1
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Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. NPJ Syst Biol Appl 2024; 10:35. [PMID: 38565850 PMCID: PMC10987498 DOI: 10.1038/s41540-024-00361-5] [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: 11/21/2023] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA.
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, Baltimore, Baltimore, MD, USA.
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2
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Blau T, Chades I, Ong CS. Machine Learning for Biological Design. Methods Mol Biol 2024; 2760:319-344. [PMID: 38468097 DOI: 10.1007/978-1-0716-3658-9_19] [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: 03/13/2024]
Abstract
We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and provide additional tools to guide scientific discovery. There are two different kinds of objectives when designing better experiments: to improve the predictive model or to improve the experimental outcome. We survey five different approaches for adaptive experimental design that iteratively search the space of possible experiments while adapting to measured data. The approaches are Bayesian optimization, bandits, reinforcement learning, optimal experimental design, and active learning. These machine learning approaches have shown promise in various areas of biology, and we provide broad guidelines to the practitioner and links to further resources.
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Affiliation(s)
- Tom Blau
- CSIRO, Data61, Eveleigh, NSW, Australia
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3
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Zilberzwige-Tal S, Fontanarrosa P, Bychenko D, Dorfan Y, Gazit E, Myers CJ. Investigating and Modeling the Factors That Affect Genetic Circuit Performance. ACS Synth Biol 2023; 12:3189-3204. [PMID: 37916512 PMCID: PMC10661042 DOI: 10.1021/acssynbio.3c00151] [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: 03/11/2023] [Indexed: 11/03/2023]
Abstract
Over the past 2 decades, synthetic biology has yielded ever more complex genetic circuits that are able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a broader test step to the design-build-test-learn workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof of concept, we have designed and evaluated a genetic circuit in various temperatures, inducer concentrations, nonsterilized soil exposure, and bacterial growth stages. We determined that the circuit's performance is dramatically altered when these factors differ from the optimal lab conditions. We observed significant changes in the time for signal detection as well as signal intensity when the genetic circuit was tested under nonoptimal lab conditions. As a learning effort, we then proceeded to generate model predictions in untested conditions, which is currently lacking in synthetic biology application design. Furthermore, broader test and learn steps uncovered a negative correlation between the time it takes for a gate to turn ON and the bacterial growth phases. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on test and learn steps (i.e., characterizing parts and circuits for a broad range of conditions) will provide missing insights on genetic circuit behavior outside the lab.
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Affiliation(s)
- Shai Zilberzwige-Tal
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Pedro Fontanarrosa
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Darya Bychenko
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Yuval Dorfan
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
- Bio-engineering,
Electrical Engineering Faculty, Holon Institute
of Technology (HIT), Holon 5810201, Israel
- Alagene
Ltd., Innovation Center, Reichman University, Herzliya 7670608, Israel
| | - Ehud Gazit
- The
Shmunis School of Biomedicine and Cancer Research, Life Sciences Faculty, Tel Aviv University, Tel Aviv-Yafo 6997801, Israel
| | - Chris J. Myers
- Department
of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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4
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Mousavi R, Lobo D. Automatic design of gene regulatory mechanisms for spatial pattern formation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550573. [PMID: 37546866 PMCID: PMC10402059 DOI: 10.1101/2023.07.26.550573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
- Greenebaum Comprehensive Cancer Center and Center for Stem Cell Biology & Regenerative Medicine, University of Maryland, School of Medicine, 22 S. Greene Street, Baltimore, MD 21201, USA
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5
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del Olmo Lianes I, Yubero P, Gómez-Luengo Á, Nogales J, Espeso DR. Technical upgrade of an open-source liquid handler to support bacterial colony screening. Front Bioeng Biotechnol 2023; 11:1202836. [PMID: 37404684 PMCID: PMC10315574 DOI: 10.3389/fbioe.2023.1202836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 06/07/2023] [Indexed: 07/06/2023] Open
Abstract
The optimization of genetically engineered biological constructs is a key step to deliver high-impact biotechnological applications. The use of high-throughput DNA assembly methods allows the construction of enough genotypic variants to successfully cover the target design space. This, however, entails extra workload for researchers during the screening stage of candidate variants. Despite the existence of commercial colony pickers, their high price excludes small research laboratories and budget-adjusted institutions from accessing such extensive screening capability. In this work we present COPICK, a technical solution to automatize colony picking in an open-source liquid handler Opentrons OT-2. COPICK relies on a mounted camera to capture images of regular Petri dishes and detect microbial colonies for automated screening. COPICK's software can then automatically select the best colonies according to different criteria (size, color and fluorescence) and execute a protocol to pick them for further analysis. Benchmark tests performed for E. coli and P. putida colonies delivers a raw picking performance over pickable colonies of 82% with an accuracy of 73.4% at an estimated rate of 240 colonies/h. These results validate the utility of COPICK, and highlight the importance of ongoing technical improvements in open-source laboratory equipment to support smaller research teams.
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Affiliation(s)
- Irene del Olmo Lianes
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Pablo Yubero
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Álvaro Gómez-Luengo
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics Towards a Circular Economy—Consejo Superior de Investigaciones Científicas, SusPlast-CSIC, Madrid, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics Towards a Circular Economy—Consejo Superior de Investigaciones Científicas, SusPlast-CSIC, Madrid, Spain
| | - David R. Espeso
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
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6
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Grozinger L, Heidrich E, Goñi-Moreno Á. An electrogenetic toggle switch model. Microb Biotechnol 2023; 16:546-559. [PMID: 36207818 PMCID: PMC9948229 DOI: 10.1111/1751-7915.14153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/29/2022] [Accepted: 09/10/2022] [Indexed: 11/29/2022] Open
Abstract
Synthetic biology uses molecular biology to implement genetic circuits that perform computations. These circuits can process inputs and deliver outputs according to predefined rules that are encoded, often entirely, into genetic parts. However, the field has recently begun to focus on using mechanisms beyond the realm of genetic parts for engineering biological circuits. We analyse the use of electrogenic processes for circuit design and present a model for a merged genetic and electrogenetic toggle switch operating in a biofilm attached to an electrode. Computational simulations explore conditions under which bistability emerges in order to identify the circuit design principles for best switch performance. The results provide a basis for the rational design and implementation of hybrid devices that can be measured and controlled both genetically and electronically.
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Affiliation(s)
- Lewis Grozinger
- School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)-Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA/CSIC), Madrid, Spain
| | - Elizabeth Heidrich
- School of Civil Engineering and Geosciences, Newcastle University, Newcastle Upon Tyne, UK
| | - 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), Madrid, Spain
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7
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Hooe SL, Ellis GA, Medintz IL. Alternative design strategies to help build the enzymatic retrosynthesis toolbox. RSC Chem Biol 2022; 3:1301-1313. [PMID: 36349225 PMCID: PMC9627731 DOI: 10.1039/d2cb00096b] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 09/11/2022] [Indexed: 05/30/2024] Open
Abstract
Most of the complex molecules found in nature still cannot be synthesized by current organic chemistry methods. Given the number of enzymes that exist in nature and the incredible potential of directed evolution, the field of synthetic biology contains perhaps all the necessary building blocks to bring about the realization of applied enzymatic retrosynthesis. Current thinking anticipates that enzymatic retrosynthesis will be implemented using conventional cell-based synthetic biology approaches where requisite native, heterologous, designer, and evolved enzymes making up a given multi-enzyme pathway are hosted by chassis organisms to carry out designer synthesis. In this perspective, we suggest that such an effort should not be limited by solely exploiting living cells and enzyme evolution and describe some useful yet less intensive complementary approaches that may prove especially productive in this grand scheme. By decoupling reactions from the environment of a living cell, a significantly larger portion of potential synthetic chemical space becomes available for exploration; most of this area is currently unavailable to cell-based approaches due to toxicity issues. In contrast, in a cell-free reaction a variety of classical enzymatic approaches can be exploited to improve performance and explore and understand a given enzyme's substrate specificity and catalytic profile towards non-natural substrates. We expect these studies will reveal unique enzymatic capabilities that are not accessible in living cells.
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Affiliation(s)
- Shelby L Hooe
- Center for Bio/Molecular Science and Engineering Code 6900, U.S. Naval Research Laboratory Washington DC 20375 USA
- National Research Council Washington DC 20001 USA
| | - Gregory A Ellis
- Center for Bio/Molecular Science and Engineering Code 6900, U.S. Naval Research Laboratory Washington DC 20375 USA
| | - Igor L Medintz
- Center for Bio/Molecular Science and Engineering Code 6900, U.S. Naval Research Laboratory Washington DC 20375 USA
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8
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Abstract
As genetic circuits become more sophisticated, the size and complexity of data about their designs increase. The data captured goes beyond genetic sequences alone; information about circuit modularity and functional details improves comprehension, performance analysis, and design automation techniques. However, new data types expose new challenges around the accessibility, visualization, and usability of design data (and metadata). Here, we present a method to transform circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into subnetworks of relevant information based on requirements such as the hierarchy of biological parts or interactions between entities. A significant advantage of a network approach is the ability to scale abstraction, providing an automatic sliding level of detail that further tailors the visualization to a given situation. Additionally, several visual changes can be applied, such as coloring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. We advocate using networks to structure, access, and improve synthetic biology information.
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Affiliation(s)
- Matthew Crowther
- School
of Computing, Newcastle University, Newcastle Upon Tyne NE4
5TG, United Kingdom
- Centro
de Biotecnología y Genómica de Plantas, Universidad
Politécnica de Madrid, Instituto
Nacional de Investigación y Tecnología Agraria y Alimentaria
(INIA-CSIC), Pozuelo
de Alarcón, 28223 Madrid, Spain
| | - Anil Wipat
- School
of Computing, Newcastle University, Newcastle Upon Tyne NE4
5TG, United Kingdom
| | - Ángel Goñi-Moreno
- Centro
de Biotecnología y Genómica de Plantas, Universidad
Politécnica de Madrid, Instituto
Nacional de Investigación y Tecnología Agraria y Alimentaria
(INIA-CSIC), Pozuelo
de Alarcón, 28223 Madrid, Spain
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9
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The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering. Nat Commun 2022; 13:5082. [PMID: 36038542 PMCID: PMC9424320 DOI: 10.1038/s41467-022-32661-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/11/2022] [Indexed: 11/27/2022] Open
Abstract
Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.
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10
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Bak SK, Seong W, Rha E, Lee H, Kim SK, Kwon KK, Kim H, Lee SG. Novel High-Throughput DNA Part Characterization Technique for Synthetic Biology. J Microbiol Biotechnol 2022; 32:1026-1033. [PMID: 35879270 PMCID: PMC9628936 DOI: 10.4014/jmb.2207.07013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 12/15/2022]
Abstract
This study presents a novel DNA part characterization technique that increases throughput by combinatorial DNA part assembly, solid plate-based quantitative fluorescence assay for phenotyping, and barcode tagging-based long-read sequencing for genotyping. We confirmed that the fluorescence intensities of colonies on plates were comparable to fluorescence at the single-cell level from a high-end, flow-cytometry device and developed a high-throughput image analysis pipeline. The barcode tagging-based long-read sequencing technique enabled rapid identification of all DNA parts and their combinations with a single sequencing experiment. Using our techniques, forty-four DNA parts (21 promoters and 23 RBSs) were successfully characterized in 72 h without any automated equipment. We anticipate that this high-throughput and easy-to-use part characterization technique will contribute to increasing part diversity and be useful for building genetic circuits and metabolic pathways in synthetic biology.
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Affiliation(s)
- Seong-Kun Bak
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea,Biosystems and Bioengineering Program, University of Science and Technology, Daejeon 34141, Republic of Korea
| | - Wonjae Seong
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Eugene Rha
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Hyewon Lee
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Seong Keun Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Kil Koang Kwon
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Haseong Kim
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea,Biosystems and Bioengineering Program, University of Science and Technology, Daejeon 34141, Republic of Korea,Corresponding authors H.S. Kim Phone: +82-42-860-4372 Fax: +82-42-860-4489 E-mail:
| | - Seung-Goo Lee
- Synthetic Biology Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea,Biosystems and Bioengineering Program, University of Science and Technology, Daejeon 34141, Republic of Korea,
S.G. Lee Phone: +82-42-860-4373 E-mail:
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11
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Berliner AJ, Lipsky I, Ho D, Hilzinger JM, Vengerova G, Makrygiorgos G, McNulty MJ, Yates K, Averesch NJH, Cockell CS, Wallentine T, Seefeldt LC, Criddle CS, Nandi S, McDonald KA, Menezes AA, Mesbah A, Arkin AP. Space bioprocess engineering on the horizon. COMMUNICATIONS ENGINEERING 2022; 1:13. [PMCID: PMC10955938 DOI: 10.1038/s44172-022-00012-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 05/17/2022] [Indexed: 06/04/2024]
Abstract
Space bioprocess engineering (SBE) is an emerging multi-disciplinary field to design, realize, and manage biologically-driven technologies specifically with the goal of supporting life on long term space missions. SBE considers synthetic biology and bioprocess engineering under the extreme constraints of the conditions of space. A coherent strategy for the long term development of this field is lacking. In this Perspective, we describe the need for an expanded mandate to explore biotechnological needs of the future missions. We then identify several key parameters—metrics, deployment, and training—which together form a pathway towards the successful development and implementation of SBE technologies of the future. Space bioprocess engineering integrates synthetic biology and bioprocess engineering with the specific goal to support human life in long term space missions. In this Perspective, Berliner and colleagues describe a pathway towards the development and implementation of space bioprocessing technologies of the future.
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Affiliation(s)
- Aaron J. Berliner
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Isaac Lipsky
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Davian Ho
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Jacob M. Hilzinger
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Gretchen Vengerova
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
| | - Georgios Makrygiorgos
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA USA
| | - Matthew J. McNulty
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemical Engineering, University of California, Davis, Davis, CA USA
| | - Kevin Yates
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemical Engineering, University of California, Davis, Davis, CA USA
| | - Nils J. H. Averesch
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA USA
| | - Charles S. Cockell
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, Edinburgh, UK
| | - Tyler Wallentine
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemistry and Biochemistry, Utah State University, Logan, UT USA
| | - Lance C. Seefeldt
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemistry and Biochemistry, Utah State University, Logan, UT USA
| | - Craig S. Criddle
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Civil and Environmental Engineering, Stanford University, Stanford, CA USA
| | - Somen Nandi
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemical Engineering, University of California, Davis, Davis, CA USA
- Global HealthShare Initiative, Davis, CA USA
| | - Karen A. McDonald
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemical Engineering, University of California, Davis, Davis, CA USA
| | - Amor A. Menezes
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL USA
| | - Ali Mesbah
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA USA
| | - Adam P. Arkin
- Center for the Utilization of Biological Engineering in Space (CUBES), Berkeley, CA USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA USA
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12
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Yang Y, Mao Y, Wang R, Li H, Liu Y, Cheng H, Shi Z, Wang Y, Wang M, Zheng P, Liao X, Ma H. AutoESD: a web tool for automatic editing sequence design for genetic manipulation of microorganisms. Nucleic Acids Res 2022; 50:W75-W82. [PMID: 35639727 PMCID: PMC9252779 DOI: 10.1093/nar/gkac417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/20/2022] [Accepted: 05/09/2022] [Indexed: 11/30/2022] Open
Abstract
Advances in genetic manipulation and genome engineering techniques have enabled on-demand targeted deletion, insertion, and substitution of DNA sequences. One important step in these techniques is the design of editing sequences (e.g. primers, homologous arms) to precisely target and manipulate DNA sequences of interest. Experimental biologists can employ multiple tools in a stepwise manner to assist editing sequence design (ESD), but this requires various software involving non-standardized data exchange and input/output formats. Moreover, necessary quality control steps might be overlooked by non-expert users. This approach is low-throughput and can be error-prone, which illustrates the need for an automated ESD system. In this paper, we introduce AutoESD (https://autoesd.biodesign.ac.cn/), which designs editing sequences for all steps of genetic manipulation of many common homologous-recombination techniques based on screening-markers. Notably, multiple types of manipulations for different targets (CDS or intergenic region) can be processed in one submission. Moreover, AutoESD has an entirely cloud-based serverless architecture, offering high reliability, robustness and scalability which is capable of parallelly processing hundreds of design tasks each having thousands of targets in minutes. To our knowledge, AutoESD is the first cloud platform enabling precise, automated, and high-throughput ESD across species, at any genomic locus for all manipulation types.
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Affiliation(s)
- Yi Yang
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufeng Mao
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Ruoyu Wang
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Haoran Li
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Ye Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Haijiao Cheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Zhenkun Shi
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Yu Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Meng Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Ping Zheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Xiaoping Liao
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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13
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Davies JA. Synthetic Morphogenesis: introducing IEEE journal readers to programming living mammalian cells to make structures. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:688-707. [PMID: 36590991 PMCID: PMC7614003 DOI: 10.1109/jproc.2021.3137077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Synthetic morphogenesis is a new engineering discipline, in which cells are genetically engineered to make designed shapes and structures. At least in this early phase of the field, devices tend to make use of natural shape-generating processes that operate in embryonic development, but invoke them artificially at times and in orders of a technologist's choosing. This requires construction of genetic control, sequencing and feedback systems that have close parallels to electronic design, which is one reason the field may be of interest to readers of IEEE journals. The other reason is that synthetic morphogenesis allows the construction of two-way interfaces, especially opto-genetic and opto-electronic, between the living and the electronic, allowing unprecedented information flow and control between the two types of 'machine'. This review introduces synthetic morphogenesis, illustrates what has been achieved, drawing parallels wherever possible between biology and electronics, and looks forward to likely next steps and challenges to be overcome.
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Affiliation(s)
- Jamie A Davies
- Professor of Experimental Anatomy at the University of Edinburgh, UK, and a member of the Centre for Mammalian Synthetic Biology at that University
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14
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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.
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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
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15
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Tellechea-Luzardo J, Otero-Muras I, Goñi-Moreno A, Carbonell P. Fast biofoundries: coping with the challenges of biomanufacturing. Trends Biotechnol 2022; 40:831-842. [PMID: 35012773 DOI: 10.1016/j.tibtech.2021.12.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/13/2021] [Accepted: 12/13/2021] [Indexed: 11/16/2022]
Abstract
Biofoundries are highly automated facilities that enable the rapid and efficient design, build, test, and learn cycle of biomanufacturing and engineering biology, which is applicable to both research and industrial production. However, developing a biofoundry platform can be expensive and time consuming. A biofoundry should grow organically, starting from a basic platform but with a vision for automation, equipment interoperability, and efficiency. By thinking about strategies early in the process through process planning, simulation, and optimization, bottlenecks can be identified and resolved. Here, we provide a survey of technological solutions in biofoundries and their advantages and limitations. We explore possible pathways towards the creation of a functional, early-phase biofoundry, and strategies towards long-term sustainability.
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Affiliation(s)
- Jonathan Tellechea-Luzardo
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politécnica de València (UPV), 46022 València, Spain
| | - Irene Otero-Muras
- Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, Catedrático Agustín Escardino Benlloch 9, Paterna, 46980 València, Spain
| | - Angel Goñi-Moreno
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politécnica de València (UPV), 46022 València, Spain.
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16
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Schladt T, Engelmann N, Kubaczka E, Hochberger C, Koeppl H. Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty. ACS Synth Biol 2021; 10:3316-3329. [PMID: 34807573 PMCID: PMC8689692 DOI: 10.1021/acssynbio.1c00193] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
![]()
Genetic design automation
methods for combinational circuits often
rely on standard algorithms from electronic design automation in their
circuit synthesis and technology mapping. However, those algorithms
are domain-specific and are hence often not directly suitable for
the biological context. In this work we identify aspects of those
algorithms that require domain-adaptation. We first demonstrate that
enumerating structural variants for a given Boolean specification
allows us to find better performing circuits and that stochastic gate
assignment methods need to be properly adjusted in order to find the
best assignment. Second, we present a general circuit scoring scheme
that accounts for the limited accuracy of biological device models
including the variability across cells and show that circuits selected
according to this score exhibit higher robustness with respect to
parametric variations. If gate characteristics in a library are just
given in terms of intervals, we provide means to efficiently propagate
signals through such a circuit and compute corresponding scores. We
demonstrate the novel design approach using the Cello gate library
and 33 logic functions that were synthesized and implemented in vivo
recently (Nielsen, A., et al., Science, 2016, 352 (6281), DOI: 10.1126/science.aac7341). Across this set of functions, 32 of them can be improved by simply
considering structural variants yielding performance gains of up to
7.9-fold, whereas 22 of them can be improved with gains up to 26-fold
when selecting circuits according to the novel robustness score. We
furthermore report on the synergistic combination of the two proposed
improvements.
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Affiliation(s)
- Tobias Schladt
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Nicolai Engelmann
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Erik Kubaczka
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Christian Hochberger
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
| | - Heinz Koeppl
- Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt 64283, Germany
- Centre for Synthetic Biology, TU Darmstadt, Darmstadt 64283, Germany
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17
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Yang Y, Mao Y, Liu Y, Wang R, Lu H, Li H, Luo J, Wang M, Liao X, Ma H. GEDpm-cg: Genome Editing Automated Design Platform for Point Mutation Construction in Corynebacterium glutamicum. Front Bioeng Biotechnol 2021; 9:768289. [PMID: 34722482 PMCID: PMC8554027 DOI: 10.3389/fbioe.2021.768289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022] Open
Abstract
Advances in robotic system-assisted genome editing techniques and computer-aided design tools have significantly facilitated the development of microbial cell factories. Although multiple separate software solutions are available for vector DNA assembly, genome editing, and verification, by far there is still a lack of complete tool which can provide a one-stop service for the entire genome modification process. This makes the design of numerous genetic modifications, especially the construction of mutations that require strictly precise genetic manipulation, a laborious, time-consuming and error-prone process. Here, we developed a free online tool called GEDpm-cg for the design of genomic point mutations in C. glutamicum. The suicide plasmid-mediated counter-selection point mutation editing method and the overlap-based DNA assembly method were selected to ensure the editability of any single nucleotide at any locus in the C. glutamicum chromosome. Primers required for both DNA assembly of the vector for genetic modification and sequencing verification were provided as design results to meet all the experimental needs. An in-silico design task of over 10,000 single point mutations can be completed in 5 min. Finally, three independent point mutations were successfully constructed in C. glutamicum guided by GEDpm-cg, which confirms that the in-silico design results could accurately and seamlessly be bridged with in vivo or in vitro experiments. We believe this platform will provide a user-friendly, powerful and flexible tool for large-scale mutation analysis in the industrial workhorse C. glutamicum via robotic/software-assisted systems.
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Affiliation(s)
- Yi Yang
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Yufeng Mao
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Ye Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Ruoyu Wang
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Hui Lu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Haoran Li
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Jiahao Luo
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Meng Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Xiaoping Liao
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
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18
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Kitano H. Nobel Turing Challenge: creating the engine for scientific discovery. NPJ Syst Biol Appl 2021; 7:29. [PMID: 34145287 PMCID: PMC8213706 DOI: 10.1038/s41540-021-00189-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/03/2021] [Indexed: 12/15/2022] Open
Abstract
Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the "science of science" needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by "AI Scientists" may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond.
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Affiliation(s)
- Hiroaki Kitano
- The Systems Biology Institute, Tokyo, Japan; Okinawa Institute of Science and Technology Graduate School, Okinawa, Japan; Sony Computer Science Laboratories, Inc., Tokyo, Japan; Sony AI, Inc., Tokyo, Japan; and The Alan Turing Institute, London, UK.
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19
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Manrubia S, Cuesta JA, Aguirre J, Ahnert SE, Altenberg L, Cano AV, Catalán P, Diaz-Uriarte R, Elena SF, García-Martín JA, Hogeweg P, Khatri BS, Krug J, Louis AA, Martin NS, Payne JL, Tarnowski MJ, Weiß M. From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics. Phys Life Rev 2021; 38:55-106. [PMID: 34088608 DOI: 10.1016/j.plrev.2021.03.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/21/2022]
Abstract
Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
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Affiliation(s)
- Susanna Manrubia
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain; Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Biocomputación y Física de Sistemas Complejos (BiFi), Universidad de Zaragoza, Spain; UC3M-Santander Big Data Institute (IBiDat), Getafe, Madrid, Spain
| | - Jacobo Aguirre
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Centro de Astrobiología, CSIC-INTA, ctra. de Ajalvir km 4, 28850 Torrejón de Ardoz, Madrid, Spain
| | - Sebastian E Ahnert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | | | - Alejandro V Cano
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pablo Catalán
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Investigaciones Biomédicas "Alberto Sols" (UAM-CSIC), Madrid, Spain
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas, I(2)SysBio (CSIC-UV), València, Spain; The Santa Fe Institute, Santa Fe, NM, USA
| | | | - Paulien Hogeweg
- Theoretical Biology and Bioinformatics Group, Utrecht University, the Netherlands
| | - Bhavin S Khatri
- The Francis Crick Institute, London, UK; Department of Life Sciences, Imperial College London, London, UK
| | - Joachim Krug
- Institute for Biological Physics, University of Cologne, Köln, Germany
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK
| | - Nora S Martin
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Joshua L Payne
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Marcel Weiß
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
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20
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Schmidt M, Kubyshkin V. How To Quantify a Genetic Firewall? A Polarity-Based Metric for Genetic Code Engineering. Chembiochem 2021; 22:1268-1284. [PMID: 33231343 PMCID: PMC8049029 DOI: 10.1002/cbic.202000758] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/20/2020] [Indexed: 12/14/2022]
Abstract
Genetic code engineering aims to produce organisms that translate genetic information in a different way from that prescribed by the standard genetic code. This endeavor could eventually lead to genetic isolation, where an organism that operates under a different genetic code will not be able to transfer functional genes with other living species, thereby standing behind a genetic firewall. It is not clear however, how distinct the code should be, or how to measure the distance. We have developed a metric (Δcode ) where we assigned polarity indices (clog D7 ) to amino acids to calculate the distances between pairs of genetic codes. We then calculated the distance between a set of 204 genetic codes, including the 24 known distinct natural codes, 11 extreme-distance codes created computationally, nine theoretical special purpose codes from literature and 160 codes in which canonical amino acids were replaced by noncanonical chemical analogues. The metric can be used for building strategies towards creating semantically alienated organisms, and testing the strength of genetic firewalls. This metric provides the basis for a map of the genetic codes that could guide future efforts towards novel biochemical worlds, biosafety and deep barcoding applications.
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Affiliation(s)
| | - Vladimir Kubyshkin
- Department of ChemistryUniversity of ManitobaDysart Road 144WinnipegR3T 2N2Canada
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21
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Oberortner E, Evans R, Meng X, Nath S, Plahar H, Simirenko L, Tarver A, Deutsch S, Hillson NJ, Cheng JF. An Integrated Computer-Aided Design and Manufacturing Workflow for Synthetic Biology. Methods Mol Biol 2021; 2205:3-18. [PMID: 32809190 DOI: 10.1007/978-1-0716-0908-8_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Biological computer-aided design and manufacturing (bioCAD/CAM) tools facilitate the design and build processes of engineering biological systems using iterative design-build-test-learn (DBTL) cycles. In this book chapter, we highlight some of the bioCAD/CAM tools developed and used at the US Department of Energy (DOE) Joint Genome Institute (JGI), Joint BioEnergy Institute (JBEI), and Agile BioFoundry (ABF). We demonstrate the use of these bioCAD/CAM tools on a common workflow for designing and building a multigene pathway in a hierarchical fashion. Each tool presented in this book chapter is specifically tailored to support one or more specific steps in a workflow, can be integrated with the others into design and build workflows, and can be deployed at academic, government, or commercial entities.
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Affiliation(s)
- Ernst Oberortner
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA. .,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Robert Evans
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Xianwei Meng
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sangeeta Nath
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Hector Plahar
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Technology Division, DOE Joint BioEnergy Institute (JBEI), Emeryville, CA, USA
| | - Lisa Simirenko
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Angela Tarver
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Samuel Deutsch
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Nathan J Hillson
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Technology Division, DOE Joint BioEnergy Institute (JBEI), Emeryville, CA, USA.,DOE Agile BioFoundry, Emeryville, CA, USA.,TeselaGen Biotechnology, Inc., San Francisco, CA, USA
| | - Jan-Fang Cheng
- DOE Joint Genome Institute (JGI), Berkeley, CA, USA.,Environmental Genomics & Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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22
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Tenhaef N, Stella R, Frunzke J, Noack S. Automated Rational Strain Construction Based on High-Throughput Conjugation. ACS Synth Biol 2021; 10:589-599. [PMID: 33593066 DOI: 10.1021/acssynbio.0c00599] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Molecular cloning is the core of synthetic biology, as it comprises the assembly of DNA and its expression in target hosts. At present, however, cloning is most often a manual, time-consuming, and repetitive process that highly benefits from automation. The automation of a complete rational cloning procedure, i.e., from DNA creation to expression in the target host, involves the integration of different operations and machines. Examples of such workflows are sparse, especially when the design is rational (i.e., the DNA sequence design is fixed and not based on randomized libraries) and the target host is less genetically tractable (e.g., not sensitive to heat-shock transformation). In this study, an automated workflow for the rational construction of plasmids and their subsequent conjugative transfer into the biotechnological platform organism Corynebacterium glutamicum is presented. The whole workflow is accompanied by a custom-made software tool. As an application example, a rationally designed library of transcription factor-biosensors based on the regulator Lrp was constructed and characterized. A sensor with an improved dynamic range was obtained, and insights from the screening provided evidence for a dual regulator function of C. glutamicum Lrp.
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Affiliation(s)
- Niklas Tenhaef
- Institute of Bio- and Geosciences − IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Robert Stella
- Institute of Bio- and Geosciences − IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Julia Frunzke
- Institute of Bio- and Geosciences − IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences − IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich 52425, Germany
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich, Jülich 52425, Germany
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23
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Mao N, Aggarwal N, Poh CL, Cho BK, Kondo A, Liu C, Yew WS, Chang MW. Future trends in synthetic biology in Asia. ADVANCED GENETICS (HOBOKEN, N.J.) 2021; 2:e10038. [PMID: 36618442 PMCID: PMC9744534 DOI: 10.1002/ggn2.10038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 01/10/2021] [Accepted: 01/21/2021] [Indexed: 05/06/2023]
Abstract
Synthetic biology research and technology translation has garnered increasing interest from the governments and private investors in Asia, where the technology has great potential in driving a sustainable bio-based economy. This Perspective reviews the latest developments in the key enabling technologies of synthetic biology and its application in bio-manufacturing, medicine, food and agriculture in Asia. Asia-centric strengths in synthetic biology to grow the bio-based economy, such as advances in genome editing and the presence of biofoundries combined with the availability of natural resources and vast markets, are also highlighted. The potential barriers to the sustainable development of the field, including inadequate infrastructure and policies, with suggestions to overcome these by building public-private partnerships, more effective multi-lateral collaborations and well-developed governance framework, are presented. Finally, the roles of technology, education and regulation in mitigating potential biosecurity risks are examined. Through these discussions, stakeholders from different groups, including academia, industry and government, are expectantly better positioned to contribute towards the establishment of innovation and bio-economy hubs in Asia.
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Affiliation(s)
- Ning Mao
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI)National University of SingaporeSingaporeSingapore
| | - Nikhil Aggarwal
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI)National University of SingaporeSingaporeSingapore
- Synthetic Biology Translational Research Program and Department of Biochemistry, Yong Loo Ling School of MedicineNational University of SingaporeSingaporeSingapore
| | - Chueh Loo Poh
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI)National University of SingaporeSingaporeSingapore
- Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore
| | - Byung Kwan Cho
- Department of Biological Sciences, and KI for the BioCenturyKorea Advanced Institute of Science and TechnologyDaejeonSouth Korea
| | - Akihiko Kondo
- Graduate School of Science, Technology and Innovation, and Engineering Biology Research CenterKobe UniversityKobeJapan
| | - Chenli Liu
- CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
| | - Wen Shan Yew
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI)National University of SingaporeSingaporeSingapore
- Synthetic Biology Translational Research Program and Department of Biochemistry, Yong Loo Ling School of MedicineNational University of SingaporeSingaporeSingapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI)National University of SingaporeSingaporeSingapore
- Synthetic Biology Translational Research Program and Department of Biochemistry, Yong Loo Ling School of MedicineNational University of SingaporeSingaporeSingapore
- Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore
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24
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Tas H, Grozinger L, Stoof R, de Lorenzo V, Goñi-Moreno Á. Contextual dependencies expand the re-usability of genetic inverters. Nat Commun 2021; 12:355. [PMID: 33441561 PMCID: PMC7806840 DOI: 10.1038/s41467-020-20656-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 12/02/2020] [Indexed: 01/29/2023] Open
Abstract
The implementation of Boolean logic circuits in cells have become a very active field within synthetic biology. Although these are mostly focussed on the genetic components alone, the context in which the circuit performs is crucial for its outcome. We characterise 20 genetic NOT logic gates in up to 7 bacterial-based contexts each, to generate 135 different functions. The contexts we focus on are combinations of four plasmid backbones and three hosts, two Escherichia coli and one Pseudomonas putida strains. Each gate shows seven different dynamic behaviours, depending on the context. That is, gates can be fine-tuned by changing only contextual parameters, thus improving the compatibility between gates. Finally, we analyse portability by measuring, scoring, and comparing gate performance across contexts. Rather than being a limitation, we argue that the effect of the genetic background on synthetic constructs expands functionality, and advocate for considering context as a fundamental design parameter.
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Affiliation(s)
- Huseyin Tas
- grid.428469.50000 0004 1794 1018Systems Biology Department, Centro Nacional de Biotecnologia-CSIC, Campus de Cantoblanco, Madrid, 28049 Spain
| | - Lewis Grozinger
- grid.1006.70000 0001 0462 7212School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5TG UK
| | - Ruud Stoof
- grid.1006.70000 0001 0462 7212School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5TG UK
| | - Victor de Lorenzo
- grid.428469.50000 0004 1794 1018Systems Biology Department, Centro Nacional de Biotecnologia-CSIC, Campus de Cantoblanco, Madrid, 28049 Spain
| | - Ángel Goñi-Moreno
- grid.1006.70000 0001 0462 7212School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5TG UK ,grid.419190.40000 0001 2300 669XCentro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politénica de Madrid (UPM), Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón, Madrid, Spain
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25
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Holowko MB, Frow EK, Reid JC, Rourke M, Vickers CE. Building a biofoundry. Synth Biol (Oxf) 2020; 6:ysaa026. [PMID: 33817343 DOI: 10.1093/synbio/ysaa026] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/26/2020] [Accepted: 11/12/2020] [Indexed: 01/21/2023] Open
Abstract
A biofoundry provides automation and analytics infrastructure to support the engineering of biological systems. It allows scientists to perform synthetic biology and aligned experimentation on a high-throughput scale, massively increasing the solution space that can be examined for any given problem or question. However, establishing a biofoundry is a challenging undertaking, with numerous technical and operational considerations that must be addressed. Using collated learnings, here we outline several considerations that should be addressed prior to and during establishment. These include drivers for establishment, institutional models, funding and revenue models, personnel, hardware and software, data management, interoperability, client engagement and biosecurity issues. The high cost of establishment and operation means that developing a long-term business model for biofoundry sustainability in the context of funding frameworks, actual and potential client base, and costing structure is critical. Moreover, since biofoundries are leading a conceptual shift in experimental design for bioengineering, sustained outreach and engagement with the research community are needed to grow the client base. Recognition of the significant, long-term financial investment required and an understanding of the complexities of operationalization is critical for a sustainable biofoundry venture. To ensure state-of-the-art technology is integrated into planning, extensive engagement with existing facilities and community groups, such as the Global Biofoundries Alliance, is recommended.
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Affiliation(s)
- Maciej B Holowko
- CSIRO Synthetic Biology Future Science Platform, CSIRO Land and Water, Brisbane, QLD 4102, Australia
| | - Emma K Frow
- School for the Future of Innovation in Society and School of Biological & Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA
| | - Janet C Reid
- CSIRO Synthetic Biology Future Science Platform, CSIRO Land and Water, Brisbane, QLD 4102, Australia
| | - Michelle Rourke
- CSIRO Synthetic Biology Future Science Platform, CSIRO Land and Water, Brisbane, QLD 4102, Australia.,Law Futures Centre, Griffith Law School, Griffith University, Nathan, QLD 4111, Australia
| | - Claudia E Vickers
- CSIRO Synthetic Biology Future Science Platform, CSIRO Land and Water, Brisbane, QLD 4102, Australia.,ARC Centre of Excellence in Synthetic Biology, Queensland University of Technology, Brisbane, QLD 4001, Australia
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26
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Young R, Haines M, Storch M, Freemont PS. Combinatorial metabolic pathway assembly approaches and toolkits for modular assembly. Metab Eng 2020; 63:81-101. [PMID: 33301873 DOI: 10.1016/j.ymben.2020.12.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 11/16/2020] [Accepted: 12/03/2020] [Indexed: 12/18/2022]
Abstract
Synthetic Biology is a rapidly growing interdisciplinary field that is primarily built upon foundational advances in molecular biology combined with engineering design principles such as modularity and interoperability. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies and methodological advances. A key concept driving the field is the Design-Build-Test-Learn cycle which provides a systematic framework for building new biological systems. One major application area for synthetic biology is biosynthetic pathway engineering that requires the modular assembly of different genetic regulatory elements and biosynthetic enzymes. In this review we provide an overview of modular DNA assembly and describe and compare the plethora of in vitro and in vivo assembly methods for combinatorial pathway engineering. Considerations for part design and methods for enzyme balancing are also presented, and we briefly discuss alternatives to intracellular pathway assembly including microbial consortia and cell-free systems for biosynthesis. Finally, we describe computational tools and automation for pathway design and assembly and argue that a deeper understanding of the many different variables of genetic design, pathway regulation and cellular metabolism will allow more predictive pathway design and engineering.
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Affiliation(s)
- Rosanna Young
- Department of Infectious Disease, Sir Alexander Fleming Building, South Kensington Campus, Imperial College London, SW7 2AZ, UK
| | - Matthew Haines
- Department of Infectious Disease, Sir Alexander Fleming Building, South Kensington Campus, Imperial College London, SW7 2AZ, UK
| | - Marko Storch
- Department of Infectious Disease, Sir Alexander Fleming Building, South Kensington Campus, Imperial College London, SW7 2AZ, UK; London Biofoundry, Imperial College Translation & Innovation Hub, London, W12 0BZ, UK
| | - Paul S Freemont
- Department of Infectious Disease, Sir Alexander Fleming Building, South Kensington Campus, Imperial College London, SW7 2AZ, UK; London Biofoundry, Imperial College Translation & Innovation Hub, London, W12 0BZ, UK; UK DRI Care Research and Technology Centre, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.
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27
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Malcı K, Walls LE, Rios-Solis L. Multiplex Genome Engineering Methods for Yeast Cell Factory Development. Front Bioeng Biotechnol 2020; 8:589468. [PMID: 33195154 PMCID: PMC7658401 DOI: 10.3389/fbioe.2020.589468] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 10/07/2020] [Indexed: 12/12/2022] Open
Abstract
As biotechnological applications of synthetic biology tools including multiplex genome engineering are expanding rapidly, the construction of strategically designed yeast cell factories becomes increasingly possible. This is largely due to recent advancements in genome editing methods like CRISPR/Cas tech and high-throughput omics tools. The model organism, baker's yeast (Saccharomyces cerevisiae) is an important synthetic biology chassis for high-value metabolite production. Multiplex genome engineering approaches can expedite the construction and fine tuning of effective heterologous pathways in yeast cell factories. Numerous multiplex genome editing techniques have emerged to capitalize on this recently. This review focuses on recent advancements in such tools, such as delta integration and rDNA cluster integration coupled with CRISPR-Cas tools to greatly enhance multi-integration efficiency. Examples of pre-placed gate systems which are an innovative alternative approach for multi-copy gene integration were also reviewed. In addition to multiple integration studies, multiplexing of alternative genome editing methods are also discussed. Finally, multiplex genome editing studies involving non-conventional yeasts and the importance of automation for efficient cell factory design and construction are considered. Coupling the CRISPR/Cas system with traditional yeast multiplex genome integration or donor DNA delivery methods expedites strain development through increased efficiency and accuracy. Novel approaches such as pre-placing synthetic sequences in the genome along with improved bioinformatics tools and automation technologies have the potential to further streamline the strain development process. In addition, the techniques discussed to engineer S. cerevisiae, can be adapted for use in other industrially important yeast species for cell factory development.
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Affiliation(s)
- Koray Malcı
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom.,Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, United Kingdom
| | - Laura E Walls
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom.,Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, United Kingdom
| | - Leonardo Rios-Solis
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom.,Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, United Kingdom
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28
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Davey JA, Wilson CJ. Engineered signal-coupled inducible promoters: measuring the apparent RNA-polymerase resource budget. Nucleic Acids Res 2020; 48:9995-10012. [PMID: 32890400 PMCID: PMC7515704 DOI: 10.1093/nar/gkaa734] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 08/18/2020] [Accepted: 08/24/2020] [Indexed: 02/07/2023] Open
Abstract
Inducible promoters are a central regulatory component in synthetic biology, metabolic engineering, and protein production for laboratory and commercial uses. Many of these applications utilize two or more exogenous promoters, imposing a currently unquantifiable metabolic burden on the living system. Here, we engineered a collection of inducible promoters (regulated by LacI-based transcription factors) that maximize the free-state of endogenous RNA polymerase (RNAP). We leveraged this collection of inducible promotors to construct simple two-channel logical controls that enabled us to measure metabolic burden – as it relates to RNAP resource partitioning. The two-channel genetic circuits utilized sets of signal-coupled transcription factors that regulate cognate inducible promoters in a coordinated logical fashion. With this fundamental genetic architecture, we evaluated the performance of each inducible promoter as discrete operations, and as coupled systems to evaluate and quantify the effects of resource partitioning. Obtaining the ability to systematically and accurately measure the apparent RNA-polymerase resource budget will enable researchers to design more robust genetic circuits, with significantly higher fidelity. Moreover, this study presents a workflow that can be used to better understand how living systems adapt RNAP resources, via the complementary pairing of constitutive and regulated promoters that vary in strength.
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Affiliation(s)
- James A Davey
- Georgia Institute of Technology, School of Chemical & Biomolecular Engineering, 311 Ferst Drive, Atlanta, GA 30332-0100, USA
| | - Corey J Wilson
- Georgia Institute of Technology, School of Chemical & Biomolecular Engineering, 311 Ferst Drive, Atlanta, GA 30332-0100, USA
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29
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Lakin MR, Phillips A. Domain-Specific Programming Languages for Computational Nucleic Acid Systems. ACS Synth Biol 2020; 9:1499-1513. [PMID: 32589838 DOI: 10.1021/acssynbio.0c00050] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The construction of models of system behavior is of great importance throughout science and engineering. In bioengineering and bionanotechnology, these often take the form of dynamic models that specify the evolution of different species over time. To ensure that scientific observations and conclusions are consistent and that systems can be reliably engineered on the basis of model predictions, it is important that models of biomolecular systems can be constructed in a reliable, principled, and efficient manner. This review focuses on efforts to address this need by using domain-specific programming languages as the basis for custom design tools for researchers working on computational nucleic acid devices, where a domain-specific language is simply a programming language tailored to a particular application domain. The underlying thesis of our review is that there is a continuum of practical implementation strategies for computational nucleic acid systems, which can all benefit from appropriate domain-specific languages and software design tools. We emphasize the need for specialized yet flexible tools that can be realized using domain-specific languages that compile to more general-purpose representations.
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Affiliation(s)
- Matthew R. Lakin
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico 87131, United States
- Department of Chemical & Biological Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
- Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
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30
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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.
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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
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31
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Robinson CJ, Carbonell P, Jervis AJ, Yan C, Hollywood KA, Dunstan MS, Currin A, Swainston N, Spiess R, Taylor S, Mulherin P, Parker S, Rowe W, Matthews NE, Malone KJ, Le Feuvre R, Shapira P, Barran P, Turner NJ, Micklefield J, Breitling R, Takano E, Scrutton NS. Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers. Metab Eng 2020; 60:168-182. [PMID: 32335188 PMCID: PMC7225752 DOI: 10.1016/j.ymben.2020.04.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 04/09/2020] [Accepted: 04/16/2020] [Indexed: 12/11/2022]
Abstract
Bio-based production of industrial chemicals using synthetic biology can provide alternative green routes from renewable resources, allowing for cleaner production processes. To efficiently produce chemicals on-demand through microbial strain engineering, biomanufacturing foundries have developed automated pipelines that are largely compound agnostic in their time to delivery. Here we benchmark the capabilities of a biomanufacturing pipeline to enable rapid prototyping of microbial cell factories for the production of chemically diverse industrially relevant material building blocks. Over 85 days the pipeline was able to produce 17 potential material monomers and key intermediates by combining 160 genetic parts into 115 unique biosynthetic pathways. To explore the scale-up potential of our prototype production strains, we optimized the enantioselective production of mandelic acid and hydroxymandelic acid, achieving gram-scale production in fed-batch fermenters. The high success rate in the rapid design and prototyping of microbially-produced material building blocks reveals the potential role of biofoundries in leading the transition to sustainable materials production.
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Affiliation(s)
- Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Adrian J Jervis
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Cunyu Yan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Katherine A Hollywood
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Mark S Dunstan
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Andrew Currin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Neil Swainston
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Reynard Spiess
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Sandra Taylor
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Paul Mulherin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Steven Parker
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - William Rowe
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Nicholas E Matthews
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Manchester Institute of Innovation Research, Alliance Manchester Business School, The University of Manchester, Manchester, M15 6PB, UK.
| | - Kirk J Malone
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Rosalind Le Feuvre
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK.
| | - Philip Shapira
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Manchester Institute of Innovation Research, Alliance Manchester Business School, The University of Manchester, Manchester, M15 6PB, UK.
| | - Perdita Barran
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Nicholas J Turner
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Jason Micklefield
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Eriko Takano
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN, UK; Department of Chemistry, The University of Manchester, Manchester, M13 9PL, UK.
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32
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DeNies MS, Liu AP, Schnell S. Are the biomedical sciences ready for synthetic biology? Biomol Concepts 2020; 11:23-31. [PMID: 31982863 DOI: 10.1515/bmc-2020-0003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 01/02/2020] [Indexed: 11/15/2022] Open
Abstract
The ability to construct a functional system from its individual components is foundational to understanding how it works. Synthetic biology is a broad field that draws from principles of engineering and computer science to create new biological systems or parts with novel function. While this has drawn well-deserved acclaim within the biotechnology community, application of synthetic biology methodologies to study biological systems has potential to fundamentally change how biomedical research is conducted by providing researchers with improved experimental control. While the concepts behind synthetic biology are not new, we present evidence supporting why the current research environment is conducive for integration of synthetic biology approaches within biomedical research. In this perspective we explore the idea of synthetic biology as a discovery science research tool and provide examples of both top-down and bottom-up approaches that have already been used to answer important physiology questions at both the organismal and molecular level.
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Affiliation(s)
- Maxwell S DeNies
- Cellular and Molecular Biology Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Allen P Liu
- Cellular and Molecular Biology Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.,Department of Biophysics, University of Michigan, Ann Arbor, Michigan, USA
| | - Santiago Schnell
- Cellular and Molecular Biology Graduate Program, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA
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33
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Charlton SGV, White MA, Jana S, Eland LE, Jayathilake PG, Burgess JG, Chen J, Wipat A, Curtis TP. Regulating, Measuring, and Modeling the Viscoelasticity of Bacterial Biofilms. J Bacteriol 2019; 201:e00101-19. [PMID: 31182499 PMCID: PMC6707926 DOI: 10.1128/jb.00101-19] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Biofilms occur in a broad range of environments under heterogeneous physicochemical conditions, such as in bioremediation plants, on surfaces of biomedical implants, and in the lungs of cystic fibrosis patients. In these scenarios, biofilms are subjected to shear forces, but the mechanical integrity of these aggregates often prevents their disruption or dispersal. Biofilms' physical robustness is the result of the multiple biopolymers secreted by constituent microbial cells which are also responsible for numerous biological functions. A better understanding of the role of these biopolymers and their response to dynamic forces is therefore crucial for understanding the interplay between biofilm structure and function. In this paper, we review experimental techniques in rheology, which help quantify the viscoelasticity of biofilms, and modeling approaches from soft matter physics that can assist our understanding of the rheological properties. We describe how these methods could be combined with synthetic biology approaches to control and investigate the effects of secreted polymers on the physical properties of biofilms. We argue that without an integrated approach of the three disciplines, the links between genetics, composition, and interaction of matrix biopolymers and the viscoelastic properties of biofilms will be much harder to uncover.
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Affiliation(s)
- Samuel G V Charlton
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Michael A White
- Interdisciplinary Computing & Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Saikat Jana
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Lucy E Eland
- Interdisciplinary Computing & Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - J Grant Burgess
- School of Natural & Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jinju Chen
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anil Wipat
- Interdisciplinary Computing & Complex BioSystems Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Thomas P Curtis
- School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
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34
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Kitney R, Adeogun M, Fujishima Y, Goñi-Moreno Á, Johnson R, Maxon M, Steedman S, Ward S, Winickoff D, Philp J. Enabling the Advanced Bioeconomy through Public Policy Supporting Biofoundries and Engineering Biology. Trends Biotechnol 2019; 37:917-920. [DOI: 10.1016/j.tibtech.2019.03.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 03/14/2019] [Accepted: 03/28/2019] [Indexed: 01/08/2023]
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35
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Nora LC, Westmann CA, Guazzaroni ME, Siddaiah C, Gupta VK, Silva-Rocha R. Recent advances in plasmid-based tools for establishing novel microbial chassis. Biotechnol Adv 2019; 37:107433. [PMID: 31437573 DOI: 10.1016/j.biotechadv.2019.107433] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 07/11/2019] [Accepted: 08/16/2019] [Indexed: 12/28/2022]
Abstract
A key challenge for domesticating alternative cultivable microorganisms with biotechnological potential lies in the development of innovative technologies. Within this framework, a myriad of genetic tools has flourished, allowing the design and manipulation of complex synthetic circuits and genomes to become the general rule in many laboratories rather than the exception. More recently, with the development of novel technologies such as DNA automated synthesis/sequencing and powerful computational tools, molecular biology has entered the synthetic biology era. In the beginning, most of these technologies were established in traditional microbial models (known as chassis in the synthetic biology framework) such as Escherichia coli and Saccharomyces cerevisiae, enabling fast advances in the field and the validation of fundamental proofs of concept. However, it soon became clear that these organisms, although extremely useful for prototyping many genetic tools, were not ideal for a wide range of biotechnological tasks due to intrinsic limitations in their molecular/physiological properties. Over the last decade, researchers have been facing the great challenge of shifting from these model systems to non-conventional chassis with endogenous capacities for dealing with specific tasks. The key to address these issues includes the generation of narrow and broad host plasmid-based molecular tools and the development of novel methods for engineering genomes through homologous recombination systems, CRISPR/Cas9 and other alternative methods. Here, we address the most recent advances in plasmid-based tools for the construction of novel cell factories, including a guide for helping with "build-your-own" microbial host.
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Affiliation(s)
- Luísa Czamanski Nora
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo 14049-900, Brazil
| | - Cauã Antunes Westmann
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo 14049-900, Brazil
| | - María-Eugenia Guazzaroni
- Faculty of Philosophy, Science and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo 14049-900, Brazil
| | | | - Vijai Kumar Gupta
- ERA Chair of Green Chemistry, Department of Chemistry and Biotechnology, School of Science, Tallinn University of Technology, 12618 Tallinn, Estonia
| | - Rafael Silva-Rocha
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo 14049-900, Brazil.
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Misirli G, Nguyen T, McLaughlin JA, Vaidyanathan P, Jones TS, Densmore D, Myers C, Wipat A. A Computational Workflow for the Automated Generation of Models of Genetic Designs. ACS Synth Biol 2019; 8:1548-1559. [PMID: 29782151 DOI: 10.1021/acssynbio.7b00459] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.
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Affiliation(s)
- Göksel Misirli
- School of Computing and Mathematics, Keele University, Staffordshire, U.K
| | - Tramy Nguyen
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | | | - Prashant Vaidyanathan
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Timothy S. Jones
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Douglas Densmore
- Department of Electrical and Computer Engineering Boston University, Boston, Massachusetts 02215, United States
| | - Chris Myers
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States
| | - Anil Wipat
- ICOS, School of Computing, Newcastle University, Newcastle upon Tyne, U.K
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Hallinan JS, Wipat A, Kitney R, Woods S, Taylor K, Goñi‐Moreno A. Future‐proofing synthetic biology: educating the next generation. ENGINEERING BIOLOGY 2019. [DOI: 10.1049/enb.2019.0001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
| | - Anil Wipat
- School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Richard Kitney
- Department of BioengineeringImperial College LondonLondonUK
| | - Simon Woods
- Policy, Ethics and Life Sciences (PEALS) Research CentreNewcastle UniversityNewcastle upon TyneUK
| | - Ken Taylor
- Policy, Ethics and Life Sciences (PEALS) Research CentreNewcastle UniversityNewcastle upon TyneUK
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38
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Walsh DI, Pavan M, Ortiz L, Wick S, Bobrow J, Guido NJ, Leinicke S, Fu D, Pandit S, Qin L, Carr PA, Densmore D. Standardizing Automated DNA Assembly: Best Practices, Metrics, and Protocols Using Robots. SLAS Technol 2019; 24:282-290. [PMID: 30768372 PMCID: PMC6819997 DOI: 10.1177/2472630318825335] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The advancement of synthetic biology requires the ability to create new DNA sequences to produce unique behaviors in biological systems. Automation is increasingly employed to carry out well-established assembly methods of DNA fragments in a multiplexed, high-throughput fashion, allowing many different configurations to be tested simultaneously. However, metrics are required to determine when automation is warranted based on factors such as assembly methodology, protocol details, and number of samples. The goal of our synthetic biology automation work is to develop and test protocols, hardware, and software to investigate and optimize DNA assembly through quantifiable metrics. We performed a parameter analysis of DNA assembly to develop a standardized, highly efficient, and reproducible MoClo protocol, suitable to be used both manually and with liquid-handling robots. We created a key DNA assembly metric (Q-metric) to characterize a given automation method's advantages over conventional manual manipulations with regard to researchers' highest-priority parameters: output, cost, and time. A software tool called Puppeteer was developed to formally capture these metrics, help define the assembly design, and provide human and robotic liquid-handling instructions. Altogether, we contribute to a growing foundation of standardizing practices, metrics, and protocols for automating DNA assembly.
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Affiliation(s)
- David I. Walsh
- Bioengineering Systems and Technologies, MIT-Lincoln Laboratory, Lexington, MA, USA
- Synthetic Biology Center at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marilene Pavan
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Luis Ortiz
- Biological Design Center, Boston University, Boston, MA, USA
- Molecular Biology, Cell Biology & Biochemistry, Boston University, Boston, MA, USA
| | - Scott Wick
- Bioengineering Systems and Technologies, MIT-Lincoln Laboratory, Lexington, MA, USA
- Synthetic Biology Center at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Johanna Bobrow
- Bioengineering Systems and Technologies, MIT-Lincoln Laboratory, Lexington, MA, USA
- Synthetic Biology Center at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas J. Guido
- Bioengineering Systems and Technologies, MIT-Lincoln Laboratory, Lexington, MA, USA
- Synthetic Biology Center at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sarah Leinicke
- Hariri Institute for Computing, Boston University, Boston, MA, USA
| | - Dany Fu
- Hariri Institute for Computing, Boston University, Boston, MA, USA
| | - Shreya Pandit
- Hariri Institute for Computing, Boston University, Boston, MA, USA
| | - Lucy Qin
- Hariri Institute for Computing, Boston University, Boston, MA, USA
| | - Peter A. Carr
- Bioengineering Systems and Technologies, MIT-Lincoln Laboratory, Lexington, MA, USA
- Synthetic Biology Center at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
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Lugagne JB, Dunlop MJ. Cell-machine interfaces for characterizing gene regulatory network dynamics. ACTA ACUST UNITED AC 2019; 14:1-8. [PMID: 31579842 DOI: 10.1016/j.coisb.2019.01.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Gene regulatory networks and the dynamic responses they produce offer a wealth of information about how biological systems process information about their environment. Recently, researchers interested in dissecting these networks have been outsourcing various parts of their experimental workflow to computers. Here we review how, using microfluidic or optogenetic tools coupled with fluorescence imaging, it is now possible to interface cells and computers. These platforms enable scientists to perform informative dynamic stimulations of genetic pathways and monitor their reaction. It is also possible to close the loop and regulate genes in real time, providing an unprecedented view of how signals propagate through the network. Finally, we outline new tools that can be used within the framework of cell-machine interfaces.
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Affiliation(s)
- Jean-Baptiste Lugagne
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.,Biological Design Center, Boston University, Boston, MA, USA
| | - Mary J Dunlop
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.,Biological Design Center, Boston University, Boston, MA, USA
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Exley K, Reynolds CR, Suckling L, Chee SM, Tsipa A, Freemont PS, McClymont D, Kitney RI. Utilising datasheets for the informed automated design and build of a synthetic metabolic pathway. J Biol Eng 2019; 13:8. [PMID: 30675181 PMCID: PMC6339355 DOI: 10.1186/s13036-019-0141-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 01/07/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The automation of modular cloning methodologies permits the assembly of many genetic designs. Utilising characterised biological parts aids in the design and redesign of genetic pathways. The characterisation information held on datasheets can be used to determine whether a biological part meets the design requirements. To manage the design of genetic pathways, researchers have turned to modelling-based computer aided design software tools. RESULT An automated workflow has been developed for the design and build of heterologous metabolic pathways. In addition, to demonstrate the powers of electronic datasheets we have developed software which can transfer part information from a datasheet to the Design of Experiment software JMP. To this end we were able to use Design of Experiment software to rationally design and test randomised samples from the design space of a lycopene pathway in E. coli. This pathway was optimised by individually modulating the promoter strength, RBS strength, and gene order targets. CONCLUSION The use of standardised and characterised biological parts will empower a design-oriented synthetic biology for the forward engineering of heterologous expression systems. A Design of Experiment approach streamlines the design-build-test cycle to achieve optimised solutions in biodesign. Developed automated workflows provide effective transfer of information between characterised information (in the form of datasheets) and DoE software.
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Affiliation(s)
- Kealan Exley
- Department of Bioengineering, Imperial College London, London, UK
- Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
| | - Christopher Robert Reynolds
- Department of Bioengineering, Imperial College London, London, UK
- Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
| | - Lorna Suckling
- Department of Bioengineering, Imperial College London, London, UK
- The London DNA Foundry, Imperial College London, London, UK
| | - Soo Mei Chee
- Department of Bioengineering, Imperial College London, London, UK
- SynbiCITE, Imperial College London, London, UK
| | - Argyro Tsipa
- Department of Bioengineering, Imperial College London, London, UK
- SynbiCITE, Imperial College London, London, UK
| | - Paul S. Freemont
- SynbiCITE, Imperial College London, London, UK
- Section of Structural Biology, Department of Medicine, Imperial College London, London, UK
| | | | - Richard Ian Kitney
- Department of Bioengineering, Imperial College London, London, UK
- SynbiCITE, Imperial College London, London, UK
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Davies JA. Real-World Synthetic Biology: Is It Founded on an Engineering Approach, and Should It Be? Life (Basel) 2019; 9:life9010006. [PMID: 30621107 PMCID: PMC6463249 DOI: 10.3390/life9010006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 12/20/2018] [Accepted: 12/29/2018] [Indexed: 12/22/2022] Open
Abstract
Authors often assert that a key feature of 21st-century synthetic biology is its use of an 'engineering approach'; design using predictive models, modular architecture, construction using well-characterized standard parts, and rigorous testing using standard metrics. This article examines whether this is, or even should be, the case. A brief survey of synthetic biology projects that have reached, or are near to, commercial application outside laboratories shows that they showed very few of these attributes. Instead, they featured much trial and error, and the use of specialized, custom components and assays. What is more, consideration of the special features of living systems suggest that a conventional engineering approach will often not be helpful. The article concludes that the engineering approach may be useful in some projects, but it should not be used to define or constrain synthetic biological endeavour, and that in fact the conventional engineering has more to gain by expanding and embracing more biological ways of working.
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Affiliation(s)
- Jamie A Davies
- UK Centre for Mammalian Synthetic Biology, University of Edinburgh, Edinburgh EH8 9YL, UK.
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Abstract
Engineering synthetic gene regulatory circuits proceeds through iterative cycles of design, building, and testing. Initial circuit designs must rely on often-incomplete models of regulation established by fields of reductive inquiry—biochemistry and molecular and systems biology. As differences in designed and experimentally observed circuit behavior are inevitably encountered, investigated, and resolved, each turn of the engineering cycle can force a resynthesis in understanding of natural network function. Here, we outline research that uses the process of gene circuit engineering to advance biological discovery. Synthetic gene circuit engineering research has not only refined our understanding of cellular regulation but furnished biologists with a toolkit that can be directed at natural systems to exact precision manipulation of network structure. As we discuss, using circuit engineering to predictively reorganize, rewire, and reconstruct cellular regulation serves as the ultimate means of testing and understanding how cellular phenotype emerges from systems-level network function.
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Affiliation(s)
- Caleb J. Bashor
- Institute for Medical Engineering and Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;,
| | - James J. Collins
- Institute for Medical Engineering and Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA;,
- Harvard–MIT Program in Health Sciences and Technology, Cambridge, Massachusetts 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, USA
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