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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.
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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
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2
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Yi X, Rasor BJ, Boadi N, Louie K, Northen TR, Karim AS, Jewett MC, Alper HS. Establishing a versatile toolkit of flux enhanced strains and cell extracts for pathway prototyping. Metab Eng 2023; 80:241-253. [PMID: 37890611 DOI: 10.1016/j.ymben.2023.10.008] [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] [Received: 08/09/2023] [Revised: 10/07/2023] [Accepted: 10/23/2023] [Indexed: 10/29/2023]
Abstract
Building and optimizing biosynthetic pathways in engineered cells holds promise to address societal needs in energy, materials, and medicine, but it is often time-consuming. Cell-free synthetic biology has emerged as a powerful tool to accelerate design-build-test-learn cycles for pathway engineering with increased tolerance to toxic compounds. However, most cell-free pathway prototyping to date has been performed in extracts from wildtype cells which often do not have sufficient flux towards the pathways of interest, which can be enhanced by engineering. Here, to address this gap, we create a set of engineered Escherichia coli and Saccharomyces cerevisiae strains rewired via CRISPR-dCas9 to achieve high-flux toward key metabolic precursors; namely, acetyl-CoA, shikimate, triose-phosphate, oxaloacetate, α-ketoglutarate, and glucose-6-phosphate. Cell-free extracts generated from these strains are used for targeted enzyme screening in vitro. As model systems, we assess in vivo and in vitro production of triacetic acid lactone from acetyl-CoA and muconic acid from the shikimate pathway. The need for these platforms is exemplified by the fact that muconic acid cannot be detected in wildtype extracts provided with the same biosynthetic enzymes. We also perform metabolomic comparison to understand biochemical differences between the cellular and cell-free muconic acid synthesis systems (E. coli and S. cerevisiae cells and cell extracts with and without metabolic rewiring). While any given pathway has different interfaces with metabolism, we anticipate that this set of pre-optimized, flux enhanced cell extracts will enable prototyping efforts for new biosynthetic pathways and the discovery of biochemical functions of enzymes.
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Affiliation(s)
- Xiunan Yi
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, 78712, USA; McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, 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
| | - Nathalie Boadi
- 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
| | - Katherine Louie
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Trent R Northen
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - 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
| | - 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; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
| | - Hal S Alper
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, 78712, USA; McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
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3
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Cordell WT, Avolio G, Takors R, Pfleger BF. Milligrams to kilograms: making microbes work at scale. Trends Biotechnol 2023; 41:1442-1457. [PMID: 37271589 DOI: 10.1016/j.tibtech.2023.05.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023]
Abstract
If biomanufacturing can become a sustainable route for producing chemicals, it will provide a critical step in reducing greenhouse gas emissions to fight climate change. However, efforts to industrialize microbial synthesis of chemicals have met with varied success, due, in part, to challenges in translating laboratory successes to industrial scale. With a particular focus on Escherichia coli, this review examines the lessons learned when studying microbial physiology and metabolism under conditions that simulate large-scale bioreactors and methods to minimize cellular waste through reduction of maintenance energy, optimizing the stress response and minimizing culture heterogeneity. With general strategies to overcome these challenges, biomanufacturing process scale-up could be de-risked and the time and cost of bringing promising syntheses to market could be reduced.
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Affiliation(s)
- William T Cordell
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Gennaro Avolio
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart 70569, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Stuttgart 70569, Germany
| | - Brian F Pfleger
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; DOE Center Advanced Bioenergy and Bioproducts Innovation, University of Wisconsin-Madison, Madison, WI 53706, USA; DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53706, USA.
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4
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Becker L, Sturm J, Eiden F, Holtmann D. Analyzing and understanding the robustness of bioprocesses. Trends Biotechnol 2023; 41:1013-1026. [PMID: 36959084 DOI: 10.1016/j.tibtech.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/25/2023]
Abstract
The robustness of bioprocesses is becoming increasingly important. The main driving forces of this development are, in particular, increasing demands on product purities as well as economic aspects. In general, bioprocesses exhibit extremely high complexity and variability. Biological systems often have a much higher intrinsic variability compared with chemical processes, which makes the development and characterization of robust processes tedious task. To predict and control robustness, a clear understanding of interactions between input and output variables is necessary. Robust bioprocesses can be realized, for example, by using advanced control strategies for the different unit operations. In this review, we discuss the different biological, technical, and mathematical tools for the analysis and control of bioprocess robustness.
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Affiliation(s)
- Lucas Becker
- Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied Sciences Mittelhessen, Wiesenstrasse 14, 35390 Giessen, Germany
| | - Jonathan Sturm
- Bioprozesstechnik Group, Westfälische Hochschule, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany; iAMB - Institute of Applied Microbiology, ABBt - Aachen Biology and Biotechnology, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Frank Eiden
- Bioprozesstechnik Group, Westfälische Hochschule, August-Schmidt-Ring 10, 45665 Recklinghausen, Germany
| | - Dirk Holtmann
- Institute of Bioprocess Engineering and Pharmaceutical Technology, University of Applied Sciences Mittelhessen, Wiesenstrasse 14, 35390 Giessen, Germany.
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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6
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Liebal UW, Schimassek R, Broderius I, Maaßen N, Vogelgesang A, Weyers P, Blank LM. Biotechnology Data Analysis Training with Jupyter Notebooks. JOURNAL OF MICROBIOLOGY & BIOLOGY EDUCATION 2023; 24:00113-22. [PMID: 37089214 PMCID: PMC10117103 DOI: 10.1128/jmbe.00113-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/20/2022] [Indexed: 05/03/2023]
Abstract
Biotechnology has experienced innovations in analytics and data processing. As the volume of data and its complexity grow, new computational procedures for extracting information are being developed. However, the rate of change outpaces the adaptation of biotechnology curricula, necessitating new teaching methodologies to equip biotechnologists with data analysis abilities. To simulate experimental data, we created a virtual organism simulator (silvio) by combining diverse cellular and subcellular microbial models. With the silvio Python package, we constructed a computer-based instructional workflow to teach growth curve data analysis, promoter sequence design, and expression rate measurement. The instructional workflow is a Jupyter Notebook with background explanations and Python-based experiment simulations combined. The data analysis is conducted either within the Notebook in Python or externally with Excel. This instructional workflow was separately implemented in two distance courses for Master's students in biology and biotechnology with assessment of the pedagogic efficiency. The concept of using virtual organism simulations that generate coherent results across different experiments can be used to construct consistent and motivating case studies for biotechnological data literacy.
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Affiliation(s)
- Ulf W. Liebal
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Rafael Schimassek
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Iris Broderius
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Nicole Maaßen
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
| | - Alina Vogelgesang
- Center for Learning Services, RWTH Aachen University, Aachen, Germany
| | - Philipp Weyers
- Center for Learning Services, RWTH Aachen University, Aachen, Germany
| | - Lars M. Blank
- Institute of Applied Microbiology, ABBT, RWTH Aachen University, Aachen, Germany
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7
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Rapoo SM, Budeli P, Thaoge ML. Recovery of Potential Starter Cultures and Probiotics from Fermented Sorghum (Ting) Slurries. Microorganisms 2023; 11:microorganisms11030715. [PMID: 36985287 PMCID: PMC10054160 DOI: 10.3390/microorganisms11030715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 02/20/2023] [Accepted: 03/01/2023] [Indexed: 03/12/2023] Open
Abstract
Fermented foods are thought to provide a source of probiotics that promote gut health. Consequently, isolation and characterization of fermented food strains and their applications in a controlled fermentation process or as probiotics present a new facet in this area of research. Therefore, the current study sought to identify dominant strains in sorghum-fermented foods (ting) and characterize their probiotic potential in vitro. Recovered isolates were identified as Lactobacillus helveticus, Lactobacillus amylolyticus, Lacticaseibacillus paracasei, Lacticaseibacillus paracasei subsp paracasei, Lactiplantibacillus plantarum, Levilactobacillus brevis, Loigolactobacillus coryniformis and Loigolactobacillus coryniformis subsp torquens based on the their 16S rRNA sequences. Increased biomass was noted in seven out of nine under a low pH of 3 and a high bile concentration of 2% in vitro. Bactericidal activities of isolated LABs presented varying degrees of resistance against selected pathogenic bacteria ranging between (1.57 to 41 mm), (10 to 41 mm), and (11.26 to 42 mm) for Salmonella typhimurium ATTC 14028, Staphylococcus aureus ATTC 6538 and Escherichia coli ATTC8739, respectively. Ampicillin, erythromycin, mupirocin, tetracycline and chloramphenicol were able to inhibit growth of all selected LABs. Thus, isolates recovered from ting partially satisfy the potential candidacy for probiotics by virtue of being more tolerant to acid and bile, antibacterial activity and antibiotic resistance.
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8
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Accelerating strain phenotyping with desorption electrospray ionization-imaging mass spectrometry and untargeted analysis of intact microbial colonies. Proc Natl Acad Sci U S A 2021; 118:2109633118. [PMID: 34857637 DOI: 10.1073/pnas.2109633118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2021] [Indexed: 11/18/2022] Open
Abstract
Reading and writing DNA were once the rate-limiting step in synthetic biology workflows. This has been replaced by the search for the optimal target sequences to produce systems with desired properties. Directed evolution and screening mutant libraries are proven technologies for isolating strains with enhanced performance whenever specialized assays are available for rapidly detecting a phenotype of interest. Armed with technologies such as CRISPR-Cas9, these experiments are capable of generating libraries of up to 1010 genetic variants. At a rate of 102 samples per day, standard analytical methods for assessing metabolic phenotypes represent a major bottleneck to modern synthetic biology workflows. To address this issue, we have developed a desorption electrospray ionization-imaging mass spectrometry screening assay that directly samples microorganisms. This technology increases the throughput of metabolic measurements by reducing sample preparation and analyzing organisms in a multiplexed fashion. To further accelerate synthetic biology workflows, we utilized untargeted acquisitions and unsupervised analytics to assess multiple targets for future engineering strategies within a single acquisition. We demonstrate the utility of the developed method using Escherichia coli strains engineered to overproduce free fatty acids. We determined discrete metabolic phenotypes associated with each strain, which include the primary fatty acid product, secondary products, and additional metabolites outside the engineered product pathway. Furthermore, we measured changes in amino acid levels and membrane lipid composition, which affect cell viability. In sum, we present an analytical method to accelerate synthetic biology workflows through rapid, untargeted, and multiplexed metabolomic analyses.
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Rienzo M, Lin KC, Mobilia KC, Sackmann EK, Kurz V, Navidi AH, King J, Onorato RM, Chao LK, Wu T, Jiang H, Valley JK, Lionberger TA, Leavell MD. High-throughput optofluidic screening for improved microbial cell factories via real-time micron-scale productivity monitoring. LAB ON A CHIP 2021; 21:2901-2912. [PMID: 34160512 DOI: 10.1039/d1lc00389e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The industrial synthetic biology sector has made huge investments to achieve relevant miniaturized screening systems for scalable fermentation. Here we present the first example of a high-throughput (>103 genotypes per week) perfusion-based screening system to improve small-molecule secretion from microbial strains. Using the Berkeley Lights Beacon® system, the productivity of each strain could be directly monitored in real time during continuous culture, yielding phenotypes that correlated strongly (r2 > 0.8, p < 0.0005) with behavior in industrially relevant bioreactor processes. This method allows a much closer approximation of a typical fed-batch fermentation than conventional batch-like droplet or microplate culture models, in addition to rich time-dependent data on growth and productivity. We demonstrate these advantages by application to the improvement of high-productivity strains using whole-genome random mutagenesis, yielding mutants with substantially improved (by up to 85%) peak specific productivities in bioreactors. Each screen of ∼5 × 103 mutants could be completed in under 8 days (including 5 days involving user intervention), saving ∼50-75% of the time required for conventional microplate-based screening methods.
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Affiliation(s)
- Matthew Rienzo
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
| | - Ke-Chih Lin
- Technology and Business Development, Berkeley Lights, Inc., 5858 Horton St., Unit 320, Emeryville, CA 94608, USA.
| | - Kellen C Mobilia
- Technology and Business Development, Berkeley Lights, Inc., 5858 Horton St., Unit 320, Emeryville, CA 94608, USA.
| | - Eric K Sackmann
- Technology and Business Development, Berkeley Lights, Inc., 5858 Horton St., Unit 320, Emeryville, CA 94608, USA.
| | - Volker Kurz
- Technology and Business Development, Berkeley Lights, Inc., 5858 Horton St., Unit 320, Emeryville, CA 94608, USA.
| | - Adam H Navidi
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
| | - Jarett King
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
| | - Robert M Onorato
- Technology and Business Development, Berkeley Lights, Inc., 5858 Horton St., Unit 320, Emeryville, CA 94608, USA.
| | - Lawrence K Chao
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
| | - Tony Wu
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
| | - Hanxiao Jiang
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
| | - Justin K Valley
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
| | - Troy A Lionberger
- Technology and Business Development, Berkeley Lights, Inc., 5858 Horton St., Unit 320, Emeryville, CA 94608, USA.
| | - Michael D Leavell
- Research and Development, Amyris, Inc., 5885 Hollis St., Suite 100, Emeryville, CA 94608, USA.
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10
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Introduction to the Special Issue on "Recent Advances in Fermentation Technology 2020". J Ind Microbiol Biotechnol 2020; 47:909-911. [PMID: 33206275 DOI: 10.1007/s10295-020-02332-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Carbonell P, Le Feuvre R, Takano E, Scrutton NS. In silico design and automated learning to boost next-generation smart biomanufacturing. Synth Biol (Oxf) 2020; 5:ysaa020. [PMID: 33344778 PMCID: PMC7737007 DOI: 10.1093/synbio/ysaa020] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/08/2020] [Accepted: 09/28/2020] [Indexed: 02/07/2023] Open
Abstract
The increasing demand for bio-based compounds produced from waste or sustainable sources is driving biofoundries to deliver a new generation of prototyping biomanufacturing platforms. Integration and automation of the design, build, test and learn (DBTL) steps in centers like SYNBIOCHEM in Manchester and across the globe (Global Biofoundries Alliance) are helping to reduce the delivery time from initial strain screening and prototyping towards industrial production. Notably, a portfolio of producer strains for a suite of material monomers was recently developed, some approaching industrial titers, in a tour de force by the Manchester Centre that was achieved in less than 90 days. New in silico design tools are providing significant contributions to the front end of the DBTL pipelines. At the same time, the far-reaching initiatives of modern biofoundries are generating a large amount of high-dimensional data and knowledge that can be integrated through automated learning to expedite the DBTL cycle. In this Perspective, the new design tools and the role of the learning component as an enabling technology for the next generation of automated biofoundries are discussed. Future biofoundries will operate under completely automated DBTL cycles driven by in silico optimal experimental planning, full biomanufacturing devices connectivity, virtualization platforms and cloud-based design. The automated generation of robotic build worklists and the integration of machine-learning algorithms will collectively allow high levels of adaptability and rapid design changes toward fully automated smart biomanufacturing.
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Affiliation(s)
- Pablo Carbonell
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Rosalind Le Feuvre
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Eriko Takano
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Nigel S Scrutton
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
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