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Lu H, Xiao L, Liao W, Yan X, Nielsen J. Cell factory design with advanced metabolic modelling empowered by artificial intelligence. Metab Eng 2024; 85:61-72. [PMID: 39038602 DOI: 10.1016/j.ymben.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/06/2024] [Accepted: 07/06/2024] [Indexed: 07/24/2024]
Abstract
Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.
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Affiliation(s)
- Hongzhong Lu
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
| | - Luchi Xiao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Wenbin Liao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Xuefeng Yan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Jens Nielsen
- BioInnovation Institute, Ole Måløes Vej, DK2200, Copenhagen N, Denmark; Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
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Ferreira S, Balola A, Sveshnikova A, Hatzimanikatis V, Vilaça P, Maia P, Carreira R, Stoney R, Carbonell P, Souza CS, Correia J, Lousa D, Soares CM, Rocha I. Computer-aided design and implementation of efficient biosynthetic pathways to produce high added-value products derived from tyrosine in Escherichia coli. Front Bioeng Biotechnol 2024; 12:1360740. [PMID: 38978715 PMCID: PMC11228882 DOI: 10.3389/fbioe.2024.1360740] [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: 12/24/2023] [Accepted: 06/03/2024] [Indexed: 07/10/2024] Open
Abstract
Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthesis of commercially attractive molecules often involves promiscuous enzymes or lacks well-established routes. To address these challenges, we developed a computational workflow integrating enumeration/retrosynthesis algorithms, a toolbox for pathway analysis, enzyme selection tools, and a gene discovery pipeline, supported by manual curation and literature review. Our focus has been on implementing biosynthetic pathways for tyrosine-derived compounds, specifically L-3,4-dihydroxyphenylalanine (L-DOPA) and dopamine, with significant applications in health and nutrition. We selected one pathway to produce L-DOPA and two different pathways for dopamine-one already described in the literature and a novel pathway. Our goal was either to identify the most suitable gene candidates for expression in Escherichia coli for the known pathways or to discover innovative pathways. Although not all implemented pathways resulted in the accumulation of target compounds, in our shake-flask experiments we achieved a maximum L-DOPA titer of 0.71 g/L and dopamine titers of 0.29 and 0.21 g/L for known and novel pathways, respectively. In the case of L-DOPA, we utilized, for the first time, a mutant version of tyrosinase from Ralstonia solanacearum. Production of dopamine via the known biosynthesis route was accomplished by coupling the L-DOPA pathway with the expression of DOPA decarboxylase from Pseudomonas putida, resulting in a unique biosynthetic pathway never reported in literature before. In the context of the novel pathway, dopamine was produced using tyramine as the intermediate compound. To achieve this, tyrosine was initially converted into tyramine by expressing TDC from Levilactobacillus brevis, which, in turn, was converted into dopamine through the action of the enzyme encoded by ppoMP from Mucuna pruriens. This marks the first time that an alternative biosynthetic pathway for dopamine has been validated in microbes. These findings underscore the effectiveness of our computational workflow in facilitating pathway enumeration and selection, offering the potential to uncover novel biosynthetic routes, thus paving the way for other target compounds of biotechnological interest.
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Affiliation(s)
- Sofia Ferreira
- Systems and Synthetic Biology Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Alexandra Balola
- Systems and Synthetic Biology Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Anastasia Sveshnikova
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Paulo Vilaça
- SilicoLife-Computational Biology Solutions for the Life Sciences, Braga, Portugal
| | - Paulo Maia
- SilicoLife-Computational Biology Solutions for the Life Sciences, Braga, Portugal
| | - Rafael Carreira
- SilicoLife-Computational Biology Solutions for the Life Sciences, Braga, Portugal
| | - Ruth Stoney
- Manchester Institute of Biotechnology, School of Chemistry, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
- Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC: Consejo Superior de Investigaciones Científicas, Paterna, Spain
| | - Caio Silva Souza
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - João Correia
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Diana Lousa
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Cláudio M Soares
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Isabel Rocha
- Systems and Synthetic Biology Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
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Sokolova N, Peng B, Haslinger K. Design and engineering of artificial biosynthetic pathways-where do we stand and where do we go? FEBS Lett 2023; 597:2897-2907. [PMID: 37777818 DOI: 10.1002/1873-3468.14745] [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: 07/03/2023] [Revised: 08/29/2023] [Accepted: 09/12/2023] [Indexed: 10/02/2023]
Abstract
The production of commodity and specialty chemicals relies heavily on fossil fuels. The negative impact of this dependency on our environment and climate has spurred a rising demand for more sustainable methods to obtain such chemicals from renewable resources. Herein, biotransformations of these renewable resources facilitated by enzymes or (micro)organisms have gained significant attention, since they can occur under mild conditions and reduce waste. These biotransformations typically leverage natural metabolic processes, which limits the scope and production capacity of such processes. In this mini-review, we provide an overview of advancements made in the past 5 years to expand the repertoire of biotransformations in engineered microorganisms. This ranges from redesign of existing pathways driven by retrobiosynthesis and computational design to directed evolution of enzymes and de novo pathway design to unlock novel routes for the synthesis of desired chemicals. We highlight notable examples of pathway designs for the production of commodity and specialty chemicals, showcasing the potential of these approaches. Lastly, we provide an outlook on future pathway design approaches.
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Affiliation(s)
- Nika Sokolova
- Department of Chemical and Pharmaceutical Biology, University of Groningen, The Netherlands
| | - Bo Peng
- Department of Chemical and Pharmaceutical Biology, University of Groningen, The Netherlands
| | - Kristina Haslinger
- Department of Chemical and Pharmaceutical Biology, University of Groningen, The Netherlands
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Han T, Nazarbekov A, Zou X, Lee SY. Recent advances in systems metabolic engineering. Curr Opin Biotechnol 2023; 84:103004. [PMID: 37778304 DOI: 10.1016/j.copbio.2023.103004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 10/03/2023]
Abstract
Systems metabolic engineering, which integrates metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, has revolutionized the sustainable production of fuels and materials through the creation of efficient microbial cell factories. Recent advancements in systems metabolic engineering targeting different biological components of the host cell have enabled the creation of highly productive microbial cell factories. This article provides a review of the recent tools and strategies used for enzyme-, genetic module-, pathway-, flux-, genome-, and cell-level engineering, supported by illustrative examples. Furthermore, we highlight recent trends in systems metabolic engineering, which involve the application of multiple tools discussed in this review. Finally, the paper addresses the challenges and perspectives of transitioning academic-level metabolic engineering studies to commercial-scale production.
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Affiliation(s)
- Taehee Han
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 34141 Daejeon, the Republic of Korea
| | - Alisher Nazarbekov
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea
| | - Xuan Zou
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 34141 Daejeon, the Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory and Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, the Republic of Korea; KAIST Institute for the BioCentury and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, the Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, 34141 Daejeon, the Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon 34141, the Republic of Korea.
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Zhong W, Li H, Wang Y. Design and Construction of Artificial Biological Systems for One-Carbon Utilization. BIODESIGN RESEARCH 2023; 5:0021. [PMID: 37915992 PMCID: PMC10616972 DOI: 10.34133/bdr.0021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
The third-generation (3G) biorefinery aims to use microbial cell factories or enzymatic systems to synthesize value-added chemicals from one-carbon (C1) sources, such as CO2, formate, and methanol, fueled by renewable energies like light and electricity. This promising technology represents an important step toward sustainable development, which can help address some of the most pressing environmental challenges faced by modern society. However, to establish processes competitive with the petroleum industry, it is crucial to determine the most viable pathways for C1 utilization and productivity and yield of the target products. In this review, we discuss the progresses that have been made in constructing artificial biological systems for 3G biorefineries in the last 10 years. Specifically, we highlight the representative works on the engineering of artificial autotrophic microorganisms, tandem enzymatic systems, and chemo-bio hybrid systems for C1 utilization. We also prospect the revolutionary impact of these developments on biotechnology. By harnessing the power of 3G biorefinery, scientists are establishing a new frontier that could potentially revolutionize our approach to industrial production and pave the way for a more sustainable future.
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Affiliation(s)
- Wei Zhong
- Westlake Center of Synthetic Biology and Integrated Bioengineering, School of Engineering,
Westlake University, Hangzhou 310000, PR China
| | - Hailong Li
- Westlake Center of Synthetic Biology and Integrated Bioengineering, School of Engineering,
Westlake University, Hangzhou 310000, PR China
- School of Materials Science and Engineering,
Zhejiang University, Zhejiang Province, Hangzhou 310000, PR China
| | - Yajie Wang
- Westlake Center of Synthetic Biology and Integrated Bioengineering, School of Engineering,
Westlake University, Hangzhou 310000, PR China
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Trostel L, Coll C, Fenner K, Hafner J. Combining predictive and analytical methods to elucidate pharmaceutical biotransformation in activated sludge. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2023; 25:1322-1336. [PMID: 37539453 DOI: 10.1039/d3em00161j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
While man-made chemicals in the environment are ubiquitous and a potential threat to human health and ecosystem integrity, the environmental fate of chemical contaminants such as pharmaceuticals is often poorly understood. Biodegradation processes driven by microbial communities convert chemicals into transformation products (TPs) that may themselves have adverse ecological effects. The detection of TPs formed during biodegradation has been continuously improved thanks to the development of TP prediction algorithms and analytical workflows. Here, we contribute to this advance by (i) reviewing past applications of TP identification workflows, (ii) applying an updated workflow for TP prediction to 42 pharmaceuticals in biodegradation experiments with activated sludge, and (iii) benchmarking 5 different pathway prediction models, comprising 4 prediction models trained on different datasets provided by enviPath, and the state-of-the-art EAWAG pathway prediction system. Using the updated workflow, we could tentatively identify 79 transformation products for 31 pharmaceutical compounds. Compared to previous works, we have further automatized several steps that were previously performed by hand. By benchmarking the enviPath prediction system on experimental data, we demonstrate the usefulness of the pathway prediction tool to generate suspect lists for screening, and we propose new avenues to improve their accuracy. Moreover, we provide a well-documented workflow that can be (i) readily applied to detect transformation products in activated sludge and (ii) potentially extended to other environmental studies.
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Affiliation(s)
- Leo Trostel
- Department of Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, 8600, Zürich, Switzerland.
| | - Claudia Coll
- Department of Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, 8600, Zürich, Switzerland.
| | - Kathrin Fenner
- Department of Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, 8600, Zürich, Switzerland.
- Department of Chemistry, University of Zürich, 8057 Zürich, Switzerland
| | - Jasmin Hafner
- Department of Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, 8600, Zürich, Switzerland.
- Department of Chemistry, University of Zürich, 8057 Zürich, Switzerland
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Arevalo Villa C, Marienhagen J, Noack S, Wahl SA. Achieving net zero CO 2 emission in the biobased production of reduced platform chemicals using defined co-feeding of methanol. Curr Opin Biotechnol 2023; 82:102967. [PMID: 37441841 DOI: 10.1016/j.copbio.2023.102967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/13/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
Next-generation bioprocesses of a future bio-based economy will rely on a flexible mix of readily available feedstocks. Renewable energy can be used to generate sustainable CO2-derived substrates. Metabolic engineering already enables the functional implementation of different pathways for the assimilation of C1 substrates in various microorganisms. In addition to feedstocks, the benchmark for all future bioprocesses will be sustainability, including the avoidance of CO2 emissions. Here we review recent advances in the utilization of C1-compounds from different perspectives, considering both strain and bioprocess engineering technologies. In particular, we evaluate methanol as a co-feed for enabling the CO2 emission-free production of acetyl-CoA-derived compounds. The possible metabolic strategies are analyzed using stoichiometric modeling combined with thermodynamic analysis and prospects for industrial-scale implementation are discussed.
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Affiliation(s)
- Carlos Arevalo Villa
- Lehrstuhl für Bioverfahrenstechnik, Friedrich Alexander Universität Erlangen-Nürnberg, D-91052 Erlangen, Germany
| | - Jan Marienhagen
- Institute of Bio, and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany; Institute of Biotechnology, RWTH Aachen University, D-52074 Aachen, Germany
| | - Stephan Noack
- Institute of Bio, and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany
| | - Sebastian Aljoscha Wahl
- Lehrstuhl für Bioverfahrenstechnik, Friedrich Alexander Universität Erlangen-Nürnberg, D-91052 Erlangen, Germany.
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Pasquini M, Stenta M. LinChemIn: SynGraph-a data model and a toolkit to analyze and compare synthetic routes. J Cheminform 2023; 15:41. [PMID: 37005691 PMCID: PMC10067316 DOI: 10.1186/s13321-023-00714-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/20/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND The increasing amount of chemical reaction data makes traditional ways to navigate its corpus less effective, while the demand for novel approaches and instruments is rising. Recent data science and machine learning techniques support the development of new ways to extract value from the available reaction data. On the one side, Computer-Aided Synthesis Planning tools can predict synthetic routes in a model-driven approach; on the other side, experimental routes can be extracted from the Network of Organic Chemistry, in which reaction data are linked in a network. In this context, the need to combine, compare and analyze synthetic routes generated by different sources arises naturally. RESULTS Here we present LinChemIn, a python toolkit that allows chemoinformatics operations on synthetic routes and reaction networks. Wrapping some third-party packages for handling graph arithmetic and chemoinformatics and implementing new data models and functionalities, LinChemIn allows the interconversion between data formats and data models and enables route-level analysis and operations, including route comparison and descriptors calculation. Object-Oriented Design principles inspire the software architecture, and the modules are structured to maximize code reusability and support code testing and refactoring. The code structure should facilitate external contributions, thus encouraging open and collaborative software development. CONCLUSIONS The current version of LinChemIn allows users to combine synthetic routes generated from various tools and analyze them, and constitutes an open and extensible framework capable of incorporating contributions from the community and fostering scientific discussion. Our roadmap envisages the development of sophisticated metrics for routes evaluation, a multi-parameter scoring system, and the implementation of an entire "ecosystem" of functionalities operating on synthetic routes. LinChemIn is freely available at https://github.com/syngenta/linchemin.
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Affiliation(s)
- Marta Pasquini
- Syngenta Crop Protection AG, Schaffhauserstrasse, 4332, Stein, AG, Switzerland.
| | - Marco Stenta
- Syngenta Crop Protection AG, Schaffhauserstrasse, 4332, Stein, AG, Switzerland
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Danchin A, Huang JD. SynBio 2.0, a new era for synthetic life: Neglected essential functions for resilience. Environ Microbiol 2023; 25:64-78. [PMID: 36045561 DOI: 10.1111/1462-2920.16140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 01/21/2023]
Affiliation(s)
- Antoine Danchin
- School of Biomedical Sciences, Li KaShing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong
| | - Jian Dong Huang
- School of Biomedical Sciences, Li KaShing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong
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10
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Xu P, Zhou K. Editorial overview: Analytical biotechnology for healthcare, strain engineering, biosensing and synthetic biology. Curr Opin Biotechnol 2022; 77:102765. [PMID: 35988531 DOI: 10.1016/j.copbio.2022.102765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Peng Xu
- Department of Chemical Engineering, Guangdong - Technion, Israel Institute of Technology, Shantou 515063, China.
| | - Kang Zhou
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore.
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