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Ma Y, Zhang Z, Jia B, Yuan Y. Automated high-throughput DNA synthesis and assembly. Heliyon 2024; 10:e26967. [PMID: 38500977 PMCID: PMC10945133 DOI: 10.1016/j.heliyon.2024.e26967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/20/2024] Open
Abstract
DNA synthesis and assembly primarily revolve around the innovation and refinement of tools that facilitate the creation of specific genes and the manipulation of entire genomes. This multifaceted process encompasses two fundamental steps: the synthesis of lengthy oligonucleotides and the seamless assembly of numerous DNA fragments. With the advent of automated pipetting workstations and integrated experimental equipment, a substantial portion of repetitive tasks in the field of synthetic biology can now be efficiently accomplished through integrated liquid handling workstations. This not only reduces the need for manual labor but also enhances overall efficiency. This review explores the ongoing advancements in the oligonucleotide synthesis platform, automated DNA assembly techniques, and biofoundries. The development of accurate and high-throughput DNA synthesis and assembly technologies presents both challenges and opportunities.
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Affiliation(s)
- Yuxin Ma
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Zhaoyang Zhang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Bin Jia
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Yingjin Yuan
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
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2
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Buecherl L, Myers CJ. Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology. Curr Opin Microbiol 2022; 68:102155. [PMID: 35588683 DOI: 10.1016/j.mib.2022.102155] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 01/23/2023]
Abstract
Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.
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Affiliation(s)
- Lukas Buecherl
- Biomedical Engineering Program, University of Colorado Boulder, 1111 Engineering Drive, Boulder, 80309 CO, United States
| | - Chris J Myers
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, 425 UCB, Boulder, 80309 CO, United States.
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Mısırlı G, Yang B, James K, Wipat A. Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits. ACS Synth Biol 2021; 10:3304-3315. [PMID: 34762797 DOI: 10.1021/acssynbio.1c00157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits for which manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications.
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Affiliation(s)
- Göksel Mısırlı
- School of Computing and Mathematics, Keele University, Keele, ST5 5BG, U.K
| | - Bill Yang
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
| | - Katherine James
- Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, U.K
| | - Anil Wipat
- School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, U.K
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Szymanski E, Scher E. Models for DNA Design Tools: The Trouble with Metaphors Is That They Don't Go Away. ACS Synth Biol 2019; 8:2635-2641. [PMID: 31580653 DOI: 10.1021/acssynbio.9b00302] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Synthetic biology relies heavily on DNA design tools to enable manipulation of DNA in silico. Existing tools, however, are falling short of enabling aspirations for the field that emphasize efficient, automated design pipelines. We review existing DNA design tools, identify underlying similarities in how they model correlations between DNA structure and function, and suggest that iterating the existing model is unlikely to overcome limitations in matching software applications to design aspirations. The current model is predicated on metaphors conceptualizing DNA as linear text, accounting for relatively little of the known complexity of DNA function. New models that can account for more of that complexity and thus enable more ambitious DNA design goals are likely to call for new underlying metaphors-a need that may be addressed by rethinking DNA in terms of human rather than computer languages.
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Affiliation(s)
- Erika Szymanski
- Colorado State University, Department of English, Fort Collins, Colorado 80523, United States
| | - Emily Scher
- Informatics Forum, School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
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Urquiza-García U, Zieliński T, Millar AJ. Better research by efficient sharing: evaluation of free management platforms for synthetic biology designs. Synth Biol (Oxf) 2019; 4:ysz016. [PMID: 31423466 PMCID: PMC6690502 DOI: 10.1093/synbio/ysz016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/25/2019] [Accepted: 05/29/2019] [Indexed: 12/25/2022] Open
Abstract
Synthetic biology aims to introduce engineering principles into biology, for example, the construction of biological devices by assembling previously-characterized, functional parts. This approach demands new resources for cataloging and sharing biological components and designs, in order to accelerate the design-build-test-learn cycle. We evaluated two free, open source software platforms for managing synthetic biology data: Joint Bioenergy Institute-Inventory of Composable Elements (JBEI-ICE) and SynBioHub. We analyzed the systems from the perspective of experimental biology research groups in academia, which seek to incorporate the repositories into their synthetic biology workflow. Here, we define the minimal requirements for a repository in this context and develop three usage scenarios, where we then examine the two platforms: (i) supporting the synthetic biology design-build-test-learn cycle, (ii) batch deposit of existing designs into the repository and (iii) discovery and reuse of designs from the repository. Our evaluation of JBEI-ICE and SynBioHub provides an insight into the current state of synthetic biology resources, might encourage their wider adoption and should guide future development to better meet the needs of this user group.
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Affiliation(s)
- Uriel Urquiza-García
- SynthSys and School of Biological Sciences, C. H. Waddington Building, University of Edinburgh, King's Buildings, Edinburgh, UK.,Institute for Molecular Plant Sciences, D. Rutherford Building, University of Edinburgh, King's Buildings, Edinburgh, UK
| | - Tomasz Zieliński
- SynthSys and School of Biological Sciences, C. H. Waddington Building, University of Edinburgh, King's Buildings, Edinburgh, UK
| | - Andrew J Millar
- SynthSys and School of Biological Sciences, C. H. Waddington Building, University of Edinburgh, King's Buildings, Edinburgh, UK
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Goodstadt MN, Marti-Renom MA. Communicating Genome Architecture: Biovisualization of the Genome, from Data Analysis and Hypothesis Generation to Communication and Learning. J Mol Biol 2018; 431:1071-1087. [PMID: 30419242 DOI: 10.1016/j.jmb.2018.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/29/2018] [Accepted: 11/01/2018] [Indexed: 01/07/2023]
Abstract
Genome discoveries at the core of biology are made by visual description and exploration of the cell, from microscopic sketches and biochemical mapping to computational analysis and spatial modeling. We outline the experimental and visualization techniques that have been developed recently which capture the three-dimensional interactions regulating how genes are expressed. We detail the challenges faced in integration of the data to portray the components and organization and their dynamic landscape. The goal is more than a single data-driven representation as interactive visualization for de novo research is paramount to decipher insights on genome organization in space.
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Affiliation(s)
- Mike N Goodstadt
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain; Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain.
| | - Marc A Marti-Renom
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain; Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, Barcelona 08010, Spain.
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Engineering Genomes with Genotype Specification Language. Methods Mol Biol 2018. [PMID: 29754240 DOI: 10.1007/978-1-4939-7795-6_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
High quality DNA design tools are becoming increasingly important as synthetic biology continues to increase the rate and throughput of building and testing genetic constructs. To make effective use of expanded build and test capacity, genotype design tools must not only be efficient enough to allow for many designs to be easily created, but also expressive enough to support the complex design patterns required by scientists on the frontier of genome engineering. Genotype Specification Language (GSL) is a language-based design tool invented at Amyris that enables scientists to quickly create DNA designs using a familiar syntax. This syntax provides a layer of abstraction that moves users away from reading and writing raw DNA sequences toward composing designs in terms of functional parts . GSL increases the speed at which scientists can design DNA constructs, provides a precise and reproducible representation of parts, and achieves these goals while maintaining design flexibility. Finally, the GSL compiler can emit information such as the exact final DNA sequence of the design as well as the reagents (primers and template information) required to physically build the constructs. Since its open-source release in February 2016, the GSL compiler can be freely downloaded and used by genome engineers to efficiently specify genetic designs. This chapter briefly introduces GSL syntax and design principles before examining specific examples of genome engineering tasks with accompanying GSL code.
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