1
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Martín Lázaro H, Marín Bautista R, Carbonell P. DetSpace: a web server for engineering detectable pathways for bio-based chemical production. Nucleic Acids Res 2024; 52:W476-W480. [PMID: 38634809 PMCID: PMC11223873 DOI: 10.1093/nar/gkae287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/18/2024] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
Tackling climate change challenges requires replacing current chemical industrial processes through the rational and sustainable use of biodiversity resources. To that end, production routes to key bio-based chemicals for the bioeconomy have been identified. However, their production still remains inefficient in terms of titers, rates, and yields; because of the hurdles found when scaling up. In order to make production more efficient, strategies like automated screening and dynamic pathway regulation through biosensors have been applied as part of strain optimization. However, to date, no systematic way exists to design a genetic circuit that is responsive to concentrations of a given target compound. Here, the DetSpace web server provides a set of integrated tools that allows a user to select and design a biological circuit that performs the sensing of a molecule of interest by its enzymatic conversion to a detectable molecule through a transcription factor. In that way, the DetSpace web server allows synthetic biologists to easily design biosensing routes for the dynamic regulation of metabolic pathways in applications ranging from genetic circuits design, screening, production, and bioremediation of bio-based chemicals, to diagnostics and drug delivery.
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Affiliation(s)
- Hèctor Martín Lázaro
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 València, Spain
| | - Ricardo Marín Bautista
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 València, Spain
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 València, Spain
- Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, Escardino Street 9, Paterna, 46980 València, Spain
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2
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Mao J, Zhang H, Chen Y, Wei L, Liu J, Nielsen J, Chen Y, Xu N. Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms. Biotechnol Adv 2024; 74:108401. [PMID: 38944217 DOI: 10.1016/j.biotechadv.2024.108401] [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: 02/01/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 07/01/2024]
Abstract
Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.
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Affiliation(s)
- Jiwei Mao
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden
| | - Hongyu Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, PR China
| | - Liang Wei
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jun Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen, Denmark.
| | - Yun Chen
- Department of Life Sciences, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kongens Lyngby, Denmark.
| | - Ning Xu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, PR China; Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, PR China.
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3
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Komera I, Chen X, Liu L, Gao C. Microbial Synthetic Epigenetic Tools Design and Applications. ACS Synth Biol 2024; 13:1621-1632. [PMID: 38758631 DOI: 10.1021/acssynbio.4c00125] [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: 05/19/2024]
Abstract
Microbial synthetic epigenetics offers significant opportunities for the design of synthetic biology tools by leveraging reversible gene control mechanisms without altering DNA sequences. However, limited understanding and a lack of technologies for thorough analysis of the mechanisms behind epigenetic modifications have hampered their utilization in biotechnological applications. In this review, we explore advancements in developing epigenetic-based synthetic gene regulatory tools at both transcriptional and post-transcriptional levels. Furthermore, we examine strategies developed to construct epigenetic-based circuits that provide controllable and stable gene regulation, aiming to boost the performance of microbial chassis cells. Finally, we discuss the current challenges and perspectives in the development of synthetic epigenetic tools.
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Affiliation(s)
- Irene Komera
- School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xiulai Chen
- School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liming Liu
- School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Cong Gao
- School of Biotechnology and Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi 214122, China
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4
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Patwari P, Pruckner F, Fabris M. Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology. Biotechnol Adv 2023; 68:108221. [PMID: 37495181 DOI: 10.1016/j.biotechadv.2023.108221] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 06/22/2023] [Accepted: 07/22/2023] [Indexed: 07/28/2023]
Abstract
Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.
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Affiliation(s)
- Payal Patwari
- SDU Biotechnology, Faculty of Engineering, University of Southern Denmark, Odense M DK-5230, Denmark
| | - Florian Pruckner
- SDU Biotechnology, Faculty of Engineering, University of Southern Denmark, Odense M DK-5230, Denmark
| | - Michele Fabris
- SDU Biotechnology, Faculty of Engineering, University of Southern Denmark, Odense M DK-5230, Denmark.
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5
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Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans 2023; 51:1871-1879. [PMID: 37656433 PMCID: PMC10657174 DOI: 10.1042/bst20221542] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
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Affiliation(s)
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, U.K
- The Alan Turing Institute, London, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh, U.K
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6
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Kell B, Ripsman R, Hilfinger A. Noise properties of adaptation-conferring biochemical control modules. Proc Natl Acad Sci U S A 2023; 120:e2302016120. [PMID: 37695915 PMCID: PMC10515136 DOI: 10.1073/pnas.2302016120] [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: 02/05/2023] [Accepted: 06/12/2023] [Indexed: 09/13/2023] Open
Abstract
A key goal of synthetic biology is to develop functional biochemical modules with network-independent properties. Antithetic integral feedback (AIF) is a recently developed control module in which two control species perfectly annihilate each other's biological activity. The AIF module confers robust perfect adaptation to the steady-state average level of a controlled intracellular component when subjected to sustained perturbations. Recent work has suggested that such robustness comes at the unavoidable price of increased stochastic fluctuations around average levels. We present theoretical results that support and quantify this trade-off for the commonly analyzed AIF variant in the idealized limit with perfect annihilation. However, we also show that this trade-off is a singular limit of the control module: Even minute deviations from perfect adaptation allow systems to achieve effective noise suppression as long as cells can pay the corresponding energetic cost. We further show that a variant of the AIF control module can achieve significant noise suppression even in the idealized limit with perfect adaptation. This atypical configuration may thus be preferable in synthetic biology applications.
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Affiliation(s)
- Brayden Kell
- Department of Physics, University of Toronto, Toronto, ONM5S 1A7, Canada
- Department of Chemical and Physical Sciences, University of Toronto, Mississauga, ONL5L 1C6, Canada
- Department of Molecular Biosciences, Northwestern University, Evanston, IL60208
- National Science Foundation-Simons Center for Quantitative Biology, Northwestern University, Evanston, IL60208
| | - Ryan Ripsman
- Department of Physics, University of Toronto, Toronto, ONM5S 1A7, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ONM5S 1A8, Canada
| | - Andreas Hilfinger
- Department of Physics, University of Toronto, Toronto, ONM5S 1A7, Canada
- Department of Chemical and Physical Sciences, University of Toronto, Mississauga, ONL5L 1C6, Canada
- Department of Mathematics, University of Toronto, Toronto, ONM5S 2E4, Canada
- Department of Cell and Systems Biology, University of Toronto, Toronto, ONM5S 3G5, Canada
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7
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Alexis E, Schulte CCM, Cardelli L, Papachristodoulou A. Regulation strategies for two-output biomolecular networks. J R Soc Interface 2023; 20:20230174. [PMID: 37528680 PMCID: PMC10394417 DOI: 10.1098/rsif.2023.0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 07/06/2023] [Indexed: 08/03/2023] Open
Abstract
Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.
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Affiliation(s)
- Emmanouil Alexis
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Carolin C. M. Schulte
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Luca Cardelli
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
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8
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Demeester W, De Baets J, Duchi D, De Mey M, De Paepe B. MoBioS: Modular Platform Technology for High-Throughput Construction and Characterization of Tunable Transcriptional Biological Sensors. BIOSENSORS 2023; 13:590. [PMID: 37366955 DOI: 10.3390/bios13060590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/16/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023]
Abstract
All living organisms have evolved and fine-tuned specialized mechanisms to precisely monitor a vast array of different types of molecules. These natural mechanisms can be sourced by researchers to build Biological Sensors (BioS) by combining them with an easily measurable output, such as fluorescence. Because they are genetically encoded, BioS are cheap, fast, sustainable, portable, self-generating and highly sensitive and specific. Therefore, BioS hold the potential to become key enabling tools that stimulate innovation and scientific exploration in various disciplines. However, the main bottleneck in unlocking the full potential of BioS is the fact that there is no standardized, efficient and tunable platform available for the high-throughput construction and characterization of biosensors. Therefore, a modular, Golden Gate-based construction platform, called MoBioS, is introduced in this article. It allows for the fast and easy creation of transcription factor-based biosensor plasmids. As a proof of concept, its potential is demonstrated by creating eight different, functional and standardized biosensors that detect eight diverse molecules of industrial interest. In addition, the platform contains novel built-in features to facilitate fast and efficient biosensor engineering and response curve tuning.
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Affiliation(s)
- Wouter Demeester
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Jasmine De Baets
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Dries Duchi
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Marjan De Mey
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Brecht De Paepe
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
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9
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Du Y, Zhang X, Zhang H, Zhu R, Zhao Z, Han J, Zhang D, Zhang X, Zhang X, Pan X, You J, Rao Z. Direct evolution of riboflavin kinase significantly enhance flavin mononucleotide synthesis by design and optimization of flavin mononucleotide riboswitch. BIORESOURCE TECHNOLOGY 2023; 381:128774. [PMID: 36822556 DOI: 10.1016/j.biortech.2023.128774] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 05/08/2023]
Abstract
Flavin mononucleotide (FMN) is the active form of riboflavin. It has a wide range of application scenarios in the pharmaceutical and food additives. However, there are limitations in selecting generic high-throughput screening platforms that improve the properties of enzymes. First, the biosensor in response to FMN concentration was constructed using the FMN riboswitch and confirmed the function of this sensor. Next, the FMN binding site of the sensor was saturated with a mutation that increased its fluorescence range by approximately 127%. Then, the biosensor and the base editing system based on T7RNAP were combined to construct a platform for rapid mutation and screening of riboflavin kinase gene ribC mutants. The mutants screened using this platform increased the yield of FMN by 8-fold. These results indicate that the high-throughput screening platform can rapidly and effectively improve the activity of target enzymes, and provide a new route for screening industrial enzymes.
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Affiliation(s)
- Yuxuan Du
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xinyi Zhang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Hengwei Zhang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Rongshuai Zhu
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhenqiang Zhao
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jin Han
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Di Zhang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xiaoling Zhang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xian Zhang
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Xuewei Pan
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Jiajia You
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China
| | - Zhiming Rao
- Key Laboratory of Industrial Biotechnology of the Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.
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10
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Tellechea-Luzardo J, Stiebritz MT, Carbonell P. Transcription factor-based biosensors for screening and dynamic regulation. Front Bioeng Biotechnol 2023; 11:1118702. [PMID: 36814719 PMCID: PMC9939652 DOI: 10.3389/fbioe.2023.1118702] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/26/2023] [Indexed: 02/09/2023] Open
Abstract
Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.
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Affiliation(s)
- Jonathan Tellechea-Luzardo
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Martin T. Stiebritz
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
| | - 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, Paterna, Spain,*Correspondence: Pablo Carbonell,
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11
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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12
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Liu D, Sica MS, Mao J, Chao LFI, Siewers V. A p-Coumaroyl-CoA Biosensor for Dynamic Regulation of Naringenin Biosynthesis in Saccharomyces cerevisiae. ACS Synth Biol 2022; 11:3228-3238. [PMID: 36137537 PMCID: PMC9594313 DOI: 10.1021/acssynbio.2c00111] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
In vivo biosensors that can convert metabolite concentrations into measurable output signals are valuable tools for high-throughput screening and dynamic pathway control in the field of metabolic engineering. Here, we present a novel biosensor in Saccharomyces cerevisiae that is responsive to p-coumaroyl-CoA, a central precursor of many flavonoids. The sensor is based on the transcriptional repressor CouR from Rhodopseudomonas palustris and was applied in combination with a previously developed malonyl-CoA biosensor for dual regulation of p-coumaroyl-CoA synthesis within the naringenin production pathway. Using this approach, we obtained a naringenin titer of 47.3 mg/L upon external precursor feeding, representing a 15-fold increase over the nonregulated system.
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13
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Hilgers F, Hogenkamp F, Klaus O, Kruse L, Loeschcke A, Bier C, Binder D, Jaeger KE, Pietruszka J, Drepper T. Light-mediated control of gene expression in the anoxygenic phototrophic bacterium Rhodobacter capsulatus using photocaged inducers. Front Bioeng Biotechnol 2022; 10:902059. [PMID: 36246361 PMCID: PMC9561348 DOI: 10.3389/fbioe.2022.902059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Abstract
Photocaged inducer molecules, especially photocaged isopropyl-β-d-1-thiogalactopyranoside (cIPTG), are well-established optochemical tools for light-regulated gene expression and have been intensively applied in Escherichia coli and other bacteria including Corynebacterium glutamicum, Pseudomonas putida or Bacillus subtilis. In this study, we aimed to implement a light-mediated on-switch for target gene expression in the facultative anoxygenic phototroph Rhodobacter capsulatus by using different cIPTG variants under both phototrophic and non-phototrophic cultivation conditions. We could demonstrate that especially 6-nitropiperonyl-(NP)-cIPTG can be applied for light-mediated induction of target gene expression in this facultative phototrophic bacterium. Furthermore, we successfully applied the optochemical approach to induce the intrinsic carotenoid biosynthesis to showcase engineering of a cellular function. Photocaged IPTG thus represents a light-responsive tool, which offers various promising properties suitable for future applications in biology and biotechnology including automated multi-factorial control of cellular functions as well as optimization of production processes.
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Affiliation(s)
- Fabienne Hilgers
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
| | - Fabian Hogenkamp
- Institute of Bioorganic Chemistry, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
| | - Oliver Klaus
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
| | - Luzie Kruse
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
| | - Anita Loeschcke
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
| | - Claus Bier
- Institute of Bioorganic Chemistry, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
| | - Dennis Binder
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
| | - Karl-Erich Jaeger
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
- Institute of Bio- and Geosciences: Biotechnology (IBG-1), Forschungszentrum Jülich, Jülich, Germany
| | - Jörg Pietruszka
- Institute of Bioorganic Chemistry, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
- Institute of Bio- and Geosciences: Biotechnology (IBG-1), Forschungszentrum Jülich, Jülich, Germany
- *Correspondence: Jörg Pietruszka, ; Thomas Drepper,
| | - Thomas Drepper
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf at Forschungszentrum Jülich, Jülich, Germany
- *Correspondence: Jörg Pietruszka, ; Thomas Drepper,
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14
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Isogai S, Tominaga M, Kondo A, Ishii J. Plant Flavonoid Production in Bacteria and Yeasts. FRONTIERS IN CHEMICAL ENGINEERING 2022. [DOI: 10.3389/fceng.2022.880694] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Flavonoids, a major group of secondary metabolites in plants, are promising for use as pharmaceuticals and food supplements due to their health-promoting biological activities. Industrial flavonoid production primarily depends on isolation from plants or organic synthesis, but neither is a cost-effective or sustainable process. In contrast, recombinant microorganisms have significant potential for the cost-effective, sustainable, environmentally friendly, and selective industrial production of flavonoids, making this an attractive alternative to plant-based production or chemical synthesis. Structurally and functionally diverse flavonoids are derived from flavanones such as naringenin, pinocembrin and eriodictyol, the major basic skeletons for flavonoids, by various modifications. The establishment of flavanone-producing microorganisms can therefore be used as a platform for producing various flavonoids. This review summarizes metabolic engineering and synthetic biology strategies for the microbial production of flavanones. In addition, we describe directed evolution strategies based on recently-developed high-throughput screening technologies for the further improvement of flavanone production. We also describe recent progress in the microbial production of structurally and functionally complicated flavonoids via the flavanone modifications. Strategies based on synthetic biology will aid more sophisticated and controlled microbial production of various flavonoids.
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15
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Metabolite-based biosensors for natural product discovery and overproduction. Curr Opin Biotechnol 2022; 75:102699. [DOI: 10.1016/j.copbio.2022.102699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/25/2022] [Accepted: 02/05/2022] [Indexed: 12/22/2022]
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16
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Boada Y, Santos-Navarro FN, Picó J, Vignoni A. Modeling and Optimization of a Molecular Biocontroller for the Regulation of Complex Metabolic Pathways. Front Mol Biosci 2022; 9:801032. [PMID: 35425808 PMCID: PMC9001882 DOI: 10.3389/fmolb.2022.801032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 02/22/2022] [Indexed: 11/30/2022] Open
Abstract
Achieving optimal production in microbial cell factories, robustness against changing intracellular and environmental perturbations requires the dynamic feedback regulation of the pathway of interest. Here, we consider a merging metabolic pathway motif, which appears in a wide range of metabolic engineering applications, including the production of phenylpropanoids among others. We present an approach to use a realistic model that accounts for in vivo implementation and then propose a methodology based on multiobjective optimization for the optimal tuning of the gene circuit parts composing the biomolecular controller and biosensor devices for a dynamic regulation strategy. We show how this approach can deal with the trade-offs between the performance of the regulated pathway, robustness to perturbations, and stability of the feedback loop. Using realistic models, our results suggest that the strategies for fine-tuning the trade-offs among performance, robustness, and stability in dynamic pathway regulation are complex. It is not always possible to infer them by simple inspection. This renders the use of the multiobjective optimization methodology valuable and necessary.
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17
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Wu T, Chen Z, Guo S, Zhang C, Huo YX. Engineering Transcription Factor BmoR Mutants for Constructing Multifunctional Alcohol Biosensors. ACS Synth Biol 2022; 11:1251-1260. [PMID: 35175734 DOI: 10.1021/acssynbio.1c00549] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Native transcription factor-based biosensors (TFBs) have the potential for the in situ detection of value-added chemicals or byproducts. However, their industrial application is limited by their ligand promiscuity, low sensitivity, and narrow detection range. Alcohols exhibit similar structures, and no reported TFB can distinguish a specific alcohol from its analogues. Here, we engineered an alcohol-regulated transcription factor, BmoR, and obtained various mutants with remarkable properties. For example, the generated signal-molecule-specific BmoRs could distinguish the constitutional isomers n-butanol and isobutanol, with insensitivity up to an ethanol concentration of 800 mM (36.9 g/L). Linear detection of 0-60 mM of a specific higher alcohol could be achieved in the presence of up to 500 mM (23.0 g/L) ethanol as background noise. Furthermore, we obtained two mutants with raised outputs and over 107-fold higher sensitivity and one mutant with an increased upper detection limit (14.8 g/L n-butanol or isobutanol). Using BmoR as an example, this study systematically explored the ultimate detection limit of a TFB toward its small-molecule ligands, paving the way for in situ detection in biofuel and wine industries.
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Affiliation(s)
- Tong Wu
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, 100081 Beijing, China
| | - Zhenya Chen
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, 100081 Beijing, China
| | - Shuyuan Guo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, 100081 Beijing, China
| | - Cuiying Zhang
- Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Industrial Microbiology Key Laboratory, College of Biotechnology, Tianjin University of Science and Technology, No. 9 13th Street, Tianjin Economic and Technological Development Zone, 300457 Tianjin, China
| | - Yi-Xin Huo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, No. 5 South Zhongguancun Street, 100081 Beijing, China
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18
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Verma BK, Mannan AA, Zhang F, Oyarzún DA. Trade-Offs in Biosensor Optimization for Dynamic Pathway Engineering. ACS Synth Biol 2022; 11:228-240. [PMID: 34968029 DOI: 10.1021/acssynbio.1c00391] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.
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Affiliation(s)
- Babita K. Verma
- School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, U.K
| | - Ahmad A. Mannan
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, U.K
| | - Fuzhong Zhang
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Diego A. Oyarzún
- School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, U.K
- School of Informatics, The University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London, NW1 2DB, U.K
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19
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Boada Y, Picó J, Vignoni A. Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation. Methods Mol Biol 2022; 2385:65-89. [PMID: 34888716 DOI: 10.1007/978-1-0716-1767-0_4] [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: 06/13/2023]
Abstract
Semi-mechanistic kinetic (i.e., dynamic) models based on first principles are particularly relevant in biology, as they can explain and predict functional behavior that arises from varying concentrations of the cellular components over time. Here, we describe a computational tuning framework to facilitate both the selection of kinetic parameters for these models and its estimation from experimental data. On the one hand, the tuning framework uses multi-objective optimization to generate a model-based set of guidelines for the selection of the kinetic parameters. These parameter values are the required ones to provide a biological system with desired behavior, while fulfilling the design criteria encoded in the optimization problem itself. On the other hand, this framework can also be used to estimate the parameter values of biological systems from experimental data, once the optimization objectives had been defined appropriately. The methodology gives accurate identification results, as it provides clear orientation on the effect of the parameter values over the system's behavior even under different experimental scenarios. It is particularly useful for easily combining time-course-averaged data and steady-state distribution data. This protocol also addresses aspects related to the appropriate description of the kinetic models and the settings of the software tools. Therefore, it supplies for hands-on testing to evaluate the validity of the underlying technical assumptions of the biological kinetic models.
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Affiliation(s)
- Yadira Boada
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Valencia, Spain
- Centro Universitario EDEM, Escuela de Empresarios, La Marina de València, Valencia, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Valencia, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de Valencia, Valencia, Spain.
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20
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Intelligent host engineering for metabolic flux optimisation in biotechnology. Biochem J 2021; 478:3685-3721. [PMID: 34673920 PMCID: PMC8589332 DOI: 10.1042/bcj20210535] [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] [Received: 07/25/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.
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21
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Gene Expression Space Shapes the Bioprocess Trade-Offs among Titer, Yield and Productivity. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11135859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Optimal gene expression is central for the development of both bacterial expression systems for heterologous protein production, and microbial cell factories for industrial metabolite production. Our goal is to fulfill industry-level overproduction demands optimally, as measured by the following key performance metrics: titer, productivity rate, and yield (TRY). Here we use a multiscale model incorporating the dynamics of (i) the cell population in the bioreactor, (ii) the substrate uptake and (iii) the interaction between the cell host and expression of the protein of interest. Our model predicts cell growth rate and cell mass distribution between enzymes of interest and host enzymes as a function of substrate uptake and the following main lab-accessible gene expression-related characteristics: promoter strength, gene copy number and ribosome binding site strength. We evaluated the differential roles of gene transcription and translation in shaping TRY trade-offs for a wide range of expression levels and the sensitivity of the TRY space to variations in substrate availability. Our results show that, at low expression levels, gene transcription mainly defined TRY, and gene translation had a limited effect; whereas, at high expression levels, TRY depended on the product of both, in agreement with experiments in the literature.
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22
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Khammash MH. Perfect adaptation in biology. Cell Syst 2021; 12:509-521. [PMID: 34139163 DOI: 10.1016/j.cels.2021.05.020] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/24/2021] [Accepted: 05/24/2021] [Indexed: 12/22/2022]
Abstract
A distinctive feature of many biological systems is their ability to adapt to persistent stimuli or disturbances that would otherwise drive them away from a desirable steady state. The resulting stasis enables organisms to function reliably while being subjected to very different external environments. This perspective concerns a stringent type of biological adaptation, robust perfect adaptation (RPA), that is resilient to certain network and parameter perturbations. As in engineered control systems, RPA requires that the regulating network satisfy certain structural constraints that cannot be avoided. We elucidate these ideas using biological examples from systems and synthetic biology. We then argue that understanding the structural constraints underlying RPA allows us to look past implementation details and offers a compelling means to unravel regulatory biological complexity.
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Affiliation(s)
- Mustafa H Khammash
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
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23
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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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