1
|
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.
Collapse
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
| |
Collapse
|
2
|
Stephenson A, Lastra L, Nguyen B, Chen YJ, Nivala J, Ceze L, Strauss K. Physical Laboratory Automation in Synthetic Biology. ACS Synth Biol 2023; 12:3156-3169. [PMID: 37935025 DOI: 10.1021/acssynbio.3c00345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.
Collapse
Affiliation(s)
- Ashley Stephenson
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Microsoft Research, Redmond, Washington 98052, United States
| | - Lauren Lastra
- Microsoft Research, Redmond, Washington 98052, United States
| | - Bichlien Nguyen
- Microsoft Research, Redmond, Washington 98052, United States
| | - Yuan-Jyue Chen
- Microsoft Research, Redmond, Washington 98052, United States
| | - Jeff Nivala
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Luis Ceze
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Karin Strauss
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Microsoft Research, Redmond, Washington 98052, United States
| |
Collapse
|
3
|
Lux MW, Strychalski EA, Vora GJ. Advancing reproducibility can ease the 'hard truths' of synthetic biology. Synth Biol (Oxf) 2023; 8:ysad014. [PMID: 38022744 PMCID: PMC10640854 DOI: 10.1093/synbio/ysad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 07/26/2023] [Accepted: 10/04/2023] [Indexed: 12/01/2023] Open
Abstract
Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.
Collapse
Affiliation(s)
- Matthew W Lux
- Research & Operations Directorate, U.S. Army Combat Capabilities Development Command Chemical Biological Center, APG, MD 21010, USA
| | - Elizabeth A Strychalski
- Cellular Engineering Group, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Gary J Vora
- Center for Bio/Molecular Science & Engineering, U.S. Naval Research Laboratory, Washington, DC 20375, USA
| |
Collapse
|
4
|
Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
Collapse
Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin Yuan
- Frontiers 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.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| |
Collapse
|
5
|
Wierenga RP, Golas S, Ho W, Coley C, Esvelt KM. PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.10.547733. [PMID: 37502883 PMCID: PMC10369895 DOI: 10.1101/2023.07.10.547733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Liquid handling robots are often limited by proprietary programming interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides a universal set of commands and representations for deck layout and labware, enabling the control of diverse accessory devices. The interface is extensible and can work with any robot that manipulates liquids within a Cartesian coordinate system. We validated the system through unit tests and several application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation.
Collapse
Affiliation(s)
- Rick P. Wierenga
- Leiden University, Leiden, the Netherlands
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stefan Golas
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Wilson Ho
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Connor Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kevin M. Esvelt
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
6
|
Bryant JA, Kellinger M, Longmire C, Miller R, Wright RC. AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots. SYNTHETIC BIOLOGY (OXFORD, ENGLAND) 2022; 8:ysac032. [PMID: 36644757 PMCID: PMC9832943 DOI: 10.1093/synbio/ysac032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/25/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022]
Abstract
As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn (DBTL) approach to simultaneously learn how a function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize and algorithmically perform each step of the DBTL cycle. However, there are many fewer options for automating the build step. Build typically involves deoxyribonucleic acid (DNA) assembly, which remains manual, low throughput and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron, an open-source Python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies, beginning from fragment amplification. We show that AssemblyTron can perform polymerase chain reactions across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies (IVAs) with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent IVA also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs and wastes associated with synthetic biology, which, along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.
Collapse
Affiliation(s)
- John A Bryant
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Mason Kellinger
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Cameron Longmire
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Ryan Miller
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | | |
Collapse
|
7
|
Ko SC, Cho M, Lee HJ, Woo HM. Biofoundry Palette: Planning-Assistant Software for Liquid Handler-Based Experimentation and Operation in the Biofoundry Workflow. ACS Synth Biol 2022; 11:3538-3543. [PMID: 36173735 DOI: 10.1021/acssynbio.2c00390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Lab automation has facilitated synthetic biology applications in an automated workflow, and biofoundry facilities have enabled automated high-throughput experiments of gene cloning and genome engineering to be conducted following a precise experimental design and protocol. However, before-experiment procedures in biofoundry applications have been underdetermined. We aimed to develop a Python-based planning-assistant software, namely Biofoundry Palette, for liquid handler-based experimentation and operation in the biofoundry workflow. Depending on the synthetic biology project, variable information and content information may vary; the Biofoundry Palette provides precise information for the before-experiment units for each process module in the biofoundry workflow. As a demonstration, more than 200 unique information sets, generated by Biofoundry Palette, were used in automated gene cloning or pathway construction. The information on planning and management can potentially help the operator faithfully execute the biofoundry workflow after securing the before-experiment unit, thereby lowering the risk of human errors and performing successful biofoundry operations for synthetic biology applications.
Collapse
Affiliation(s)
- Sung Cheon Ko
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea.,Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Mingu Cho
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Hyun Jeong Lee
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea.,Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Han Min Woo
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea.,Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| |
Collapse
|
8
|
Malcı K, Watts E, Roberts TM, Auxillos JY, Nowrouzi B, Boll HO, Nascimento CZSD, Andreou A, Vegh P, Donovan S, Fragkoudis R, Panke S, Wallace E, Elfick A, Rios-Solis L. Standardization of Synthetic Biology Tools and Assembly Methods for Saccharomyces cerevisiae and Emerging Yeast Species. ACS Synth Biol 2022; 11:2527-2547. [PMID: 35939789 PMCID: PMC9396660 DOI: 10.1021/acssynbio.1c00442] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
![]()
As redesigning organisms using engineering principles
is one of
the purposes of synthetic biology (SynBio), the standardization of
experimental methods and DNA parts is becoming increasingly a necessity.
The synthetic biology community focusing on the engineering of Saccharomyces cerevisiae has been in the foreground in this
area, conceiving several well-characterized SynBio toolkits widely
adopted by the community. In this review, the molecular methods and
toolkits developed for S. cerevisiae are discussed
in terms of their contributions to the required standardization efforts.
In addition, the toolkits designed for emerging nonconventional yeast
species including Yarrowia lipolytica, Komagataella
phaffii, and Kluyveromyces marxianus are
also reviewed. Without a doubt, the characterized DNA parts combined
with the standardized assembly strategies highlighted in these toolkits
have greatly contributed to the rapid development of many metabolic
engineering and diagnostics applications among others. Despite the
growing capacity in deploying synthetic biology for common yeast genome
engineering works, the yeast community has a long journey to go to
exploit it in more sophisticated and delicate applications like bioautomation.
Collapse
Affiliation(s)
- Koray Malcı
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Kings Buildings, EH9 3BF Edinburgh, United Kingdom.,Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Kings Buildings, EH9 3BD Edinburgh, United Kingdom
| | - Emma Watts
- School of Biological Sciences, University of Edinburgh, Kings Buildings, EH9 3JW Edinburgh, United Kingdom
| | | | - Jamie Yam Auxillos
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Kings Buildings, EH9 3BD Edinburgh, United Kingdom.,Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Kings Buildings, EH9 3FF Edinburgh, United Kingdom
| | - Behnaz Nowrouzi
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Kings Buildings, EH9 3BF Edinburgh, United Kingdom.,Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Kings Buildings, EH9 3BD Edinburgh, United Kingdom
| | - Heloísa Oss Boll
- Department of Genetics and Morphology, Institute of Biological Sciences, University of Brasília, Brasília, Federal District 70910-900, Brazil
| | | | - Andreas Andreou
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Kings Buildings, EH9 3BD Edinburgh, United Kingdom
| | - Peter Vegh
- Edinburgh Genome Foundry, University of Edinburgh, Kings Buildings, Edinburgh EH9 3BF, United Kingdom
| | - Sophie Donovan
- Edinburgh Genome Foundry, University of Edinburgh, Kings Buildings, Edinburgh EH9 3BF, United Kingdom
| | - Rennos Fragkoudis
- Edinburgh Genome Foundry, University of Edinburgh, Kings Buildings, Edinburgh EH9 3BF, United Kingdom
| | - Sven Panke
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
| | - Edward Wallace
- Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Kings Buildings, EH9 3BD Edinburgh, United Kingdom.,Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Kings Buildings, EH9 3FF Edinburgh, United Kingdom
| | - Alistair Elfick
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Kings Buildings, EH9 3BF Edinburgh, United Kingdom.,Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Kings Buildings, EH9 3BD Edinburgh, United Kingdom
| | - Leonardo Rios-Solis
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Kings Buildings, EH9 3BF Edinburgh, United Kingdom.,Centre for Synthetic and Systems Biology (SynthSys), University of Edinburgh, Kings Buildings, EH9 3BD Edinburgh, United Kingdom.,School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom
| |
Collapse
|
9
|
Gurdo N, Volke DC, Nikel PI. Merging automation and fundamental discovery into the design–build–test–learn cycle of nontraditional microbes. Trends Biotechnol 2022; 40:1148-1159. [DOI: 10.1016/j.tibtech.2022.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/12/2022] [Accepted: 03/16/2022] [Indexed: 12/29/2022]
|
10
|
Sechkar K, A Tuza Z, Stan GB. A Linear Programming-Based Strategy to Save Pipette Tips in Automated DNA Assembly. Synth Biol (Oxf) 2022; 7:ysac004. [PMID: 35540864 PMCID: PMC9074407 DOI: 10.1093/synbio/ysac004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 02/05/2022] [Accepted: 03/18/2022] [Indexed: 11/22/2022] Open
Abstract
Laboratory automation and mathematical optimization are key to improving the efficiency of synthetic biology research. While there are algorithms optimizing the construct designs and synthesis strategies for DNA assembly, the optimization of how DNA assembly reaction mixes are prepared remains largely unexplored. Here, we focus on reducing the pipette tip consumption of a liquid-handling robot as it delivers DNA parts across a multi-well plate where several constructs are being assembled in parallel. We propose a linear programming formulation of this problem based on the capacitated vehicle routing problem, as well as an algorithm which applies a linear programming solver to our formulation, hence providing a strategy to prepare a given set of DNA assembly mixes using fewer pipette tips. The algorithm performed well in randomly generated and real-life scenarios concerning several modular DNA assembly standards, proving to be capable of reducing the pipette tip consumption by up to \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{upgreek}
\usepackage{mathrsfs}
\setlength{\oddsidemargin}{-69pt}
\begin{document}
}{}$59\%$\end{document} in large-scale cases. Combining automatic process optimization and robotic liquid handling, our strategy promises to greatly improve the efficiency of DNA assembly, either used alone or combined with other algorithmic DNA assembly optimization methods.
Graphical Abstract
Collapse
Affiliation(s)
- Kirill Sechkar
- Department of Bioengineering, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK
| | - Zoltan A Tuza
- Department of Bioengineering, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK
| |
Collapse
|
11
|
Luo Y, James JS, Jones S, Martella A, Cai Y. EMMA-CAD: Design Automation for Synthetic Mammalian Constructs. ACS Synth Biol 2022; 11:579-586. [PMID: 35050610 DOI: 10.1021/acssynbio.1c00433] [Citation(s) in RCA: 2] [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
Computational design tools are the cornerstone of synthetic biology and have underpinned its rapid development over the past two decades. As the field has matured, the scale of biological investigation has expanded dramatically, and researchers often must rely on computational tools to operate in the high-throughput investigational space. This is especially apparent in the modular design of DNA expression circuits, where complexity is accumulated rapidly. Alongside our automated pipeline for the high-throughput construction of Extensible Modular Mammalian Assembly (EMMA) expression vectors, we recognized the need for an integrated software solution for EMMA vector design. Here we present EMMA-CAD (https://emma.cailab.org), a powerful web-based computer-aided design tool for the rapid design of bespoke mammalian expression vectors. EMMA-CAD features a variety of functionalities, including a user-friendly design interface, automated connector selection underpinned by rigorous computer optimization algorithms, customization of part libraries, and personalized design spaces. Capable of translating vector assembly designs into human- and machine-readable protocols for vector construction, EMMA-CAD integrates seamlessly into our automated EMMA pipeline, hence completing an end-to-end design to production workflow.
Collapse
Affiliation(s)
- Yisha Luo
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - Joshua S. James
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore
| | - Sally Jones
- John Innes Centre, Norwich Research Park, Norwich, Norfolk NR4 7UH, U.K
| | - Andrea Martella
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Yizhi Cai
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| |
Collapse
|
12
|
James JS, Jones S, Martella A, Luo Y, Fisher DI, Cai Y. Automation and Expansion of EMMA Assembly for Fast-Tracking Mammalian System Engineering. ACS Synth Biol 2022; 11:587-595. [PMID: 35061373 DOI: 10.1021/acssynbio.1c00330] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
With applications from functional genomics to the production of therapeutic biologics, libraries of mammalian expression vectors have become a cornerstone of modern biological investigation and engineering. Multiple modular vector platforms facilitate the rapid design and assembly of vectors. However, such systems approach a technical bottleneck when a library of bespoke vectors is required. Utilizing the flexibility and robustness of the Extensible Mammalian Modular Assembly (EMMA) toolkit, we present an automated workflow for the library-scale design, assembly, and verification of mammalian expression vectors. Vector design is simplified using our EMMA computer-aided design tool (EMMA-CAD), while the precision and speed of acoustic droplet ejection technology are applied in vector assembly. Our pipeline facilitates significant reductions in both reagent usage and researcher hands-on time compared with manual assembly, as shown by system Q-metrics. To demonstrate automated EMMA performance, we compiled a library of 48 distinct plasmid vectors encoding either CRISPR interference or activation modalities. Characterization of the workflow parameters shows that high assembly efficiency is maintained across vectors of various sizes and design complexities. Our system also performs strongly compared with manual assembly efficiency benchmarks. Alongside our automated pipeline, we present a straightforward strategy for integrating gRNA and Cas modules into the EMMA platform, enabling the design and manufacture of valuable genome editing resources.
Collapse
Affiliation(s)
- Joshua S James
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore
| | - Sally Jones
- John Innes Centre, Norwich Research Park, Norwich, Norfolk NR4 7UH, U.K
| | - Andrea Martella
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Yisha Luo
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - David I Fisher
- Discovery Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Yizhi Cai
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| |
Collapse
|
13
|
Automated liquid handling robot for rapid lateral flow assay development. Anal Bioanal Chem 2022; 414:2607-2618. [PMID: 35091761 PMCID: PMC8799445 DOI: 10.1007/s00216-022-03897-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/21/2021] [Accepted: 01/11/2022] [Indexed: 11/01/2022]
Abstract
AbstractThe lateral flow assay (LFA) is one of the most popular technologies on the point-of-care diagnostics market due to its low cost and ease of use, with applications ranging from pregnancy to environmental toxins to infectious disease. While the use of these tests is relatively straightforward, significant development time and effort are required to create tests that are both sensitive and specific. Workflows to guide the LFA development process exist but moving from target selection to an LFA that is ready for field testing can be labor intensive, resource heavy, and time consuming. To reduce the cost and the duration of the LFA development process, we introduce a novel development platform centered on the flexibility, speed, and throughput of an automated robotic liquid handling system. The system comprises LFA-specific hardware and software that enable large optimization experiments with discrete and continuous variables such as antibody pair selection or reagent concentration. Initial validation of the platform was demonstrated during development of a malaria LFA but was readily expanded to encompass development of SARS-CoV-2 and Mycobacterium tuberculosis LFAs. The validity of the platform, where optimization experiments are run directly on LFAs rather than in solution, was based on a direct comparison between the robotic system and a more traditional ELISA-like method. By minimizing hands-on time, maximizing experiment size, and enabling improved reproducibility, the robotic system improved the quality and quantity of LFA assay development efforts.
Graphical abstract
Collapse
|
14
|
Liu Z, Wang J, Nielsen J. Yeast synthetic biology advances biofuel production. Curr Opin Microbiol 2021; 65:33-39. [PMID: 34739924 DOI: 10.1016/j.mib.2021.10.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/10/2021] [Accepted: 10/11/2021] [Indexed: 01/24/2023]
Abstract
Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production.
Collapse
Affiliation(s)
- Zihe Liu
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Junyang Wang
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Jens Nielsen
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China; Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen N, Denmark.
| |
Collapse
|
15
|
Perry JM, Soffer G, Jain R, Shih SCC. Expanding the limits towards 'one-pot' DNA assembly and transformation on a rapid-prototype microfluidic device. LAB ON A CHIP 2021; 21:3730-3741. [PMID: 34369550 DOI: 10.1039/d1lc00415h] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
DNA assembly and transformation are crucial to the building process in synthetic biology. These steps are significant roadblocks when engineering increasingly complex biological systems. To address this, recent development of widespread 'biofoundry' facilities has employed automation equipment to expedite the synthetic biology workflow. Despite significant progress, there is a clear demand for lower-cost and smaller-footprint automation equipment. The field of microfluidics have emerged to provide automation capabilities to meet this demand. However, we still lack devices capable of building large multi-gene systems in a consolidated process. In response to this challenge, we have developed a digital microfluidic platform that performs "one-pot" Golden Gate DNA assembly of large plasmids and transformation of E coli. The system features a novel electrode geometry and modular design, which make these devices simple to fabricate and use, thus improving the accessibility of microfluidics. This device incorporates an impedance-based adaptive closed loop water replenishment system to compensate for droplet evaporation and maintain constant assembly reaction concentrations, which we found to be crucial to the DNA assembly efficiency. We also showcase a closed-loop temperature control system that generates precise thermodynamic profiles to optimize heat shock transformation. Moreover, we validated the system by assembling and transforming large and complex plasmids conferring a biosynthetic pathway, resulting in performance comparable to those of standard techniques. We propose that the methods described here will contribute to a new generation of accessible automation platforms aimed at speeding up the 'building' process, lowering reagent consumption and removing manual work from synthetic biology.
Collapse
Affiliation(s)
- James M Perry
- Department of Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada.
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada
| | - Guy Soffer
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada
- Centre for Applied Synthetic Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| | - Raja Jain
- Department of Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada.
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada
| | - Steve C C Shih
- Department of Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada.
- Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada
- Centre for Applied Synthetic Biology, Concordia University, 7141 Sherbrooke Street West, Montréal, Québec, H4B 1R6, Canada
| |
Collapse
|
16
|
Prototyping of microbial chassis for the biomanufacturing of high-value chemical targets. Biochem Soc Trans 2021; 49:1055-1063. [PMID: 34100907 DOI: 10.1042/bst20200017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/06/2021] [Accepted: 05/10/2021] [Indexed: 12/19/2022]
Abstract
Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design-Build-Test-Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.
Collapse
|
17
|
Chory EJ, Gretton DW, DeBenedictis EA, Esvelt KM. Enabling high-throughput biology with flexible open-source automation. Mol Syst Biol 2021; 17:e9942. [PMID: 33764680 PMCID: PMC7993322 DOI: 10.15252/msb.20209942] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 02/03/2021] [Accepted: 02/05/2021] [Indexed: 12/19/2022] Open
Abstract
Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand-pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open-source Python platform that can execute complex pipetting patterns required for custom high-throughput experiments such as the simulation of metapopulation dynamics. With an integrated plate reader, we maintain nearly 500 remotely monitored bacterial cultures in log-phase growth for days without user intervention by taking regular density measurements to adjust the robotic method in real-time. Using these capabilities, we systematically optimize bioreactor protein production by monitoring the fluorescent protein expression and growth rates of a hundred different continuous culture conditions in triplicate to comprehensively sample the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate that flexible software can empower existing hardware to enable new types and scales of experiments, empowering areas from biomanufacturing to fundamental biology.
Collapse
Affiliation(s)
- Emma J Chory
- Media LaboratoryMassachusetts Institute of TechnologyCambridgeMAUSA
- Institute for Medical Engineering and ScienceMassachusetts Institute of TechnologyCambridgeMAUSA
- Broad Institute of MIT and HarvardCambridgeMAUSA
| | - Dana W Gretton
- Media LaboratoryMassachusetts Institute of TechnologyCambridgeMAUSA
| | - Erika A DeBenedictis
- Media LaboratoryMassachusetts Institute of TechnologyCambridgeMAUSA
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMAUSA
| | - Kevin M Esvelt
- Media LaboratoryMassachusetts Institute of TechnologyCambridgeMAUSA
| |
Collapse
|
18
|
Yáñez Feliú G, Earle Gómez B, Codoceo Berrocal V, Muñoz Silva M, Nuñez IN, Matute TF, Arce Medina A, Vidal G, Vitalis C, Dahlin J, Federici F, Rudge TJ. Flapjack: Data Management and Analysis for Genetic Circuit Characterization. ACS Synth Biol 2021; 10:183-191. [PMID: 33382586 DOI: 10.1021/acssynbio.0c00554] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.
Collapse
Affiliation(s)
- Guillermo Yáñez Feliú
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Benjamín Earle Gómez
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Verner Codoceo Berrocal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Isaac N Nuñez
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Tamara F Matute
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Anibal Arce Medina
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Carlos Vitalis
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Jonathan Dahlin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Fernán Federici
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- FONDAP, Center for Genome Regulation, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Timothy J Rudge
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| |
Collapse
|
19
|
Setting Up an Automated Biomanufacturing Laboratory. Methods Mol Biol 2021; 2229:137-155. [PMID: 33405219 DOI: 10.1007/978-1-0716-1032-9_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Laboratory automation is a key enabling technology for genetic engineering that can lead to higher throughput, more efficient and accurate experiments, better data management and analysis, decrease in the DBT (Design, Build, and Test) cycle turnaround, increase of reproducibility, and savings in lab resources. Choosing the correct framework among so many options available in terms of software, hardware, and skills needed to operate them is crucial for the success of any automation project. This chapter explores the multiple aspects to be considered for the solid development of a biofoundry project including available software and hardware tools, resources, strategies, partnerships, and collaborations in the field needed to speed up the translation of research results to solve important society problems.
Collapse
|
20
|
Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
Collapse
Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
| |
Collapse
|
21
|
Kumar P, Sinha R, Shukla P. Artificial intelligence and synthetic biology approaches for human gut microbiome. Crit Rev Food Sci Nutr 2020; 62:2103-2121. [PMID: 33249867 DOI: 10.1080/10408398.2020.1850415] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The gut microbiome comprises a variety of microorganisms whose genes encode proteins to carry out crucial metabolic functions that are responsible for the majority of health-related issues in human beings. The advent of the technological revolution in artificial intelligence (AI) assisted synthetic biology (SB) approaches will play a vital role in the modulating the therapeutic and nutritive potential of probiotics. This can turn human gut as a reservoir of beneficial bacterial colonies having an immense role in immunity, digestion, brain function, and other health benefits. Hence, in the present review, we have discussed the role of several gene editing tools and approaches in synthetic biology that have equipped us with novel tools like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR-Cas) systems to precisely engineer probiotics for diagnostic, therapeutic and nutritive value. A brief discussion over the AI techniques to understand the metagenomic data from the healthy and diseased gut microbiome is also presented. Further, the role of AI in potentially impacting the pace of developments in SB and its current challenges is also discussed. The review also describes the health benefits conferred by engineered microbes through the production of biochemicals, nutraceuticals, drugs or biotherapeutics molecules etc. Finally, the review concludes with the challenges and regulatory concerns in adopting synthetic biology engineered microbes for clinical applications. Thus, the review presents a synergistic approach of AI and SB toward human gut microbiome for better health which will provide interesting clues to researchers working in the area of rapidly evolving food and nutrition science.
Collapse
Affiliation(s)
- Prasoon Kumar
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, India.,Department of Medical Devices, National Institute of Pharmaceutical Education and Research, Ahmedabad, India
| | | | - Pratyoosh Shukla
- School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, India.,Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
| |
Collapse
|
22
|
Karim AS, Liew F(E, Garg S, Vögeli B, Rasor BJ, Gonnot A, Pavan M, Juminaga A, Simpson SD, Köpke M, Jewett MC. Modular cell-free expression plasmids to accelerate biological design in cells. Synth Biol (Oxf) 2020; 5:ysaa019. [PMID: 33344777 PMCID: PMC7737004 DOI: 10.1093/synbio/ysaa019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/18/2020] [Accepted: 09/22/2020] [Indexed: 01/28/2023] Open
Abstract
Industrial biotechnology aims to produce high-value products from renewable resources. This can be challenging because model microorganisms-organisms that are easy to use like Escherichia coli-often lack the machinery required to utilize desired feedstocks like lignocellulosic biomass or syngas. Non-model organisms, such as Clostridium, are industrially proven and have desirable metabolic features but have several hurdles to mainstream use. Namely, these species grow more slowly than conventional laboratory microbes, and genetic tools for engineering them are far less prevalent. To address these hurdles for accelerating cellular design, cell-free synthetic biology has matured as an approach for characterizing non-model organisms and rapidly testing metabolic pathways in vitro. Unfortunately, cell-free systems can require specialized DNA architectures with minimal regulation that are not compatible with cellular expression. In this work, we develop a modular vector system that allows for T7 expression of desired enzymes for cell-free expression and direct Golden Gate assembly into Clostridium expression vectors. Utilizing the Joint Genome Institute's DNA Synthesis Community Science Program, we designed and synthesized these plasmids and genes required for our projects allowing us to shuttle DNA easily between our in vitro and in vivo experiments. We next validated that these vectors were sufficient for cell-free expression of functional enzymes, performing on par with the previous state-of-the-art. Lastly, we demonstrated automated six-part DNA assemblies for Clostridium autoethanogenum expression with efficiencies ranging from 68% to 90%. We anticipate this system of plasmids will enable a framework for facile testing of biosynthetic pathways in vitro and in vivo by shortening development cycles.
Collapse
Affiliation(s)
- Ashty S Karim
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
| | | | | | - Bastian Vögeli
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
| | - Blake J Rasor
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
| | | | | | | | | | | | - Michael C Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
- Simpson Querrey Institute, Northwestern University, Chicago, IL 60611, USA
| |
Collapse
|
23
|
Storch M, Haines MC, Baldwin GS. DNA-BOT: a low-cost, automated DNA assembly platform for synthetic biology. Synth Biol (Oxf) 2020; 5:ysaa010. [PMID: 32995552 PMCID: PMC7476404 DOI: 10.1093/synbio/ysaa010] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/05/2020] [Accepted: 06/26/2020] [Indexed: 01/10/2023] Open
Abstract
Multi-part DNA assembly is the physical starting point for many projects in Synthetic and Molecular Biology. The ability to explore a genetic design space by building extensive libraries of DNA constructs is essential for creating programmed biological systems. With multiple DNA assembly methods and standards adopted in the Synthetic Biology community, automation of the DNA assembly process is now receiving serious attention. Automation will enable larger builds using less researcher time, while increasing the accessible design space. However, these benefits currently incur high costs for both equipment and consumables. Here, we address this limitation by introducing low-cost DNA assembly with BASIC on OpenTrons (DNA-BOT). For this purpose, we developed an open-source software package and demonstrated the performance of DNA-BOT by simultaneously assembling 88 constructs composed of 10 genetic parts, evaluating the promoter, ribosome binding site and gene order design space for a three-gene operon. All 88 constructs were assembled with high accuracy, at a consumables cost of $1.50–$5.50 per construct. This illustrates the efficiency, accuracy and affordability of DNA-BOT, making it accessible for most labs and democratizing automated DNA assembly.
Collapse
Affiliation(s)
- Marko Storch
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.,Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK.,London Biofoundry, Imperial College Translation & Innovation Hub, London, W12 0BZ, UK
| | - Matthew C Haines
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.,Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
| | - Geoff S Baldwin
- Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.,Imperial College Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
| |
Collapse
|
24
|
Kelwick RJR, Webb AJ, Freemont PS. Biological Materials: The Next Frontier for Cell-Free Synthetic Biology. Front Bioeng Biotechnol 2020; 8:399. [PMID: 32478045 PMCID: PMC7235315 DOI: 10.3389/fbioe.2020.00399] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 04/08/2020] [Indexed: 12/13/2022] Open
Abstract
Advancements in cell-free synthetic biology are enabling innovations in sustainable biomanufacturing, that may ultimately shift the global manufacturing paradigm toward localized and ecologically harmonized production processes. Cell-free synthetic biology strategies have been developed for the bioproduction of fine chemicals, biofuels and biological materials. Cell-free workflows typically utilize combinations of purified enzymes, cell extracts for biotransformation or cell-free protein synthesis reactions, to assemble and characterize biosynthetic pathways. Importantly, cell-free reactions can combine the advantages of chemical engineering with metabolic engineering, through the direct addition of co-factors, substrates and chemicals -including those that are cytotoxic. Cell-free synthetic biology is also amenable to automatable design cycles through which an array of biological materials and their underpinning biosynthetic pathways can be tested and optimized in parallel. Whilst challenges still remain, recent convergences between the materials sciences and these advancements in cell-free synthetic biology enable new frontiers for materials research.
Collapse
Affiliation(s)
- Richard J. R. Kelwick
- Section of Structural and Synthetic Biology, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Alexander J. Webb
- Section of Structural and Synthetic Biology, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Paul S. Freemont
- Section of Structural and Synthetic Biology, Department of Infectious Disease, Imperial College London, London, United Kingdom
- The London Biofoundry, Imperial College Translation & Innovation Hub, London, United Kingdom
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London, London, United Kingdom
| |
Collapse
|
25
|
Snyder JE, Walsh D, Carr PA, Rothschild LJ. A Makerspace for Life Support Systems in Space. Trends Biotechnol 2019; 37:1164-1174. [DOI: 10.1016/j.tibtech.2019.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 12/12/2022]
|