable exchange and reproducibility of genetic designs. This paper describes a procedure for converting between two standards: the Systems Biology Markup Language (SBML) and the Synthetic Biology Open Language (SBOL). SBML is a standard for behavioral models of biological systems at the molecular level. SBOL describes structural and basic qualitative behavioral aspects of a biological design. Converting SBML to SBOL enables a consistent connection between behavioral and structural information for a biological design. The conversion process described in this paper leverages Systems Biology Ontology (SBO) annotations to enable inference of a designs qualitative function.
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Affiliation(s)
| | - Nicholas Roehner
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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22
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Roehner N, Beal J, Clancy K, Bartley B, Misirli G, Grünberg R, Oberortner E, Pocock M, Bissell M, Madsen C, Nguyen T, Zhang M, Zhang Z, Zundel Z, Densmore D, Gennari JH, Wipat A, Sauro HM, Myers CJ. Sharing Structure and Function in Biological Design with SBOL 2.0. ACS Synth Biol 2016; 5:498-506. [PMID: 27111421 DOI: 10.1021/acssynbio.5b00215] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The Synthetic Biology Open Language (SBOL) is a standard that enables collaborative engineering of biological systems across different institutions and tools. SBOL is developed through careful consideration of recent synthetic biology trends, real use cases, and consensus among leading researchers in the field and members of commercial biotechnology enterprises. We demonstrate and discuss how a set of SBOL-enabled software tools can form an integrated, cross-organizational workflow to recapitulate the design of one of the largest published genetic circuits to date, a 4-input AND sensor. This design encompasses the structural components of the system, such as its DNA, RNA, small molecules, and proteins, as well as the interactions between these components that determine the system's behavior/function. The demonstrated workflow and resulting circuit design illustrate the utility of SBOL 2.0 in automating the exchange of structural and functional specifications for genetic parts, devices, and the biological systems in which they operate.
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Affiliation(s)
- Nicholas Roehner
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Kevin Clancy
- Thermo Fisher Scientific, Carlsbad, California 92008, United States
| | - Bryan Bartley
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Goksel Misirli
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Raik Grünberg
- Institute
for Research in Immunology and Cancer, University of Montreal, Montreal, Quebec H3T 1J4, Canada
| | - Ernst Oberortner
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, California 94598, United States
| | - Matthew Pocock
- Turing Ate My Hamster, Ltd., Newcastle
upon Tyne NE27 0RT, U.K
| | | | - Curtis Madsen
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Tramy Nguyen
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Michael Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Zhen Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Zach Zundel
- Department
of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Douglas Densmore
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - John H. Gennari
- Department
of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington 98195, United States
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K
| | - Herbert M. Sauro
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
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23
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Abstract
Recently, semirational approaches that rely on combinatorial assembly of characterized DNA components have been used to engineer biosynthetic pathways. In practice, however, it is not practical to assemble and test millions of pathway variants in order to elucidate how different DNA components affect the behavior of a pathway. To address this challenge, we apply a rigorous mathematical approach known as design of experiments (DOE) that can be used to construct empirical models of system behavior without testing all variants. To support this approach, we have developed a tool named Double Dutch, which uses a formal grammar and heuristic algorithms to automate the process of DOE library design. Compared to designing by hand, Double Dutch enables users to more efficiently and scalably design libraries of pathway variants that can be used in a DOE framework and uniquely provides a means to flexibly balance design considerations of statistical analysis, construction cost, and risk of homologous recombination, thereby demonstrating the utility of automating decision making when faced with complex design trade-offs.
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Affiliation(s)
- Nicholas Roehner
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Eric M. Young
- Department
of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Christopher A. Voigt
- Department
of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - D. Benjamin Gordon
- Department
of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Douglas Densmore
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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24
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Zhang Z, Nguyen T, Roehner N, Misirli G, Pocock M, Oberortner E, Samineni M, Zundel Z, Beal J, Clancy K, Wipat A, Myers CJ. libSBOLj 2.0: A Java Library to Support SBOL 2.0. ACTA ACUST UNITED AC 2015. [DOI: 10.1109/lls.2016.2546546] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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25
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Abstract
In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).
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Affiliation(s)
- Nicholas Roehner
- Department
of Bioengineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Zhen Zhang
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Tramy Nguyen
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City, Utah 84112, United States
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26
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Bartley B, Beal J, Clancy K, Misirli G, Roehner N, Oberortner E, Pocock M, Bissell M, Madsen C, Nguyen T, Zhang Z, Gennari JH, Myers C, Wipat A, Sauro H. Synthetic Biology Open Language (SBOL) Version 2.0.0. J Integr Bioinform 2015. [DOI: 10.1515/jib-2015-272] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Summary Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.0 of SBOL, introducing a standardized format for the electronic exchange of information on the structural and functional aspects of biological designs. The standard has been designed to support the explicit and unambiguous description of biological designs by means of a well defined data model. The standard also includes rules and best practices on how to use this data model and populate it with relevant design details. The publication of this specification is intended to make these capabilities more widely accessible to potential developers and users in the synthetic biology community and beyond.
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Affiliation(s)
- Bryan Bartley
- 1University of Washington, Seattle, United States of America
| | - Jacob Beal
- 2Raytheon BBN Technologies, Cambridge, United States of America
| | - Kevin Clancy
- 3ThermoFisher Scientific, Waltham, United States of America
| | - Goksel Misirli
- 4Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland
| | | | - Ernst Oberortner
- 6DOE Joint Genome Institute, Walnut Creek, United States of America
| | - Matthew Pocock
- 4Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland
| | | | - Curtis Madsen
- 4Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland
| | - Tramy Nguyen
- 8University of Utah, Salt Lake City, United States of America
| | - Zhen Zhang
- 8University of Utah, Salt Lake City, United States of America
| | - John H. Gennari
- 1University of Washington, Seattle, United States of America
| | - Chris Myers
- 8University of Utah, Salt Lake City, United States of America
| | - Anil Wipat
- 4Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland
| | - Herbert Sauro
- 1University of Washington, Seattle, United States of America
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Roehner N, Oberortner E, Pocock M, Beal J, Clancy K, Madsen C, Misirli G, Wipat A, Sauro H, Myers CJ. Proposed data model for the next version of the synthetic biology open language. ACS Synth Biol 2015; 4:57-71. [PMID: 24896221 DOI: 10.1021/sb500176h] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
While the first version of the Synthetic Biology Open Language (SBOL) has been adopted by several academic and commercial genetic design automation (GDA) software tools, it only covers a limited number of the requirements for a standardized exchange format for synthetic biology. In particular, SBOL Version 1.1 is capable of representing DNA components and their hierarchical composition via sequence annotations. This proposal revises SBOL Version 1.1, enabling the representation of a wider range of components with and without sequences, including RNA components, protein components, small molecules, and molecular complexes. It also introduces modules to instantiate groups of components on the basis of their shared function and assert molecular interactions between components. By increasing the range of structural and functional descriptions in SBOL and allowing for their composition, the proposed improvements enable SBOL to represent and facilitate the exchange of a broader class of genetic designs.
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Affiliation(s)
- Nicholas Roehner
- Department of Bioengineering, University of Utah, Salt Lake City, Utah, United States
| | - Ernst Oberortner
- Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, United States
| | - Matthew Pocock
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts, United States
| | - Kevin Clancy
- Life Technologies, Carlsbad, California, United States
| | - Curtis Madsen
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Goksel Misirli
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Herbert Sauro
- Department of Bioengineering, University of Washington, Seattle, Washington, United States
| | - Chris J. Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, United States
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Madsen C, Myers C, Roehner N, Winstead C, Zhang Z. Efficient analysis methods in synthetic biology. Methods Mol Biol 2014; 1244:217-57. [PMID: 25487100 DOI: 10.1007/978-1-4939-1878-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
This chapter describes new analysis and verification techniques for synthetic genetic circuits. In particular, it applies stochastic model checking techniques to models of genetic circuits in order to ensure that they behave correctly and are as robust as possible for a variety of different inputs and parameter settings. In addition to stochastic model checking, this chapter proposes new variants to the incremental stochastic simulation algorithm (iSSA) that are capable of presenting a researcher with a simulation trace of the typical behavior of the system. Before the development of this algorithm, discerning this information was extremely error-prone as it involved performing many simulations and attempting to wade through the massive amounts of data. This algorithm greatly aids researchers in designing genetic circuits as it efficiently shows the researcher the most likely behavior of the circuit. Both the iSSA and stochastic model checking can be used in concert to give a researcher the likelihood that the system exhibits its most typical behavior, as well as, non-typical behaviors. This methodology is applied to several genetic circuits leading to new understanding of the effects of various parameters on the behavior of these circuits.
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Affiliation(s)
- Curtis Madsen
- School of Computing, University of Utah, Salt Lake City, UT, USA,
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Abstract
As engineering foundations such as standards and abstraction begin to mature within synthetic biology, it is vital that genetic design automation (GDA) tools be developed to enable synthetic biologists to automatically select standardized DNA components from a library to meet the behavioral specification for a genetic circuit. To this end, we have developed a genetic technology mapping algorithm that builds on the directed acyclic graph (DAG) based mapping techniques originally used to select parts for digital electronic circuit designs and implemented it in our GDA tool, iBioSim. It is among the first genetic technology mapping algorithms to adapt techniques from electronic circuit design, in particular the use of a cost function to guide the search for an optimal solution, and perhaps that which makes the greatest use of standards for describing genetic function and structure to represent design specifications and component libraries. This paper demonstrates the use of our algorithm to map the specifications for three different genetic circuits against four randomly generated libraries of increasing size to evaluate its performance against both exhaustive search and greedy variants for finding optimal and near-optimal solutions.
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Affiliation(s)
- Nicholas Roehner
- Department
of Bioengineering, University of Utah, Salt Lake City 84112, United States
| | - Chris J. Myers
- Department
of Electrical and Computer Engineering, University of Utah, Salt Lake
City 84112, United States
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Abstract
Recently, we have begun to witness the potential of synthetic biology, noted here in the form of bacteria and yeast that have been genetically engineered to produce biofuels, manufacture drug precursors, and even invade tumor cells. The success of these projects, however, has often failed in translation and application to new projects, a problem exacerbated by a lack of engineering standards that combine descriptions of the structure and function of DNA. To address this need, this paper describes a methodology to connect the systems biology markup language (SBML) to the synthetic biology open language (SBOL), existing standards that describe biochemical models and DNA components, respectively. Our methodology involves first annotating SBML model elements such as species and reactions with SBOL DNA components. A graph is then constructed from the model, with vertices corresponding to elements within the model and edges corresponding to the cause-and-effect relationships between these elements. Lastly, the graph is traversed to assemble the annotating DNA components into a composite DNA component, which is used to annotate the model itself and can be referenced by other composite models and DNA components. In this way, our methodology can be used to build up a hierarchical library of models annotated with DNA components. Such a library is a useful input to any future genetic technology mapping algorithm that would automate the process of composing DNA components to satisfy a behavioral specification. Our methodology for SBML-to-SBOL annotation is implemented in the latest version of our genetic design automation (GDA) software tool, iBioSim.
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Affiliation(s)
- Nicholas Roehner
- Department of Bioengineering, University of Utah, Salt Lake
City, Utah 84112, United States
| | - Chris J. Myers
- Department of Electrical
and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
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Madsen C, Myers CJ, Patterson T, Roehner N, Stevens JT, Winstead C. 06 2012 C1 C1 6327710 10.1109/MDT.2012.2211731 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6327710 ${\tt iBioSim}$iBioSim. ACTA ACUST UNITED AC 2012. [DOI: 10.1109/mdt.2012.2187875] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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