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Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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2
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Xu S, Gao S, An Y. Research progress of engineering microbial cell factories for pigment production. Biotechnol Adv 2023; 65:108150. [PMID: 37044266 DOI: 10.1016/j.biotechadv.2023.108150] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 03/14/2023] [Accepted: 04/06/2023] [Indexed: 04/14/2023]
Abstract
Pigments are widely used in people's daily life, such as food additives, cosmetics, pharmaceuticals, textiles, etc. In recent years, the natural pigments produced by microorganisms have attracted increased attention because these processes cannot be affected by seasons like the plant extraction methods, and can also avoid the environmental pollution problems caused by chemical synthesis. Synthetic biology and metabolic engineering have been used to construct and optimize metabolic pathways for production of natural pigments in cellular factories. Building microbial cell factories for synthesis of natural pigments has many advantages, including well-defined genetic background of the strains, high-density and rapid culture of cells, etc. Until now, the technical means about engineering microbial cell factories for pigment production and metabolic regulation processes have not been systematically analyzed and summarized. Therefore, the studies about construction, modification and regulation of synthetic pathways for microbial synthesis of pigments in recent years have been reviewed, aiming to provide an up-to-date summary of engineering strategies for microbial synthesis of natural pigments including carotenoids, melanins, riboflavins, azomycetes and quinones. This review should provide new ideas for further improving microbial production of natural pigments in the future.
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Affiliation(s)
- Shumin Xu
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China; College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Song Gao
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China
| | - Yingfeng An
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China; College of Food Science, Shenyang Agricultural University, Shenyang, China; Shenyang Key Laboratory of Microbial Resources Mining and Molecular Breeding, Shenyang, China; Liaoning Provincial Key Laboratory of Agricultural Biotechnology, Shenyang, China.
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Imani M, Mohajeri N, Rastegar M, Zarghami N. Synthesis and Characterization of N-rich Fluorescent Bio-dots as a Reporter in the Design of Dual-labeled FRET Probe for TaqMan PCR: a Feasibility Study. Biotechnol Appl Biochem 2022; 70:645-658. [PMID: 35900086 DOI: 10.1002/bab.2387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 07/12/2022] [Indexed: 11/07/2022]
Abstract
DNA-based analytical techniques have provided an advantageous sensing assay in the realm of biotechnology. Bio-inspired fluorescent nanodots are a novel type of biological staining agent with excellent optical properties widely used for cellular imaging and diagnostics. In the present research, we successfully synthesized bio-dots with excellent optical properties and high-quantum yield from DNA sodium salt through the hydrothermal method. We conjugated the bio-dots with 3' Eclipse® Dark Quencher (Eclipse) labeled single strand oligodeoxyribonucleotide according to carbodiimide chemistry, to design a fluorescence resonance energy transfer (FRET) probe. The results confirmed the prosperous synthesis and surface functionalization of the bio-dot. Analysis of size, zeta potential, and FTIR spectroscopy verified successful bioconjugation of the bio-dots with probes. UV-Visibility analysis and fluorescence intensity profile of the bio-dot and bio-dot@probes represented a concentration-dependent quenching of fluorescent signal of bio-dot by Eclipse after probe conjugation. The results demonstrated that TaqMan PCR was not feasible using the designed bio-dot@probes. Our results indicated that bio-dot can be used as an efficient fluorescent tag in the design of fluorescently labeled oligonucleotides with high biocompatibility and optical features. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mahsa Imani
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nasrin Mohajeri
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mojgan Rastegar
- Department of Biochemistry and Medical Genetics, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Nosratollah Zarghami
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Clinical Biochemistry and Laboratory Medicine, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biochemistry, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
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PlasmidMaker is a versatile, automated, and high throughput end-to-end platform for plasmid construction. Nat Commun 2022; 13:2697. [PMID: 35577775 PMCID: PMC9110713 DOI: 10.1038/s41467-022-30355-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/28/2022] [Indexed: 01/01/2023] Open
Abstract
Plasmids are used extensively in basic and applied biology. However, design and construction of plasmids, specifically the ones carrying complex genetic information, remains one of the most time-consuming, labor-intensive, and rate-limiting steps in performing sophisticated biological experiments. Here, we report the development of a versatile, robust, automated end-to-end platform named PlasmidMaker that allows error-free construction of plasmids with virtually any sequences in a high throughput manner. This platform consists of a most versatile DNA assembly method using Pyrococcus furiosus Argonaute (PfAgo)-based artificial restriction enzymes, a user-friendly frontend for plasmid design, and a backend that streamlines the workflow and integration with a robotic system. As a proof of concept, we used this platform to generate 101 plasmids from six different species ranging from 5 to 18 kb in size from up to 11 DNA fragments. PlasmidMaker should greatly expand the potential of synthetic biology. Despite their broad utility, design and construction of plasmids remains laborious and time-consuming. Here the authors report a robust, versatile, and automated end-to-end platform that enables scarless construction of virtually any plasmid.
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Casas A, Bultelle M, Motraghi C, Kitney R. PASIV: A Pooled Approach-Based Workflow to Overcome Toxicity-Induced Design of Experiments Failures and Inefficiencies. ACS Synth Biol 2022; 11:1272-1291. [PMID: 35261238 PMCID: PMC8938949 DOI: 10.1021/acssynbio.1c00562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
We present here a
newly developed workflow—which we have
called PASIV—designed to provide a solution to a practical
problem with design of experiments (DoE) methodology: i.e., what can
be done if the scoping phase of the DoE cycle is severely hampered
by burden and toxicity issues (caused by either the metabolite or
an intermediary), making it unreliable or impossible to proceed to
the screening phase? PASIV—standing for pooled approach, screening,
identification, and visualization—was designed so the (viable)
region of interest can be made to appear through an interplay between
biology and software. This was achieved by combining multiplex construction
in a pooled approach (one-pot reaction) with a viability assay and
with a range of bioinformatics tools (including a novel construct
matching tool). PASIV was tested on the exemplar of the lycopene pathway—under
stressful constitutive expression—yielding a region of interest
with comparatively stronger producers.
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Affiliation(s)
- Alexis Casas
- Department of Bioengineering, Imperial College London, Exhibition Road, London SW7 2BX, United Kingdom
| | - Matthieu Bultelle
- Department of Bioengineering, Imperial College London, Exhibition Road, London SW7 2BX, United Kingdom
| | - Charles Motraghi
- Department of Bioengineering, Imperial College London, Exhibition Road, London SW7 2BX, United Kingdom
| | - Richard Kitney
- Department of Bioengineering, Imperial College London, Exhibition Road, London SW7 2BX, United Kingdom
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Casas A, Bultelle M, Motraghi C, Kitney R. Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology. Front Bioeng Biotechnol 2022; 9:785131. [PMID: 35083201 PMCID: PMC8784771 DOI: 10.3389/fbioe.2021.785131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/14/2021] [Indexed: 11/30/2022] Open
Abstract
We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).
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Affiliation(s)
- Alexis Casas
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Matthieu Bultelle
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Charles Motraghi
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Richard Kitney
- Department of Bioengineering, Imperial College London, London, United Kingdom
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Prototyping of microbial chassis for the biomanufacturing of high-value chemical targets. Biochem Soc Trans 2021; 49:1055-1063. [PMID: 34100907 DOI: 10.1042/bst20200017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/06/2021] [Accepted: 05/10/2021] [Indexed: 12/19/2022]
Abstract
Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design-Build-Test-Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.
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Zhang J, Chen Y, Fu L, Guo E, Wang B, Dai L, Si T. Accelerating strain engineering in biofuel research via build and test automation of synthetic biology. Curr Opin Biotechnol 2021; 67:88-98. [PMID: 33508635 DOI: 10.1016/j.copbio.2021.01.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 01/07/2021] [Accepted: 01/11/2021] [Indexed: 12/18/2022]
Abstract
Biofuels are a type of sustainable and renewable energy. However, for the economical production of bulk-volume biofuels, biosystems design is particularly challenging to achieve sufficient yield, titer, and productivity. Because of the lack of predictive modeling, high-throughput screening remains essential. Recently established biofoundries provide an emerging infrastructure to accelerate biological design-build-test-learn (DBTL) cycles through the integration of robotics, synthetic biology, and informatics. In this review, we first introduce the technical advances of build and test automation in synthetic biology, focusing on the use of industry-standard microplates for DNA assembly, chassis engineering, and enzyme and strain screening. Proof-of-concept studies on prototypes of automated foundries are then discussed, for improving biomass deconstruction, metabolic conversion, and host robustness. We conclude with future challenges and opportunities in creating a flexible, versatile, and data-driven framework to support biofuel research and development in biofoundries.
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Affiliation(s)
- Jianzhi Zhang
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yongcan Chen
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Erpeng Guo
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Bo Wang
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- CAS Key Laboratory of Quantitative Engineering Biology, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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9
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Yáñez Feliú G, Earle Gómez B, Codoceo Berrocal V, Muñoz Silva M, Nuñez IN, Matute TF, Arce Medina A, Vidal G, Vitalis C, Dahlin J, Federici F, Rudge TJ. Flapjack: Data Management and Analysis for Genetic Circuit Characterization. ACS Synth Biol 2021; 10:183-191. [PMID: 33382586 DOI: 10.1021/acssynbio.0c00554] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.
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Affiliation(s)
- Guillermo Yáñez Feliú
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Benjamín Earle Gómez
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Verner Codoceo Berrocal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Macarena Muñoz Silva
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Isaac N Nuñez
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Tamara F Matute
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Anibal Arce Medina
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Gonzalo Vidal
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Carlos Vitalis
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
| | - Jonathan Dahlin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Fernán Federici
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- ANID - Millennium Science Initiative Program - Millennium Institute for Integrative Biology (iBio), Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
- FONDAP, Center for Genome Regulation, Pontificia Universidad Católica de Chile, Santiago 8330005, Chile
| | - Timothy J Rudge
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago 7820244, Chile
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Gilman J, Walls L, Bandiera L, Menolascina F. Statistical Design of Experiments for Synthetic Biology. ACS Synth Biol 2021; 10:1-18. [PMID: 33406821 DOI: 10.1021/acssynbio.0c00385] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.
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Affiliation(s)
- James Gilman
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Laura Walls
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Lucia Bandiera
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Filippo Menolascina
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
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11
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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12
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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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13
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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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Patron NJ. Beyond natural: synthetic expansions of botanical form and function. THE NEW PHYTOLOGIST 2020; 227:295-310. [PMID: 32239523 PMCID: PMC7383487 DOI: 10.1111/nph.16562] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 03/03/2020] [Indexed: 05/05/2023]
Abstract
Powered by developments that enabled genome-scale investigations, systems biology emerged as a field aiming to understand how phenotypes emerge from network functions. These advances fuelled a new engineering discipline focussed on synthetic reconstructions of complex biological systems with the goal of predictable rational design and control. Initially, progress in the nascent field of synthetic biology was slow due to the ad hoc nature of molecular biology methods such as cloning. The application of engineering principles such as standardisation, together with several key technical advances, enabled a revolution in the speed and accuracy of genetic manipulation. Combined with mathematical and statistical modelling, this has improved the predictability of engineering biological systems of which nonlinearity and stochasticity are intrinsic features leading to remarkable achievements in biotechnology as well as novel insights into biological function. In the past decade, there has been slow but steady progress in establishing foundations for synthetic biology in plant systems. Recently, this has enabled model-informed rational design to be successfully applied to the engineering of plant gene regulation and metabolism. Synthetic biology is now poised to transform the potential of plant biotechnology. However, reaching full potential will require conscious adjustments to the skillsets and mind sets of plant scientists.
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Affiliation(s)
- Nicola J. Patron
- Engineering BiologyEarlham InstituteNorwich Research Park, NorwichNorfolkNR4 7UZUK
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15
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Kelwick RJR, Webb AJ, Freemont PS. Biological Materials: The Next Frontier for Cell-Free Synthetic Biology. Front Bioeng Biotechnol 2020; 8:399. [PMID: 32478045 PMCID: PMC7235315 DOI: 10.3389/fbioe.2020.00399] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 04/08/2020] [Indexed: 12/13/2022] Open
Abstract
Advancements in cell-free synthetic biology are enabling innovations in sustainable biomanufacturing, that may ultimately shift the global manufacturing paradigm toward localized and ecologically harmonized production processes. Cell-free synthetic biology strategies have been developed for the bioproduction of fine chemicals, biofuels and biological materials. Cell-free workflows typically utilize combinations of purified enzymes, cell extracts for biotransformation or cell-free protein synthesis reactions, to assemble and characterize biosynthetic pathways. Importantly, cell-free reactions can combine the advantages of chemical engineering with metabolic engineering, through the direct addition of co-factors, substrates and chemicals -including those that are cytotoxic. Cell-free synthetic biology is also amenable to automatable design cycles through which an array of biological materials and their underpinning biosynthetic pathways can be tested and optimized in parallel. Whilst challenges still remain, recent convergences between the materials sciences and these advancements in cell-free synthetic biology enable new frontiers for materials research.
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Affiliation(s)
- Richard J. R. Kelwick
- Section of Structural and Synthetic Biology, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Alexander J. Webb
- Section of Structural and Synthetic Biology, Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Paul S. Freemont
- Section of Structural and Synthetic Biology, Department of Infectious Disease, Imperial College London, London, United Kingdom
- The London Biofoundry, Imperial College Translation & Innovation Hub, London, United Kingdom
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London, London, United Kingdom
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16
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Prangemeier T, Lehr FX, Schoeman RM, Koeppl H. Microfluidic platforms for the dynamic characterisation of synthetic circuitry. Curr Opin Biotechnol 2020; 63:167-176. [PMID: 32172160 DOI: 10.1016/j.copbio.2020.02.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/31/2020] [Accepted: 02/03/2020] [Indexed: 01/28/2023]
Abstract
Generating novel functionality from well characterised synthetic parts and modules lies at the heart of synthetic biology. Ideally, circuitry is rationally designed in silico with quantitatively predictive models to predetermined design specifications. Synthetic circuits are intrinsically stochastic, often dynamically modulated and set in a dynamic fluctuating environment within a living cell. To build more complex circuits and to gain insight into context effects, intrinsic noise and transient performance, characterisation techniques that resolve both heterogeneity and dynamics are required. Here we review recent advances in both in vitro and in vivo microfluidic technologies that are suitable for the characterisation of synthetic circuitry, modules and parts.
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Affiliation(s)
- Tim Prangemeier
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany
| | - François-Xavier Lehr
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany
| | - Rogier M Schoeman
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany
| | - Heinz Koeppl
- Centre for Synthetic Biology, Department of Electrical Engineering and Information Technology, Department of Biology, Technische Universität Darmstadt, Germany.
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Abstract
Production of fuels and chemicals from renewable lignocellulosic feedstocks is a promising alternative to petroleum-derived compounds. Due to the complexity of lignocellulosic feedstocks, microbial conversion of all potential substrates will require substantial metabolic engineering. Non-model microbes offer desirable physiological traits, but also increase the difficulty of heterologous pathway engineering and optimization. The development of modular design principles that allow metabolic pathways to be used in a variety of novel microbes with minimal strain-specific optimization will enable the rapid construction of microbes for commercial production of biofuels and bioproducts. In this review, we discuss variability of lignocellulosic feedstocks, pathways for catabolism of lignocellulose-derived compounds, challenges to heterologous engineering of catabolic pathways, and opportunities to apply modular pathway design. Implementation of these approaches will simplify the process of modifying non-model microbes to convert diverse lignocellulosic feedstocks.
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18
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