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Genetic Constructor: An Online DNA Design Platform

View Author Information
Autodesk Life Sciences, San Francisco, California 94111, United States
Edinburgh Genome Foundry, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, U.K.
§ Radiant Genomics, Emeryville, California 94608, United States
Cite this: ACS Synth. Biol. 2017, 6, 12, 2362–2365
Publication Date (Web):October 11, 2017
https://doi.org/10.1021/acssynbio.7b00236

Copyright © 2017 American Chemical Society. This publication is licensed under these Terms of Use.

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Supporting Info (1)»

Abstract

Genetic Constructor is a cloud Computer Aided Design (CAD) application developed to support synthetic biologists from design intent through DNA fabrication and experiment iteration. The platform allows users to design, manage, and navigate complex DNA constructs and libraries, using a new visual language that focuses on functional parts abstracted from sequence. Features like combinatorial libraries and automated primer design allow the user to separate design from construction by focusing on functional intent, and design constraints aid iterative refinement of designs. A plugin architecture enables contributions from scientists and coders to leverage existing powerful software and connect to DNA foundries. The software is easily accessible and platform agnostic, free for academics, and available in an open-source community edition. Genetic Constructor seeks to democratize DNA design, manufacture, and access to tools and services from the synthetic biology community.

Synthetic biologists unite the principles of engineering with traditional molecular and cell biology, (1) and have developed a powerful array of tools to inform and enable design of pathways. (2-5) The growing capacity and fidelity of DNA synthesis platforms and cloud foundries allows for increasingly intricate designs in biological engineering. These projects may encompass large spans of DNA sequence, introduce several complex, novel constructs, and test entire libraries simultaneously, instead of single sequences. However, it can be difficult to compose existing tools to devise cellular function, and the use of several unconnected tools through the design process is inefficient, and hinders reproducibility and record keeping.
Existing sequence editing tools, such as ApE, Benchling, Vector NTI, Geneious, and Genome Compiler, (6-10) handle nucleotide level design and optimization gracefully, but their emphasis on linear and nucleotide-level representations proves unwieldy in projects that require abstraction of biological complexity. Especially when outlining a complex project, focus on sequence hampers concentration on function, centralization of design and experimental intent, and the encapsulation and reuse of parts. Conversely, many design specification tools and languages, like the Genotype Specification Language (GSL) (3) and Eugene, (2) present text-driven interfaces with limited visual feedback, which are difficult for some researchers to access. Tools focused on part composition, like GenoCAD (4) and Teselagen, (5) use restrictive grammars to formalize manufacture-driven principles. Cello supports combinatorial design, but focuses on biological circuits. (11) j5 and Device Editor support functional design paradigms, but across separate tools and do not deeply support extension or ordering integration. (12)
Genetic Constructor aims to ease the design process for Synthetic Biology by allowing a biologist to smoothly navigate a common workflow, from concept to manufactured DNA, in a centralized and extensible platform. Namely, the software works to disentangle design from the restrictions of manufacturing, while still integrating with assembly foundries and leveraging new DNA assembly methods from the community. (13) Constructor frees researchers to focus on functional design by beginning with free-form drafting, and eases the transition through DNA construction by delegating some workflows to algorithms, like codon optimization, and allowing for the functional expression of others, like automated assembly primer design.

The Genetic Constructor Application

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Genetic Constructor builds on thought leadership of community standards like SBOL, (14) advancing design and fabrication paradigms scalable to devising and building large numbers of complex assemblies. User experience design and ongoing user research guided development of the application interface, in which users advance through a process of outlining a design intent, adding constraints, specifying sequence, manufacturing DNA, and progressive iteration. All work history is saved, allowing users to access prior versions of their work, and can be published publicly, or shared privately among users.
A “sketching” feature provides a digital medium for initial drafting, allowing genetic designers to outline functional representations using glyphs adapted from SBOL Visual, (15) alongside other metadata (Figure 1.1). Sketches are hierarchical, composed of nonoverlapping parts called “blocks,” and can be progressively annotated and defined with sequence to ultimately yield DNA specifications.

Figure 1

Figure 1. Design process. Users begin by (1) sketching a construct using “blocks”, (2) defining template rules and specifying lists of parts in combinatorial designs, using “list blocks”, (3) using hierarchy to add specific genes and sequences, yielding complete specifications that can be ordered (4). These specifications can be ordered from DNA foundries, potentially limited to subsets of combinatorial space. Designs are refined during experimental iteration.

Combinatorial design, visually captured using “list blocks” (Figure 1.2), and design constraints assist in addressing increased complexity. For example, users may define positional or sequence constraints or prevent subsets of changes. Many of these rules may be applied to parts independent of sequence, and can be composed to define reusable templates. Constraints can be honed as a design’s logic is guided by experiment.
DNA sequences can be added manually, or imported from local inventories, public repositories like NCBI or iGEM, genome analysis tools, (16) or exposed foundry inventories (Figure 1.3). Files in common formats, like Genbank, from these sources can be imported and are automatically converted into hierarchical constructs. Alternatively, sequences may be derived from algorithms integrated through Genetic Constructor’s plugin architecture (Figure 2.1). These algorithms can simplify the design concerns of synthesis and assembly, which are influenced by evolving manufacture processes, so that drafting functional intent is dissociated from the generation of fragments for assembly.

Figure 2

Figure 2. Extensions. (1) Schema of the Genetic Constructor architecture depicting examples of design (specifically GSL) and build plugins. The core application provides the base for many classes of plugins. The GSL application (left) is a design plugin adding a command line editor connected to a compiler. Foundry integrations (right), such as EGF, can create custom inventories, provide design templates and execute design checks for live ordering. (2) Application interface relevant to the GSL extension. On the top is the visually rich canvas for creating and exploring designs; at the bottom an IDE to define and execute GSL.

Extensions

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An integral plugin framework allows Genetic Constructor to package existing specialized software and bespoke industrial tools. This architecture aims to support an ecosystem of connected software, averting users from laboriously chaperoning data across different applications. Extensions are supported on both the web client and server and may manifest as visual plugins (e.g., plasmid viewer), as algorithms (codon optimization), as connections to manufacture (foundries, assembly methods), or as adapters to other tools and services (structure prediction, custom data pipelines).
One such extension, developed in conjunction with Amyris Inc., links the text-based GSL (3) to Genetic Constructor’s design canvas in an integrated development environment (IDE) (Figure 2.1), providing visual feedback from code through compilation (Figure 2.2).

Integration with Foundries

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Genetic Constructor simplifies access to industry scale DNA synthesis and assembly, broadening their access to scientists in industry and academia, illustrated by a connection to the Edinburgh Genome Foundry (Figures 1.4 and 2.1). Genetic Constructor is designed to integrate with foundries and DNA synthesis providers, sending a bill of materials and referring to parts rather than just a single sequence, and providing API hooks interact at key points in the progression from design to product.
Genetic Constructor was used to generate a combination of 8 constructs from a genetic template (Figure S1) using the modular assembly kit EMMA, (17) and ordered from the Edinburgh Genome Foundry. Upon ordering, the designs are sent as a list of parts identifiers to the Foundry’s ordering interface, where they are validated before the order enters the Foundry’s production pipeline. The designs were assembled using a highly automated robotics chain and in-house decision-making software, resulting in a seamless design-to-manufacture process with minimal human intervention (see Supporting Information for a detailed description of the workflow).

Architecture/Technical

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Genetic Constructor is a platform-independent web application written in JavaScript and Python. An open-source community edition with core functionality but lacking certain features, like primer design or molecular visualization, is available on GitHub. (18) Docker (19) containerization is used for deployment, so the software is easy to acquire, install, and scale. Genetic Constructor is hosted by Autodesk online, (20) or may be downloaded and run on a local machine for development and extension. Extensions take the form of npm (21) packages and can extend functionality of both the web client and server. REST APIs allow external access to application data and functionality (Figure 2.1), including APIs from extensions. Data models are serialized to JSON and heavily influenced by (though independent of) SBOL 2.0 (14) (Figure S4). Authentication and user management are managed by Autodesk separate from the application, and can easily be substituted or extended in local builds.

Future

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Future work will include more deeply and broadly integrating external services and software, thereby closing the design, build, test and learn cycle. We plan to add additional features for sequence annotation, optimization, and fabrication preparation. Concentrating on the transition from design to manufacture will require greater algorithmic control over designs. Deeper integration with foundries requires more granularity, capturing steps including performing automated design checks, receiving real-time feedback, algorithmically refining sequence, and providing the user with immediate order confirmation. Finally, incorporating learning from experimental data will allow the software to better inform subsequent design refinements.

Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssynbio.7b00236.

  • Details of EMMA toolkit demonstration (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
  • Authors
    • Maxwell Bates - Autodesk Life Sciences, San Francisco, California 94111, United States
    • Joe Lachoff - Autodesk Life Sciences, San Francisco, California 94111, United States
    • Duncan Meech - Autodesk Life Sciences, San Francisco, California 94111, United States
    • Valentin Zulkower - Edinburgh Genome Foundry, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, U.K.
    • Anaïs Moisy - Edinburgh Genome Foundry, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, U.K.
    • Yisha Luo - Edinburgh Genome Foundry, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, U.K.
    • Hille Tekotte - Edinburgh Genome Foundry, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, U.K.
    • Cornelia Johanna Franziska Scheitz - Autodesk Life Sciences, San Francisco, California 94111, United States
    • Rupal Khilari - Autodesk Life Sciences, San Francisco, California 94111, United States
    • Florencio Mazzoldi - Autodesk Life Sciences, San Francisco, California 94111, United States
    • Deepak Chandran - Radiant Genomics, Emeryville, California 94608, United States
  • Notes
    The authors declare the following competing financial interest(s): The corresponding author and several current or former Autodesk employees who are co-authors own Autodesk stock.

Acknowledgment

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Our thanks to Autodesk Life Sciences for funding this work and Amyris for their collaboration on GSL. The EGF Foundry team is supported by Research Councils’ UK Synthetic Biology for Growth Programme (BBSRC grants BB/M025659/1, BB/M025640/1, and BB/M00029X/1 to YC). EGF Template library based on research of Andrea Marcela (EGF). Darren Platt (Amyris), the author of the GSL language, assisted in its integration.

Abbreviations

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API

Application Programming Interface

CAD

Computer Aided Design

EMMA

Extensible Mammalian Modular Assembly Toolkit

GSL

Genotype Specification Language

IDE

Integrated Development Environment

SBOL

Synthetic Biology Open Language

References

ARTICLE SECTIONS
Jump To

This article references 21 other publications.

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    Brophy, J. A. N. and Voigt, C. A. (2014) Principles of genetic circuit design Nat. Methods 11, 508 520 DOI: 10.1038/nmeth.2926
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    Wilson, E. H., Sagawa, S., Weis, J. W., Schubert, M. G., Bissell, M., Hawthorne, B., Reeves, C. D., Dean, J., and Platt, D. (2016) Genotype Specification Language ACS Synth. Biol. 5, 471 DOI: 10.1021/acssynbio.5b00194
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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, J. H., Myers, C., Wipat, A., and Sauro, H. (2015) Synthetic Biology Open Language (SBOL) Version 2.0.0 J. Integr. Bioinform. 12, 272
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Cited By

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This article is cited by 9 publications.

  1. Göksel Mısırlı, Bill Yang, Katherine James, Anil Wipat. Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits. ACS Synthetic Biology 2021, 10 (12) , 3304-3315. https://doi.org/10.1021/acssynbio.1c00157
  2. Erika Szymanski, Emily Scher. Models for DNA Design Tools: The Trouble with Metaphors Is That They Don’t Go Away. ACS Synthetic Biology 2019, 8 (12) , 2635-2641. https://doi.org/10.1021/acssynbio.9b00302
  3. Yuxin Ma, Zhaoyang Zhang, Bin Jia, Yingjin Yuan. Automated high-throughput DNA synthesis and assembly. Heliyon 2024, 10 (6) , e26967. https://doi.org/10.1016/j.heliyon.2024.e26967
  4. Sai Bhavani Gottumukkala, Pushkar Malakar, Anbumathi Palanisamy. Role of synthetic biology to build a sustainable vaccine industry. 2024, 363-388. https://doi.org/10.1016/B978-0-443-15378-5.00020-6
  5. Lukas Buecherl, Chris J Myers. Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology. Current Opinion in Microbiology 2022, 68 , 102155. https://doi.org/10.1016/j.mib.2022.102155
  6. Jean-Christophe Lachance, Sébastien Rodrigue, Bernhard O. Palsson. The Use of In Silico Genome-Scale Models for the Rational Design of Minimal Cells. 2020, 141-175. https://doi.org/10.1007/978-3-030-31897-0_6
  7. Mike N. Goodstadt, Marc A. Marti-Renom. Communicating Genome Architecture: Biovisualization of the Genome, from Data Analysis and Hypothesis Generation to Communication and Learning. Journal of Molecular Biology 2019, 431 (6) , 1071-1087. https://doi.org/10.1016/j.jmb.2018.11.008
  8. Uriel Urquiza-García, Tomasz Zieliński, Andrew J Millar. Better research by efficient sharing: evaluation of free management platforms for synthetic biology designs. Synthetic Biology 2019, 4 (1) https://doi.org/10.1093/synbio/ysz016
  9. Erin H. Wilson, Chris Macklin, Darren Platt. Engineering Genomes with Genotype Specification Language. 2018, 373-398. https://doi.org/10.1007/978-1-4939-7795-6_21
  • Abstract

    Figure 1

    Figure 1. Design process. Users begin by (1) sketching a construct using “blocks”, (2) defining template rules and specifying lists of parts in combinatorial designs, using “list blocks”, (3) using hierarchy to add specific genes and sequences, yielding complete specifications that can be ordered (4). These specifications can be ordered from DNA foundries, potentially limited to subsets of combinatorial space. Designs are refined during experimental iteration.

    Figure 2

    Figure 2. Extensions. (1) Schema of the Genetic Constructor architecture depicting examples of design (specifically GSL) and build plugins. The core application provides the base for many classes of plugins. The GSL application (left) is a design plugin adding a command line editor connected to a compiler. Foundry integrations (right), such as EGF, can create custom inventories, provide design templates and execute design checks for live ordering. (2) Application interface relevant to the GSL extension. On the top is the visually rich canvas for creating and exploring designs; at the bottom an IDE to define and execute GSL.

  • References

    ARTICLE SECTIONS
    Jump To

    This article references 21 other publications.

    1. 1
      Brophy, J. A. N. and Voigt, C. A. (2014) Principles of genetic circuit design Nat. Methods 11, 508 520 DOI: 10.1038/nmeth.2926
    2. 2
      Bilitchenko, L., Liu, A., Cheung, S., Weeding, E., Xia, B., Leguia, M., Anderson, J. C., and Densmore, D. (2011) Eugene - A domain specific language for specifying and constraining synthetic biological parts, devices, and systems PLoS One 6, e18882 DOI: 10.1371/journal.pone.0018882
    3. 3
      Wilson, E. H., Sagawa, S., Weis, J. W., Schubert, M. G., Bissell, M., Hawthorne, B., Reeves, C. D., Dean, J., and Platt, D. (2016) Genotype Specification Language ACS Synth. Biol. 5, 471 DOI: 10.1021/acssynbio.5b00194
    4. 4
      Czar, M. J., Cai, Y., and Peccoud, J. (2009) Writing DNA with genoCAD Nucleic Acids Res. 37, W40 7 DOI: 10.1093/nar/gkp361
    5. 5
      Teselagen (2017) https://www.teselagen.com/.
    6. 6
      ApE (2017) http://biologylabs.utah.edu/jorgensen/wayned/ape/.
    7. 7
      Benchling (2017) https://benchling.com/.
    8. 8
      Lu, G. and Moriyama, E. N. (2004) Vector NTI, a balanced all-in-one sequence analysis suite Briefings Bioinf. 5, 378 388 DOI: 10.1093/bib/5.4.378
    9. 9
      Kearse, M., Moir, R., Wilson, A., Stones-Havas, S., Cheung, M., Sturrock, S., Buxton, S., Cooper, A., Markowitz, S., Duran, C., Thierer, T., Ashton, B., Meintjes, P., and Drummond, A. (2012) Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data Bioinformatics 28, 1647 1649 DOI: 10.1093/bioinformatics/bts199
    10. 10
      Genome Compiler (2017) http://www.genomecompiler.com/.
    11. 11
      Nielsen, A. A., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., and Voigt, C. A. (2016) Genetic circuit design automation Science 352 (6281) aac7341 DOI: 10.1126/science.aac7341
    12. 12
      Hillson, N. J., Rosengarten, R. D., and Keasling, J. D. (2011) j5 DNA Assembly Design Automation Software ACS Synth. Biol. 1, 14 DOI: 10.1021/sb2000116
    13. 13
      Chao, R., Yuan, Y., and Zhao, H. (2015) Recent advances in DNA assembly technologies FEMS Yeast Res. DOI: 10.1111/1567-1364.12171
    14. 14
      Bartley, B., Beal, J., Clancy, K., Misirli, G., Roehner, N., Oberortner, E., Pocock, M., Bissell, M., Madsen, C., Nguyen, T., Zhang, Z., Gennari, J. H., Myers, C., Wipat, A., and Sauro, H. (2015) Synthetic Biology Open Language (SBOL) Version 2.0.0 J. Integr. Bioinform. 12, 272
    15. 15
      Quinn, J. Y., Cox, R. S., Adler, A., Beal, J., Bhatia, S., Cai, Y., Chen, J., Clancy, K., Galdzicki, M., Hillson, N. J., Le Novere, N., Maheshwari, A. J., McLaughlin, J. A., Myers, C. J., Umesh, P., Pocock, M., Rodriguez, C., Soldatova, L., Stan, G. B. V., Swainston, N., Wipat, A., and Sauro, H. M. (2015) SBOL Visual: A Graphical Language for Genetic Designs PLoS Biol. 13, e1002310 DOI: 10.1371/journal.pbio.1002310
    16. 16
      Weber, T., Blin, K., Duddela, S., Krug, D., Kim, H. U., Bruccoleri, R., Lee, S. Y., Fischbach, M. A., Müller, R., Wohlleben, W., Breitling, R., Takano, E., and Medema, M. H. (2015) antiSMASH 3.0-a comprehensive resource for the genome mining of biosynthetic gene clusters Nucleic Acids Res. 43, W237 43 DOI: 10.1093/nar/gkv437
    17. 17
      Martella, A., Matjusaitis, M., Auxillos, J., Pollard, S. M., and Cai, Y. (2017) EMMA: An Extensible Mammalian Modular Assembly Toolkit for the Rapid Design and Production of Diverse Expression Vectors ACS Synth. Biol. 6, 1380 DOI: 10.1021/acssynbio.7b00016
    18. 18
      Genetic Constructor Github Repository (2017) https://github.com/Autodesk/genetic-constructor-ce.
    19. 19
      Docker (2017) https://www.docker.com/.
    20. 20
      Genetic Constructor: Design and manufacture living things (2017) http://www.geneticconstructor.com.
    21. 21
      npm (2017) https://www.npmjs.com/.
  • Supporting Information

    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssynbio.7b00236.

    • Details of EMMA toolkit demonstration (PDF)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.