1
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Snoeck S, Guidi C, De Mey M. "Metabolic burden" explained: stress symptoms and its related responses induced by (over)expression of (heterologous) proteins in Escherichia coli. Microb Cell Fact 2024; 23:96. [PMID: 38555441 PMCID: PMC10981312 DOI: 10.1186/s12934-024-02370-9] [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: 12/01/2023] [Accepted: 03/18/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Engineering bacterial strains to redirect the metabolism towards the production of a specific product has enabled the development of industrial biotechnology. However, rewiring the metabolism can have severe implications for a microorganism, rendering cells with stress symptoms such as a decreased growth rate, impaired protein synthesis, genetic instability and an aberrant cell size. On an industrial scale, this is reflected in processes that are not economically viable. MAIN TEXT In literature, most stress symptoms are attributed to "metabolic burden", however the actual triggers and stress mechanisms involved are poorly understood. Therefore, in this literature review, we aimed to get a better insight in how metabolic engineering affects Escherichia coli and link the observed stress symptoms to its cause. Understanding the possible implications that chosen engineering strategies have, will help to guide the reader towards optimising the envisioned process more efficiently. CONCLUSION This review addresses the gap in literature and discusses the triggers and effects of stress mechanisms that can be activated when (over)expressing (heterologous) proteins in Escherichia coli. It uncovers that the activation of the different stress mechanisms is complex and that many are interconnected. The reader is shown that care has to be taken when (over)expressing (heterologous) proteins as the cell's metabolism is tightly regulated.
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Affiliation(s)
- Sofie Snoeck
- Department of Biotechnology, Centre for Synthetic Biology, Coupure Links 653, Gent, 9000, Belgium
| | - Chiara Guidi
- Department of Biotechnology, Centre for Synthetic Biology, Coupure Links 653, Gent, 9000, Belgium
| | - Marjan De Mey
- Department of Biotechnology, Centre for Synthetic Biology, Coupure Links 653, Gent, 9000, Belgium.
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2
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Sechkar K, Steel H, Perrino G, Stan GB. A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits. Nat Commun 2024; 15:1981. [PMID: 38438391 PMCID: PMC10912777 DOI: 10.1038/s41467-024-46410-9] [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: 05/15/2023] [Accepted: 02/27/2024] [Indexed: 03/06/2024] Open
Abstract
Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model's usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.
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Affiliation(s)
- Kirill Sechkar
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Harrison Steel
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Giansimone Perrino
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
| | - Guy-Bart Stan
- Department of Bioengineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
- Imperial College Centre of Excellence in Synthetic Biology, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.
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3
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Baig Y, Ma HR, Xu H, You L. Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics. Nat Commun 2023; 14:7937. [PMID: 38049401 PMCID: PMC10696002 DOI: 10.1038/s41467-023-43455-0] [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: 01/30/2023] [Accepted: 11/09/2023] [Indexed: 12/06/2023] Open
Abstract
The ability to effectively represent microbiome dynamics is a crucial challenge in their quantitative analysis and engineering. By using autoencoder neural networks, we show that microbial growth dynamics can be compressed into low-dimensional representations and reconstructed with high fidelity. These low-dimensional embeddings are just as effective, if not better, than raw data for tasks such as identifying bacterial strains, predicting traits like antibiotic resistance, and predicting community dynamics. Additionally, we demonstrate that essential dynamical information of these systems can be captured using far fewer variables than traditional mechanistic models. Our work suggests that machine learning can enable the creation of concise representations of high-dimensional microbiome dynamics to facilitate data analysis and gain new biological insights.
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Affiliation(s)
- Yasa Baig
- Department of Physics, Duke University, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Helena R Ma
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA
| | - Helen Xu
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Center for Quantitative Biodesign, Duke University, Durham, NC, USA.
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
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4
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Delvigne F, Martinez JA. Advances in automated and reactive flow cytometry for synthetic biotechnology. Curr Opin Biotechnol 2023; 83:102974. [PMID: 37515938 DOI: 10.1016/j.copbio.2023.102974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/20/2023] [Accepted: 07/03/2023] [Indexed: 07/31/2023]
Abstract
Automated flow cytometry (FC) has been initially considered for bioprocess monitoring and optimization. More recently, new physical and software interfaces have been made available, facilitating the access to this technology for labs and industries. It also comes with new capabilities, such as being able to act on the cultivation conditions based on population data. This approach, known as reactive FC, extended the range of applications of automated FC to bioprocess control and the stabilization of cocultures, but also to the broad field of synthetic and systems biology for the characterization of gene circuits. However, several issues must be addressed before automated and reactive FC can be considered standard and modular technologies.
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Affiliation(s)
- Frank Delvigne
- Terra Research and Teaching Center, Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium.
| | - Juan A Martinez
- Terra Research and Teaching Center, Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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5
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Alonso VPP, Lemos JG, Nascimento MDSD. Yeast biofilms on abiotic surfaces: Adhesion factors and control methods. Int J Food Microbiol 2023; 400:110265. [PMID: 37267839 DOI: 10.1016/j.ijfoodmicro.2023.110265] [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: 03/06/2023] [Revised: 05/21/2023] [Accepted: 05/23/2023] [Indexed: 06/04/2023]
Abstract
Biofilms are highly resistant to antimicrobials and are a common problem in many industries, including pharmaceutical, food and beverage. Yeast biofilms can be formed by various yeast species, including Candida albicans, Saccharomyces cerevisiae, and Cryptococcus neoformans. Yeast biofilm formation is a complex process that involves several stages, including reversible adhesion, followed by irreversible adhesion, colonization, exopolysaccharide matrix formation, maturation and dispersion. Intercellular communication in yeast biofilms (quorum-sensing mechanism), environmental factors (pH, temperature, composition of the culture medium), and physicochemical factors (hydrophobicity, Lifshitz-van der Waals and Lewis acid-base properties, and electrostatic interactions) are essential to the adhesion process. Studies on the adhesion of yeast to abiotic surfaces such as stainless steel, wood, plastic polymers, and glass are still scarce, representing a gap in the field. The biofilm control formation can be a challenging task for food industry. However, some strategies can help to reduce biofilm formation, such as good hygiene practices, including regular cleaning and disinfection of surfaces. The use of antimicrobials and alternative methods to remove the yeast biofilms may also be helpful to ensure food safety. Furthermore, physical control measures such as biosensors and advanced identification techniques are promising for yeast biofilms control. However, there is a gap in understanding why some yeast strains are more tolerant or resistant to sanitization methods. A better understanding of tolerance and resistance mechanisms can help researchers and industry professionals to develop more effective and targeted sanitization strategies to prevent bacterial contamination and ensure product quality. This review aimed to identify the most important information about yeast biofilms in the food industry, followed by the removal of these biofilms by antimicrobial agents. In addition, the review summarizes the alternative sanitizing methods and future perspectives for controlling yeast biofilm formation by biosensors.
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Affiliation(s)
| | - Jéssica Gonçalves Lemos
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Rua Monteiro Lobato n° 80, Campinas, São Paulo 13083-862, Brazil
| | - Maristela da Silva do Nascimento
- Department of Food Engineering and Technology, School of Food Engineering, University of Campinas, Rua Monteiro Lobato n° 80, Campinas, São Paulo 13083-862, Brazil.
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6
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Gyorgy A, Menezes A, Arcak M. A blueprint for a synthetic genetic feedback optimizer. Nat Commun 2023; 14:2554. [PMID: 37137895 PMCID: PMC10156725 DOI: 10.1038/s41467-023-37903-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/05/2023] [Indexed: 05/05/2023] Open
Abstract
Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.
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Affiliation(s)
- Andras Gyorgy
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Amor Menezes
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
| | - Murat Arcak
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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7
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Caringella G, Bandiera L, Menolascina F. Recent advances, opportunities and challenges in cybergenetic identification and control of biomolecular networks. Curr Opin Biotechnol 2023; 80:102893. [PMID: 36706519 DOI: 10.1016/j.copbio.2023.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 01/26/2023]
Abstract
Cybergenetics is a new area of research aimed at developing digital and biological controllers for living systems. Synthetic biologists have begun exploiting cybergenetic tools and platforms to both accelerate the development of mathematical models and develop control strategies for complex biological phenomena. Here, we review the state of the art in cybergenetic identification and control. Our aim is to lower the entry barrier to this field and foster the adoption of methods and technologies that will accelerate the pace at which Synthetic Biology progresses toward applications.
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Affiliation(s)
- Gianpio Caringella
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK
| | - Lucia Bandiera
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK; Centre for Engineering Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Filippo Menolascina
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK; Centre for Engineering Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK.
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8
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Biomolecular feedback controllers: from theory to applications. Curr Opin Biotechnol 2023; 79:102882. [PMID: 36638743 DOI: 10.1016/j.copbio.2022.102882] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/07/2022] [Indexed: 01/13/2023]
Abstract
Billions of years of evolution have led to the creation of sophisticated genetic regulatory mechanisms that control various biological processes in a timely and precise fashion, despite their uncertain and noisy environments. Understanding such naturally existing mechanisms and even designing novel ones will have direct implications in various fields such as biotechnology, medicine, and synthetic biology. In particular, many studies have revealed that feedback-based control mechanisms inside the living cells endow the overall system with multiple attractive features, including homeostasis, noise reduction, and high dynamic performance. The remarkable interdisciplinary nature of these studies has brought together disparate disciplines such as systems/synthetic biology and control theory in an effort to design and build more powerful and reliable biomolecular control systems. Here, we review various biomolecular feedback controllers, highlight their characteristics, and point out their promising impact.
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9
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Csibra E, Stan GB. Absolute protein quantification using fluorescence measurements with FPCountR. Nat Commun 2022; 13:6600. [PMID: 36329019 PMCID: PMC9633595 DOI: 10.1038/s41467-022-34232-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
This paper presents a generalisable method for the calibration of fluorescence readings on microplate readers, in order to convert arbitrary fluorescence units into absolute units. FPCountR relies on the generation of bespoke fluorescent protein (FP) calibrants, assays to determine protein concentration and activity, and a corresponding analytical workflow. We systematically characterise the assay protocols for accuracy, sensitivity and simplicity, and describe an 'ECmax' assay that outperforms the others and even enables accurate calibration without requiring the purification of FPs. To obtain cellular protein concentrations, we consider methods for the conversion of optical density to either cell counts or alternatively to cell volumes, as well as examining how cells can interfere with protein counting via fluorescence quenching, which we quantify and correct for the first time. Calibration across different instruments, disparate filter sets and mismatched gains is demonstrated to yield equivalent results. It also reveals that mCherry absorption at 600 nm does not confound cell density measurements unless expressed to over 100,000 proteins per cell. FPCountR is presented as pair of open access tools (protocol and R package) to enable the community to use this method, and ultimately to facilitate the quantitative characterisation of synthetic microbial circuits.
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Affiliation(s)
- Eszter Csibra
- grid.7445.20000 0001 2113 8111Department of Bioengineering, Imperial College Centre for Synthetic Biology (IC-CSynB), Imperial College London, London, SW7 2AY UK
| | - Guy-Bart Stan
- grid.7445.20000 0001 2113 8111Department of Bioengineering, Imperial College Centre for Synthetic Biology (IC-CSynB), Imperial College London, London, SW7 2AY UK
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10
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Lee TA, Steel H. Cybergenetic control of microbial community composition. Front Bioeng Biotechnol 2022; 10:957140. [PMID: 36277404 PMCID: PMC9582452 DOI: 10.3389/fbioe.2022.957140] [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: 05/30/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The use of bacterial communities in bioproduction instead of monocultures has potential advantages including increased productivity through division of labour, ability to utilise cheaper substrates, and robustness against perturbations. A key challenge in the application of engineered bacterial communities is the ability to reliably control the composition of the community in terms of its constituent species. This is crucial to prevent faster growing species from outcompeting others with a lower relative fitness, and to ensure that all species are present at an optimal ratio during different steps in a biotechnological process. In contrast to purely biological approaches such as synthetic quorum sensing circuits or paired auxotrophies, cybergenetic control techniques - those in which computers interface with living cells-are emerging as an alternative approach with many advantages. The community composition is measured through methods such as fluorescence intensity or flow cytometry, with measured data fed real-time into a computer. A control action is computed using a variety of possible control algorithms and then applied to the system, with actuation taking the form of chemical (e.g., inducers, nutrients) or physical (e.g., optogenetic, mechanical) inputs. Subsequent changes in composition are then measured and the cycle repeated, maintaining or driving the system to a desired state. This review discusses recent and future developments in methods for implementing cybergenetic control systems, contrasts their capabilities with those of traditional biological methods of population control, and discusses future directions and outstanding challenges for the field.
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11
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Kumar S, Khammash M. Platforms for Optogenetic Stimulation and Feedback Control. Front Bioeng Biotechnol 2022; 10:918917. [PMID: 35757811 PMCID: PMC9213687 DOI: 10.3389/fbioe.2022.918917] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Harnessing the potential of optogenetics in biology requires methodologies from different disciplines ranging from biology, to mechatronics engineering, to control engineering. Light stimulation of a synthetic optogenetic construct in a given biological species can only be achieved via a suitable light stimulation platform. Emerging optogenetic applications entail a consistent, reproducible, and regulated delivery of light adapted to the application requirement. In this review, we explore the evolution of light-induction hardware-software platforms from simple illumination set-ups to sophisticated microscopy, microtiter plate and bioreactor designs, and discuss their respective advantages and disadvantages. Here, we examine design approaches followed in performing optogenetic experiments spanning different cell types and culture volumes, with induction capabilities ranging from single cell stimulation to entire cell culture illumination. The development of automated measurement and stimulation schemes on these platforms has enabled researchers to implement various in silico feedback control strategies to achieve computer-controlled living systems—a theme we briefly discuss in the last part of this review.
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Affiliation(s)
- Sant Kumar
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Basel, Switzerland
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Basel, Switzerland
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12
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León-Buitimea A, Balderas-Cisneros FDJ, Garza-Cárdenas CR, Garza-Cervantes JA, Morones-Ramírez JR. Synthetic Biology Tools for Engineering Microbial Cells to Fight Superbugs. Front Bioeng Biotechnol 2022; 10:869206. [PMID: 35600895 PMCID: PMC9114757 DOI: 10.3389/fbioe.2022.869206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/18/2022] [Indexed: 11/23/2022] Open
Abstract
With the increase in clinical cases of bacterial infections with multiple antibiotic resistance, the world has entered a health crisis. Overuse, inappropriate prescribing, and lack of innovation of antibiotics have contributed to the surge of microorganisms that can overcome traditional antimicrobial treatments. In 2017, the World Health Organization published a list of pathogenic bacteria, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli (ESKAPE). These bacteria can adapt to multiple antibiotics and transfer their resistance to other organisms; therefore, studies to find new therapeutic strategies are needed. One of these strategies is synthetic biology geared toward developing new antimicrobial therapies. Synthetic biology is founded on a solid and well-established theoretical framework that provides tools for conceptualizing, designing, and constructing synthetic biological systems. Recent developments in synthetic biology provide tools for engineering synthetic control systems in microbial cells. Applying protein engineering, DNA synthesis, and in silico design allows building metabolic pathways and biological circuits to control cellular behavior. Thus, synthetic biology advances have permitted the construction of communication systems between microorganisms where exogenous molecules can control specific population behaviors, induce intracellular signaling, and establish co-dependent networks of microorganisms.
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Affiliation(s)
- Angel León-Buitimea
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, Mexico
- Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Parque de Investigación e Innovación Tecnológica, Universidad Autónoma de Nuevo León, Apodaca, Mexico
| | - Francisco de Jesús Balderas-Cisneros
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, Mexico
- Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Parque de Investigación e Innovación Tecnológica, Universidad Autónoma de Nuevo León, Apodaca, Mexico
| | - César Rodolfo Garza-Cárdenas
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, Mexico
- Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Parque de Investigación e Innovación Tecnológica, Universidad Autónoma de Nuevo León, Apodaca, Mexico
| | - Javier Alberto Garza-Cervantes
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, Mexico
- Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Parque de Investigación e Innovación Tecnológica, Universidad Autónoma de Nuevo León, Apodaca, Mexico
| | - José Rubén Morones-Ramírez
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, Mexico
- Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Parque de Investigación e Innovación Tecnológica, Universidad Autónoma de Nuevo León, Apodaca, Mexico
- *Correspondence: José Rubén Morones-Ramírez,
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13
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Walker RSK, Pretorius IS. Synthetic biology for the engineering of complex wine yeast communities. NATURE FOOD 2022; 3:249-254. [PMID: 37118192 DOI: 10.1038/s43016-022-00487-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 03/11/2022] [Indexed: 04/30/2023]
Abstract
Wine fermentation is a representation of complex higher-order microbial interactions. Despite the beneficial properties that these communities bring to wine, their complexity poses challenges in predicting the nature and outcome of fermentation. Technological developments in synthetic biology enable the potential to engineer synthetic microbial communities for new purposes. Here we present the challenges and applications of engineered yeast communities in the context of a wine fermentation vessel, how this represents a model system to enable novel solutions for winemaking and introduce the concept of a 'synthetic' terroir. Furthermore, we introduce our vision for the application of control engineering.
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Affiliation(s)
- Roy S K Walker
- School of Natural Sciences, Macquarie University, Sydney, New South Wales, Australia.
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia.
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia.
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14
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Abstract
Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering-which is used to confer and balance properties such as stability and efficiency-with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.
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