1
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Nava A, Fear AL, Lee N, Mellinger P, Lan G, McCauley J, Tan S, Kaplan N, Goyal G, Coates RC, Roberts J, Johnson Z, Hu R, Wu B, Ahn J, Kim WE, Wan Y, Yin K, Hillson N, Haushalter RW, Keasling JD. Automated Platform for the Plasmid Construction Process. ACS Synth Biol 2023; 12:3506-3513. [PMID: 37948662 PMCID: PMC10729297 DOI: 10.1021/acssynbio.3c00292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Indexed: 11/12/2023]
Abstract
There is a growing need for applications capable of handling large synthesis biology experiments. At the core of synthetic biology is the process of cloning and manipulating DNA as plasmids. Here, we report the development of an application named DNAda capable of writing automation instructions for any given DNA construct design generated by the J5 DNA assembly program. We also describe the automation pipeline and several useful features. The pipeline is particularly useful for the construction of combinatorial DNA assemblies. Furthermore, we demonstrate the platform by constructing a library of polyketide synthase parts, which includes 120 plasmids ranging in size from 7 to 14 kb from 4 to 7 DNA fragments.
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Affiliation(s)
- Alberto
A. Nava
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Anna Lisa Fear
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Namil Lee
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Peter Mellinger
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Guangxu Lan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Joshua McCauley
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Stephen Tan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Nurgul Kaplan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Garima Goyal
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - R. Cameron Coates
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Jacob Roberts
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Zahmiria Johnson
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Romina Hu
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Bryan Wu
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Jared Ahn
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Woojoo E. Kim
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Yao Wan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Kevin Yin
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Plant and Microbial Biology, University
of California, Berkeley, Berkeley, California 94720, United States
| | - Nathan Hillson
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Robert W. Haushalter
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Jay D. Keasling
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
- Center
for Synthetic Biochemistry, Shenzhen Institutes
for Advanced Technologies, Shenzhen 518055, P.R. China
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, Kemitorvet, Building 220, Kongens Lyngby 2800, Denmark
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2
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Anhel AM, Alejaldre L, Goñi-Moreno Á. The Laboratory Automation Protocol (LAP) Format and Repository: A Platform for Enhancing Workflow Efficiency in Synthetic Biology. ACS Synth Biol 2023; 12:3514-3520. [PMID: 37982688 PMCID: PMC7615385 DOI: 10.1021/acssynbio.3c00397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Laboratory automation deals with eliminating manual tasks in high-throughput protocols. It therefore plays a crucial role in allowing fast and reliable synthetic biology. However, implementing open-source automation solutions often demands experimental scientists to possess scripting skills, and even when they do, there is no standardized toolkit available for their use. To address this, we present the Laboratory Automation Protocol (LAP) Format and Repository. LAPs adhere to a standardized script-based format, enhancing end-user implementation and simplifying further development. With a modular design, LAPs can be seamlessly combined to create customized, target-specific workflows. Furthermore, all LAPs undergo experimental validation, ensuring their reliability. Detailed information is provided within each repository entry, allowing users to validate the LAPs in their own laboratory settings. We advocate for the adoption of the LAP Format and Repository as a community resource, which will continue to expand, improving the reliability and reproducibility of the automation processes.
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Affiliation(s)
- Ana-Mariya Anhel
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), 28223, Madrid, Spain
| | - Lorea Alejaldre
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), 28223, Madrid, Spain
| | - Ángel Goñi-Moreno
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), 28223, Madrid, Spain
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3
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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4
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Raj K, Venayak N, Diep P, Golla SA, Yakunin AF, Mahadevan R. Automation assisted anaerobic phenotyping for metabolic engineering. Microb Cell Fact 2021; 20:184. [PMID: 34556155 PMCID: PMC8461876 DOI: 10.1186/s12934-021-01675-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. RESULTS In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. CONCLUSIONS Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.
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Affiliation(s)
- Kaushik Raj
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, M5S 3E5 Canada
| | - Naveen Venayak
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, M5S 3E5 Canada
| | - Patrick Diep
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, M5S 3E5 Canada
| | - Sai Akhil Golla
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, M5S 3E5 Canada
| | - Alexander F. Yakunin
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, M5S 3E5 Canada
- School of Natural Sciences, Bangor University, Bangor, LL57 2DG UK
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, M5S 3E5 Canada
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, M5S 3G9 Canada
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5
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Prusokas A, Hawkins M, Nieduszynski CA, Retkute R. Effectiveness of glass beads for plating cell cultures. Phys Rev E 2021; 103:052410. [PMID: 34134194 DOI: 10.1103/physreve.103.052410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 04/23/2021] [Indexed: 11/07/2022]
Abstract
Cell plating, the spreading out of a liquid suspension of cells on a surface followed by colony growth, is a common laboratory procedure in microbiology. Despite this, the exact impact of its parameters on colony growth has not been extensively studied. A common protocol involves the shaking of glass beads within a Petri dish containing solid growth media. We investigated the effects of multiple parameters in this protocol: the number of beads, the shape of movement, and the number of movements. Standard suspensions of Escherichia coli were spread while varying these parameters to assess their impact on colony growth. Results were assessed by a variety of metrics: the number of colonies, the mean distance between closest colonies, and the variability and uniformity of their spatial distribution. Finally, we devised a mathematical model of shifting billiard to explain the heterogeneities in the observed spatial patterns. Exploring the parameters that affect the most fundamental techniques in microbiology allows us to better understand their function, giving us the ability to precisely control their outputs for our exact needs.
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Affiliation(s)
- Alidivinas Prusokas
- Plant and Microbial Sciences, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom and Department of Biology, University of York, Heslington, York YO10 5DD, United Kingdom
| | - Michelle Hawkins
- Department of Biology, University of York, Heslington, York YO10 5DD, United Kingdom
| | | | - Renata Retkute
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom
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6
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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.
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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.
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7
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Roch LM, Häse F, Kreisbeck C, Tamayo-Mendoza T, Yunker LPE, Hein JE, Aspuru-Guzik A. ChemOS: An orchestration software to democratize autonomous discovery. PLoS One 2020; 15:e0229862. [PMID: 32298284 PMCID: PMC7161969 DOI: 10.1371/journal.pone.0229862] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 02/16/2020] [Indexed: 01/25/2023] Open
Abstract
The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving laboratories have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solutions significantly impedes the development of self-driving laboratories. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving laboratories. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated laboratories. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.
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Affiliation(s)
- Loïc M. Roch
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Florian Häse
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Christoph Kreisbeck
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Teresa Tamayo-Mendoza
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Lars P. E. Yunker
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jason E. Hein
- Department of Chemistry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alán Aspuru-Guzik
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Chemistry and Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Canadian Institute of Advanced Research, Toronto, Ontario, Canada
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8
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Christler A, Felföldi E, Mosor M, Sauer D, Walch N, Dürauer A, Jungbauer A. Semi-automation of process analytics reduces operator effect. Bioprocess Biosyst Eng 2019; 43:753-764. [PMID: 31813007 PMCID: PMC7125066 DOI: 10.1007/s00449-019-02254-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 11/11/2019] [Indexed: 01/14/2023]
Abstract
The aim of this study was to semi-automate process analytics for the quantification of common impurities in downstream processing such as host cell DNA, host cell proteins and endotoxins using a commercial liquid handling station. By semi-automation, the work load to fully analyze the elution peak of a purification run was reduced by at least 2.41 h. The relative standard deviation of results among different operators over a time span of up to 6 months was at the best reduced by half, e.g. from 13.7 to 7.1% in dsDNA analysis. Automation did not improve the reproducibility of results produced by one operator but released time for data evaluation and interpretation or planning of experiments. Overall, semi-automation of process analytics reduced operator-specific influence on test results. Such robust and reproducible analytics is fundamental to establish process analytical technology and get downstream processing ready for Quality by Design approaches.
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Affiliation(s)
- A Christler
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria
| | - E Felföldi
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria
| | - M Mosor
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria
| | - D Sauer
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria
| | - N Walch
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria
| | - A Dürauer
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria.,Institute of Bioprocess Science and Engineering, Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria
| | - A Jungbauer
- Austrian Centre of Industrial Biotechnology, Muthgasse 11, 1190, Vienna, Austria. .,Institute of Bioprocess Science and Engineering, Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, Muthgasse 18, 1190, Vienna, Austria.
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9
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Interactive programming paradigm for real-time experimentation with remote living matter. Proc Natl Acad Sci U S A 2019; 116:5411-5419. [PMID: 30824592 PMCID: PMC6431204 DOI: 10.1073/pnas.1815367116] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Biology cloud laboratories are an emerging approach to lowering access barriers for life-science experimentation. However, suitable programming approaches and interfaces are lacking for both domain experts and lay users, especially ones that enable interaction with the living matter itself and not just the control of equipment. Here we present a programming paradigm for real-time interactive applications with remotely housed biological systems which is accessible and useful for scientists, programmers, and lay people. Our user studies show that scientists and nonscientists are able to rapidly develop a variety of applications, such as interactive biophysics experiments and games. This paradigm has the potential to make first-hand experiences with biology accessible to all of society and to accelerate the rate of scientific discovery. Recent advancements in life-science instrumentation and automation enable entirely new modes of human interaction with microbiological processes and corresponding applications for science and education through biology cloud laboratories. A critical barrier for remote and on-site life-science experimentation (for both experts and nonexperts alike) is the absence of suitable abstractions and interfaces for programming living matter. To this end we conceptualize a programming paradigm that provides stimulus and sensor control functions for real-time manipulation of physical biological matter. Additionally, a simulation mode facilitates higher user throughput, program debugging, and biophysical modeling. To evaluate this paradigm, we implemented a JavaScript-based web toolkit, “Bioty,” that supports real-time interaction with swarms of phototactic Euglena cells hosted on a cloud laboratory. Studies with remote and on-site users demonstrate that individuals with little to no biology knowledge and intermediate programming knowledge were able to successfully create and use scientific applications and games. This work informs the design of programming environments for controlling living matter in general, for living material microfabrication and swarm robotics applications, and for lowering the access barriers to the life sciences for professional and citizen scientists, learners, and the lay public.
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10
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Mao Y, Pan Y, Li X, Li B, Chu J, Pan T. High-precision digital droplet pipetting enabled by a plug-and-play microfluidic pipetting chip. LAB ON A CHIP 2018; 18:2720-2729. [PMID: 30014071 DOI: 10.1039/c8lc00505b] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Emerging demands for handling minute liquid samples and reagents have been constantly growing in a wide variety of medical and biological areas. This calls for low-volume and high-precision liquid handling solutions with ease-of-use and portability. In this article, a new digital droplet pipetting method is introduced for the first time, derived from the microfluidic impact printing principle. Configured as a conventional handheld pipette, the prototype device consists of a plug-and-play and disposable microfluidic pipetting chip, driven by a programmable electromagnetic actuator for on-demand dispensing of nanoliter droplets. In particular, the impact-driven microfluidic pipetting chip, in place of the traditional disposable pipette tips, offers both liquid loading and droplet generation. The printing nozzle has been micro-fabricated using a femtosecond laser with a super-hydrophobic structure, in order to minimize the dispensing residues. As a result of the high-precision droplet dispensing principle, the variations of the dispensed volume have been successfully reduced from 49.5% to 0.6% at 0.1 μL, as compared to its commercial counterparts. A proof-of-concept study for concentration dilution and quantitative analysis of cell drug resistance has been carried out by using the digital droplet pipetting system, demonstrating its potential in a broad range of biomedical applications which require both high precision and low-volume processing.
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Affiliation(s)
- Yuxin Mao
- Department of Precision Machinery & Precision Instrumentation, University of Science & Technology of China, Hefei, Anhui 230027, China.
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