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Li ZL, Pei S, Chen Z, Huang TY, Wang XD, Shen L, Chen X, Wang QQ, Wang DX, Ao YF. Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity. Nat Commun 2024; 15:8778. [PMID: 39389964 PMCID: PMC11467325 DOI: 10.1038/s41467-024-53048-0] [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: 02/26/2024] [Accepted: 09/30/2024] [Indexed: 10/12/2024] Open
Abstract
Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research.
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Affiliation(s)
- Zi-Lin Li
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Shuxin Pei
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China
| | - Ziying Chen
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China
| | - Teng-Yu Huang
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xu-Dong Wang
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China
| | - Lin Shen
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China.
- Yantai-Jingshi Institute of Material Genome Engineering, Yantai, China.
| | - Xuebo Chen
- Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education, College of Chemistry, Beijing Normal University, Beijing, China.
- Yantai-Jingshi Institute of Material Genome Engineering, Yantai, China.
- Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing, Yantai, China.
| | - Qi-Qiang Wang
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - De-Xian Wang
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yu-Fei Ao
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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2
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Huang Z, Gustave W, Bai S, Li Y, Li B, Elçin E, Jiang B, Jia Z, Zhang X, Shaheen SM, He F. Challenges and opportunities in commercializing whole-cell bioreporters in environmental application. ENVIRONMENTAL RESEARCH 2024; 262:119801. [PMID: 39147190 DOI: 10.1016/j.envres.2024.119801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 08/17/2024]
Abstract
Since the initial introduction of whole-cell bioreporters (WCBs) nearly 30 years ago, their high sensitivity, selectivity, and suitability for on-site detection have rendered them highly promising for environmental monitoring, medical diagnosis, food safety, biomanufacturing, and other fields. Especially in the environmental field, the technology provides a fast and efficient way to assess the bioavailability of pollutants in the environment. Despite these advantages, the technology has not been commercialized. This lack of commercialization is confusing, given the broad application prospects of WCBs. Over the years, numerous research papers have focused primarily on enhancing the sensitivity and selectivity of WCBs, with little attention paid to their wider commercial applications. So far, there is no a critical review has been published yet on this topic. Therefore, in this article we critically reviewed the research progress of WCBs over the past three decades, assessing the performance and limitations of current systems to understand the barriers to commercial deployment. By identifying these obstacles, this article provided researchers and industry stakeholders with deeper insights into the challenges hindering market entry and inspire further research toward overcoming these barriers, thereby facilitating the commercialization of WCBs as a promising technology for environmental monitoring.
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Affiliation(s)
- Zefeng Huang
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi, 214122, China
| | - Williamson Gustave
- School of Chemistry, Environmental & Life Sciences, University of the Bahamas, Nassau, 4912, Bahamas
| | - Shanshan Bai
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi, 214122, China
| | - Yongshuo Li
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi, 214122, China
| | - Boling Li
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, 215123, China; Meadows Center for Water and the Environment, Texas State University, San Marcos, TX, 78666, USA
| | - Evrim Elçin
- Department of Agricultural Biotechnology, Division of Enzyme and Microbial Biotechnology, Faculty of Agriculture, Aydın Adnan Menderes University, Aydın, 09970, Turkey
| | - Bo Jiang
- School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Zhemin Jia
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi, 214122, China
| | - Xiaokai Zhang
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi, 214122, China.
| | - Sabry M Shaheen
- University of Wuppertal, School of Architecture and Civil Engineering, Institute of Foundation Engineering, Water- and Waste-Management, Laboratory of Soil- and Groundwater-Management, Pauluskirchstraße 7, 42285 Wuppertal, Germany; King Abdulaziz University, Faculty of Environmental Sciences, Department of Agriculture, 21589 Jeddah, Saudi Arabia; University of Kafrelsheikh, Faculty of Agriculture, Department of Soil and Water Sciences, 33516, Kafr El-Sheikh, Egypt
| | - Feng He
- Institute of Environmental Processes and Pollution Control, School of Environment and Ecology, Jiangnan University, Wuxi, 214122, China
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3
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Fontana J, Sparkman-Yager D, Faulkner I, Cardiff R, Kiattisewee C, Walls A, Primo TG, Kinnunen PC, Garcia Martin H, Zalatan JG, Carothers JM. Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling. Nat Commun 2024; 15:6341. [PMID: 39068154 PMCID: PMC11283517 DOI: 10.1038/s41467-024-50528-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 07/12/2024] [Indexed: 07/30/2024] Open
Abstract
Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.
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Affiliation(s)
- Jason Fontana
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - David Sparkman-Yager
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Ian Faulkner
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Ryan Cardiff
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Cholpisit Kiattisewee
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA
- Department of Chemistry, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Aria Walls
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Tommy G Primo
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Patrick C Kinnunen
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, CA, USA
- DOE Agile BioFoundry, Emeryville, CA, USA
| | - Hector Garcia Martin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Biofuels and Bioproducts Division, DOE Joint BioEnergy Institute, Emeryville, CA, USA
- DOE Agile BioFoundry, Emeryville, CA, USA
| | - Jesse G Zalatan
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA.
- Department of Chemistry, University of Washington, Seattle, WA, USA.
| | - James M Carothers
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, USA.
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA.
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Dong Y, Wang J, Chen L, Chen H, Dang S, Li F. Aptamer-based assembly systems for SARS-CoV-2 detection and therapeutics. Chem Soc Rev 2024; 53:6830-6859. [PMID: 38829187 DOI: 10.1039/d3cs00774j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Nucleic acid aptamers are oligonucleotide chains with molecular recognition properties. Compared with antibodies, aptamers show advantages given that they are readily produced via chemical synthesis and elicit minimal immunogenicity in biomedicine applications. Notably, aptamer-encoded nucleic acid assemblies further improve the binding affinity of aptamers with the targets due to their multivalent synergistic interactions. Specially, aptamers can be engineered with special topological arrangements in nucleic acid assemblies, which demonstrate spatial and valence matching towards antigens on viruses, thus showing potential in the detection and therapeutic applications of viruses. This review presents the recent progress on the aptamers explored for SARS-CoV-2 detection and infection treatment, wherein applications of aptamer-based assembly systems are introduced in detail. Screening methods and chemical modification strategies for aptamers are comprehensively summarized, and the types of aptamers employed against different target domains of SARS-CoV-2 are illustrated. The evolution of aptamer-based assembly systems for the detection and neutralization of SARS-CoV-2, as well as the construction principle and characteristics of aptamer-based DNA assemblies are demonstrated. The typically representative works are presented to demonstrate how to assemble aptamers rationally and elaborately for specific applications in SARS-CoV-2 diagnosis and neutralization. Finally, we provide deep insights into the current challenges and future perspectives towards aptamer-based nucleic acid assemblies for virus detection and neutralization in nanomedicine.
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Affiliation(s)
- Yuhang Dong
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.
| | - Jingping Wang
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.
| | - Ling Chen
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.
| | - Haonan Chen
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.
| | - Shuangbo Dang
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.
| | - Feng Li
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.
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5
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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6
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Ao YF, Dörr M, Menke MJ, Born S, Heuson E, Bornscheuer UT. Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity. Chembiochem 2024; 25:e202300754. [PMID: 38029350 DOI: 10.1002/cbic.202300754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 12/01/2023]
Abstract
Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.
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Affiliation(s)
- Yu-Fei Ao
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
- Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Zhongguancun North First Street 2, Beijing, 100190, China
- University of Chinese Academy of Sciences, Yuquan Road 19(A), Beijing, 100049, China
| | - Mark Dörr
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
| | - Marian J Menke
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
| | - Stefan Born
- Technische Universität Berlin, Chair of Bioprocess Engineering, Ackerstraße 76, 13355, Berlin, Germany
| | - Egon Heuson
- Univ. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181 UCCS, Unité de Catalyse et Chimie du Solide, 59000, Lille, France
| | - Uwe T Bornscheuer
- Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany
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7
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Wu J, Lv J, Zhao L, Zhao R, Gao T, Xu Q, Liu D, Yu Q, Ma F. Exploring the role of microbial proteins in controlling environmental pollutants based on molecular simulation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167028. [PMID: 37704131 DOI: 10.1016/j.scitotenv.2023.167028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/03/2023] [Accepted: 09/10/2023] [Indexed: 09/15/2023]
Abstract
Molecular simulation has been widely used to study microbial proteins' structural composition and dynamic properties, such as volatility, flexibility, and stability at the microscopic scale. Herein, this review describes the key elements of molecular docking and molecular dynamics (MD) simulations in molecular simulation; reviews the techniques combined with molecular simulation, such as crystallography, spectroscopy, molecular biology, and machine learning, to validate simulation results and bridge information gaps in the structure, microenvironmental changes, expression mechanisms, and intensity quantification; illustrates the application of molecular simulation, in characterizing the molecular mechanisms of interaction of microbial proteins with four different types of contaminants, namely heavy metals (HMs), pesticides, dyes and emerging contaminants (ECs). Finally, the review outlines the important role of molecular simulations in the study of microbial proteins for controlling environmental contamination and provides ideas for the application of molecular simulation in screening microbial proteins and incorporating targeted mutagenesis to obtain more effective contaminant control proteins.
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Affiliation(s)
- Jieting Wu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Jin Lv
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Lei Zhao
- State Key Laboratory of Urban Water Resources & Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Ruofan Zhao
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Tian Gao
- Key Laboratory of Integrated Regulation and Resource Development of Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Xikang Road #1, Nanjing 210098, China
| | - Qi Xu
- PetroChina Fushun Petrochemical Company, Fushun 113000, China
| | - Dongbo Liu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Qiqi Yu
- School of Environmental Science, Liaoning University, Shenyang 110036, China
| | - Fang Ma
- State Key Laboratory of Urban Water Resources & Environment, Harbin Institute of Technology, Harbin 150090, China.
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8
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Motoche-Monar C, Ordoñez JE, Chang O, Gonzales-Zubiate FA. gRNA Design: How Its Evolution Impacted on CRISPR/Cas9 Systems Refinement. Biomolecules 2023; 13:1698. [PMID: 38136570 PMCID: PMC10741458 DOI: 10.3390/biom13121698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/05/2023] [Accepted: 06/12/2023] [Indexed: 12/24/2023] Open
Abstract
Over the past decade, genetic engineering has witnessed a revolution with the emergence of a relatively new genetic editing tool based on RNA-guided nucleases: the CRISPR/Cas9 system. Since the first report in 1987 and characterization in 2007 as a bacterial defense mechanism, this system has garnered immense interest and research attention. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system capable of modifying the genomes of a wide range of organisms. The refinement of these modifications encompasses diverse approaches, including the development of more accurate nucleases, understanding of the cellular context and epigenetic conditions, and the re-designing guide RNAs (gRNAs). Considering the critical importance of the correct performance of CRISPR/Cas9 systems, our scope will emphasize the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting the evolution of their computational architecture and gRNA characteristics over the years. Our study explains computational approaches that use machine learning techniques, neural networks, and gRNA/target interactions data to enable predictions and classifications. This review could open the door to a dynamic community that uses up-to-date algorithms to optimize and create promising gRNAs, suitable for modern CRISPR/Cas9 engineering.
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Affiliation(s)
- Cristofer Motoche-Monar
- School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador
| | - Julián E. Ordoñez
- School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador
| | - Oscar Chang
- Departamento de Electrónica, Universidad Simon Bolivar, Caracas 1080, Venezuela
- MIND Research Group, Model Intelligent Networks Development, Urcuquí 100119, Ecuador
| | - Fernando A. Gonzales-Zubiate
- School of Biological Sciences and Engineering, Yachay Tech University, Urcuquí 100119, Ecuador
- MIND Research Group, Model Intelligent Networks Development, Urcuquí 100119, Ecuador
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9
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Qin J, Kurt E, LBassi T, Sa L, Xie D. Biotechnological production of omega-3 fatty acids: current status and future perspectives. Front Microbiol 2023; 14:1280296. [PMID: 38029217 PMCID: PMC10662050 DOI: 10.3389/fmicb.2023.1280296] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Omega-3 fatty acids, including alpha-linolenic acids (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), have shown major health benefits, but the human body's inability to synthesize them has led to the necessity of dietary intake of the products. The omega-3 fatty acid market has grown significantly, with a global market from an estimated USD 2.10 billion in 2020 to a predicted nearly USD 3.61 billion in 2028. However, obtaining a sufficient supply of high-quality and stable omega-3 fatty acids can be challenging. Currently, fish oil serves as the primary source of omega-3 fatty acids in the market, but it has several drawbacks, including high cost, inconsistent product quality, and major uncertainties in its sustainability and ecological impact. Other significant sources of omega-3 fatty acids include plants and microalgae fermentation, but they face similar challenges in reducing manufacturing costs and improving product quality and sustainability. With the advances in synthetic biology, biotechnological production of omega-3 fatty acids via engineered microbial cell factories still offers the best solution to provide a more stable, sustainable, and affordable source of omega-3 fatty acids by overcoming the major issues associated with conventional sources. This review summarizes the current status, key challenges, and future perspectives for the biotechnological production of major omega-3 fatty acids.
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Affiliation(s)
| | | | | | | | - Dongming Xie
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, United States
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10
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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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11
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Khamwachirapithak P, Sae-Tang K, Mhuantong W, Tanapongpipat S, Zhao XQ, Liu CG, Wei DQ, Champreda V, Runguphan W. Optimizing Ethanol Production in Saccharomyces cerevisiae at Ambient and Elevated Temperatures through Machine Learning-Guided Combinatorial Promoter Modifications. ACS Synth Biol 2023; 12:2897-2908. [PMID: 37681736 PMCID: PMC10594650 DOI: 10.1021/acssynbio.3c00199] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Indexed: 09/09/2023]
Abstract
Bioethanol has gained popularity in recent decades as an ecofriendly alternative to fossil fuels due to increasing concerns about global climate change. However, economically viable ethanol fermentation remains a challenge. High-temperature fermentation can reduce production costs, but Saccharomyces cerevisiae yeast strains normally ferment poorly under high temperatures. In this study, we present a machine learning (ML) approach to optimize bioethanol production in S. cerevisiae by fine-tuning the promoter activities of three endogenous genes. We created 216 combinatorial strains of S. cerevisiae by replacing native promoters with five promoters of varying strengths to regulate ethanol production. Promoter replacement resulted in a 63% improvement in ethanol production at 30 °C. We created an ML-guided workflow by utilizing XGBoost to train high-performance models based on promoter strengths and cellular metabolite concentrations obtained from ethanol production of 216 combinatorial strains at 30 °C. This strategy was then applied to optimize ethanol production at 40 °C, where we selected 31 strains for experimental fermentation. This reduced experimental load led to a 7.4% increase in ethanol production in the second round of the ML-guided workflow. Our study offers a comprehensive library of promoter strength modifications for key ethanol production enzymes, showcasing how machine learning can guide yeast strain optimization and make bioethanol production more cost-effective and efficient. Furthermore, we demonstrate that metabolic engineering processes can be accelerated and optimized through this approach.
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Affiliation(s)
- Peerapat Khamwachirapithak
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Kittapong Sae-Tang
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Wuttichai Mhuantong
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Sutipa Tanapongpipat
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Xin-Qing Zhao
- State
Key Laboratory of Microbial Metabolism, Joint International Research
Laboratory of Metabolic & Developmental Sciences, School of Life
Sciences and Biotechnology, Shanghai Jiao
Tong University, Shanghai 200240, People’s
Republic of China
| | - Chen-Guang Liu
- State
Key Laboratory of Microbial Metabolism, Joint International Research
Laboratory of Metabolic & Developmental Sciences, School of Life
Sciences and Biotechnology, Shanghai Jiao
Tong University, Shanghai 200240, People’s
Republic of China
| | - Dong-Qing Wei
- Department
of Bioinformatics and Biological Statistics, School of Life Sciences
and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
| | - Verawat Champreda
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
| | - Weerawat Runguphan
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency
(NSTDA) 111 Thailand Science Park, Phahonyothin Road, Khlong
Nueng, Khlong Luang, Pathum Thani 12120, Thailand
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12
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Sequeiros C, Vázquez C, Banga JR, Otero-Muras I. Automated Design of Synthetic Gene Circuits in the Presence of Molecular Noise. ACS Synth Biol 2023; 12:2865-2876. [PMID: 37812682 PMCID: PMC10726474 DOI: 10.1021/acssynbio.3c00033] [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: 01/17/2023] [Indexed: 10/11/2023]
Abstract
Microorganisms (mainly bacteria and yeast) are frequently used as hosts for genetic constructs in synthetic biology applications. Molecular noise might have a significant effect on the dynamics of gene regulation in microbial cells, mainly attributed to the low copy numbers of mRNA species involved. However, the inclusion of molecular noise in the automated design of biocircuits is not a common practice due to the computational burden linked to the chemical master equation describing the dynamics of stochastic gene regulatory circuits. Here, we address the automated design of synthetic gene circuits under the effect of molecular noise combining a mixed integer nonlinear global optimization method with a partial integro-differential equation model describing the evolution of stochastic gene regulatory systems that approximates very efficiently the chemical master equation. We demonstrate the performance of the proposed methodology through a number of examples of relevance in synthetic biology, including different bimodal stochastic gene switches, robust stochastic oscillators, and circuits capable of achieving biochemical adaptation under noise.
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Affiliation(s)
- Carlos Sequeiros
- Computational
Biology Lab, MBG-CSIC, Spanish National
Research Council, 36143 Pontevedra, Spain
| | - Carlos Vázquez
- Department
of Mathematics and CITIC, Universidade da
Coruña, 15071 A Coruña, Spain
| | - Julio R. Banga
- Computational
Biology Lab, MBG-CSIC, Spanish National
Research Council, 36143 Pontevedra, Spain
| | - Irene Otero-Muras
- Computational
Synthetic Biology Group, Institute for Integrative
Systems Biology: I2SysBio (CSIC-UV), 46980 Valencia, Spain
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13
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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14
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Cai P, Liu S, Zhang D, Xing H, Han M, Liu D, Gong L, Hu QN. SynBioTools: a one-stop facility for searching and selecting synthetic biology tools. BMC Bioinformatics 2023; 24:152. [PMID: 37069545 PMCID: PMC10111727 DOI: 10.1186/s12859-023-05281-5] [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: 01/09/2023] [Accepted: 04/11/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.
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Affiliation(s)
- Pengli Cai
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Sheng Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dachuan Zhang
- Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, 8093, Zurich, Switzerland
| | - Huadong Xing
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengying Han
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Dongliang Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Linlin Gong
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
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15
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Kumar AK, Jain S, Jain S, Ritam M, Xia Y, Chandra R. Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107421. [PMID: 36805280 DOI: 10.1016/j.cmpb.2023.107421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex. METHODS We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions. RESULTS We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases. CONCLUSION We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.
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Affiliation(s)
- Amit Krishan Kumar
- Faculty of Electrical-Electronic Engineering, Duy Tan University, Da Nang, 550000, Vietnam; State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Snigdha Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Shirin Jain
- Department of Electronics and Communications Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - M Ritam
- Department of Chemical Engineering, Indian Institute of Technology Guwahati, Assam, India.
| | - Yuanqing Xia
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Rohitash Chandra
- Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney, NSW 2052, Australia.
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16
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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: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [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
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17
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Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, Lall B, Anees S, Pollmann K, Jain R. Review on machine learning-based bioprocess optimization, monitoring, and control systems. BIORESOURCE TECHNOLOGY 2023; 370:128523. [PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
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Affiliation(s)
- Partha Pratim Mondal
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Abhinav Galodha
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Vishal Kumar Verma
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, 382715, Gujarat, India
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
| | - Brejesh Lall
- Electrical Engineering Department, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Sanya Anees
- Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Bongora, Guwahati 781015, India
| | - Katrin Pollmann
- Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Rohan Jain
- Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany.
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18
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Wainaina S, Taherzadeh MJ. Automation and artificial intelligence in filamentous fungi-based bioprocesses: A review. BIORESOURCE TECHNOLOGY 2023; 369:128421. [PMID: 36462761 DOI: 10.1016/j.biortech.2022.128421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/25/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
By utilizing their powerful metabolic versatility, filamentous fungi can be utilized in bioprocesses aimed at achieving circular economy. With the current digital transformation within the biomanufacturing sector, the interest of automating fungi-based systems has intensified. The purpose of this paper was therefore to review the potentials connected to the use of automation and artificial intelligence in fungi-based systems. Automation is characterized by the substitution of manual tasks with mechanized tools. Artificial intelligence is, on the other hand, a domain within computer science that aims at designing tools and machines with the capacity to execute functions that would usually require human aptitude. Process flexibility, enhanced data reliability and increased productivity are some of the benefits of integrating automation and artificial intelligence in fungi-based bioprocesses. One of the existing gaps that requires further investigation is the use of such data-based technologies in the production of food from fungi.
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Affiliation(s)
- Steven Wainaina
- Swedish Centre for Resource Recovery, University of Borås, 50190 Borås, Sweden
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19
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Watcharawipas A, Runguphan W. Red yeasts and their carotenogenic enzymes for microbial carotenoid production. FEMS Yeast Res 2023; 23:6895548. [PMID: 36513367 DOI: 10.1093/femsyr/foac063] [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: 06/24/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
Carotenoids are C40 isoprene-based compounds with significant commercial interests that harbor diverse bioactivities. Prominent examples of carotenoids are beta-carotene, a precursor to vitamin A essential for proper eye health, and lycopene and astaxanthin, powerful antioxidants implicated in preventing cancers and atherosclerosis. Due to their benefits to human health, the market value for carotenoids is rapidly increasing and is projected to reach USD 1.7 billion by 2025. However, their production now relies on chemical synthesis and extraction from plants that pose risks to food management and numerous biological safety issues. Thus, carotenoid production from microbes is considered a promising strategy for achieving a healthy society with more sustainability. Red yeast is a heterogeneous group of basidiomycetous fungi capable of producing carotenoids. It is a critical source of microbial carotenoids from low-cost substrates. Carotenogenic enzymes from red yeasts have also been highly efficient, invaluable biological resources for biotechnological applications. In this minireview, we focus on red yeast as a promising source for microbial carotenoids, strain engineering strategies for improving carotenoid production in red yeasts, and potential applications of carotenogenic enzymes from red yeasts in conventional and nonconventional yeasts.
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Affiliation(s)
- Akaraphol Watcharawipas
- Department of Microbiology, Faculty of Science, Mahidol University, 272 Rama VI Road, Ratchathewi, Bangkok 10400, Thailand
| | - Weerawat Runguphan
- National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, Pathumthani 12120, Thailand
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20
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Daboussi F, Lindley ND. Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications. Methods Mol Biol 2023; 2553:1-20. [PMID: 36227536 DOI: 10.1007/978-1-0716-2617-7_1] [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: 06/16/2023]
Abstract
Metabolic engineering has evolved towards creating cell factories with increasingly complex pathways as economic criteria push biotechnology to higher value products to provide a sustainable source of speciality chemicals. Optimization of such pathways often requires high combinatory exploration of best pathway balance, and this has led to increasing use of high-throughput automated strain construction platforms or novel optimization techniques. In addition, the low catalytic efficiency of such pathways has shifted emphasis from gene expression strategies towards novel protein engineering to increase specific activity of the enzymes involved so as to limit the metabolic burden associated with excessively high pressure on ribosomal machinery when using massive overexpression systems. Metabolic burden is now generally recognized as a major hurdle to be overcome with consequences on genetic stability but also on the intensified performance needed industrially to attain the economic targets for successful product launch. Increasing awareness of the need to integrate novel genetic information into specific sites within the genome which not only enhance genetic stability (safe harbors) but also enable maximum expression profiles has led to genome-wide assessment of best integration sites, and bioinformatics will facilitate the identification of most probable landing pads within the genome.To facilitate the transfer of novel biotechnological potential to industrial-scale production, more attention, however, has to be paid to engineering metabolic fitness adapted to the specific stress conditions inherent to large-scale fermentation and the inevitable heterogeneity that will occur due to mass transfer limitations and the resulting deviation away from ideal conditions as seen in laboratory-scale validation of the engineered cells. To ensure smooth and rapid transfer of novel cell lines to industry with an accelerated passage through scale-up, better coordination is required form the onset between the biochemical engineers involved in process technology and the genetic engineers building the new strain so as to have an overall strategy able to maximize innovation at all levels. This should be one of our key objectives when building fermentation-friendly chassis organisms.
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Affiliation(s)
- Fayza Daboussi
- Toulouse White Biotechnology, Toulouse cedex 4, France
- Toulouse Biotechnology Institute, Toulouse cedex 4, France
| | - Nic D Lindley
- Toulouse White Biotechnology, Toulouse cedex 4, France.
- Toulouse Biotechnology Institute, Toulouse cedex 4, France.
- ASTAR Singapore Institute of Food and Biotechnology Innovation (SIFBI), Singapore, Singapore.
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21
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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22
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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: 29] [Impact Index Per Article: 14.5] [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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23
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Accuracy and data efficiency in deep learning models of protein expression. Nat Commun 2022; 13:7755. [PMID: 36517468 PMCID: PMC9751117 DOI: 10.1038/s41467-022-34902-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. We show that deep learning can achieve good prediction accuracy with much smaller datasets than previously thought. We demonstrate that controlled sequence diversity leads to substantial gains in data efficiency and employed Explainable AI to show that convolutional neural networks can finely discriminate between input DNA sequences. Our results provide guidelines for designing genotype-phenotype screens that balance cost and quality of training data, thus helping promote the wider adoption of deep learning in the biotechnology sector.
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24
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Sun D, Sun M, Zhang J, Lin X, Zhang Y, Lin F, Zhang P, Yang C, Song J. Computational tools for aptamer identification and optimization. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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26
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Yilmaz S, Nyerges A, van der Oost J, Church GM, Claassens NJ. Towards next-generation cell factories by rational genome-scale engineering. Nat Catal 2022. [DOI: 10.1038/s41929-022-00836-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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27
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Li H, Lyv Y, Zhou S, Yu S, Zhou J. Microbial cell factories for the production of flavonoids-barriers and opportunities. BIORESOURCE TECHNOLOGY 2022; 360:127538. [PMID: 35777639 DOI: 10.1016/j.biortech.2022.127538] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/24/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
Flavonoids are natural plant products with important nutritional value, health-promoting benefits, and therapeutic potential. The use of microbial cell factories to generate flavonoids is an appealing option. The microbial biosynthesis of flavonoids is compared to the classic plant extract approach in this review, and the pharmaceutical applications were presented. This paper summarize approaches for effective flavonoid biosynthesis from microorganisms, and discuss the challenges and prospects of microbial flavonoid biosynthesis. Finally, the barriers and strategies for industrial bio-production of flavonoids are highlighted. This review offers guidance on how to create robust microbial cell factories for producing flavonoids and other relevant chemicals.
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Affiliation(s)
- Hongbiao Li
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yunbin Lyv
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Shenghu Zhou
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Shiqin Yu
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jingwen Zhou
- Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; The Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
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28
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Pandi A, Diehl C, Yazdizadeh Kharrazi A, Scholz SA, Bobkova E, Faure L, Nattermann M, Adam D, Chapin N, Foroughijabbari Y, Moritz C, Paczia N, Cortina NS, Faulon JL, Erb TJ. A versatile active learning workflow for optimization of genetic and metabolic networks. Nat Commun 2022; 13:3876. [PMID: 35790733 PMCID: PMC9256728 DOI: 10.1038/s41467-022-31245-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 06/10/2022] [Indexed: 11/13/2022] Open
Abstract
Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities. Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.
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Affiliation(s)
- Amir Pandi
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
| | - Christoph Diehl
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | | | - Scott A Scholz
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Elizaveta Bobkova
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Léon Faure
- Micalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France
| | - Maren Nattermann
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - David Adam
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Nils Chapin
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Yeganeh Foroughijabbari
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Charles Moritz
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Nicole Paczia
- Core Facility for Metabolomics and Small Molecule Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Niña Socorro Cortina
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.,LiVeritas Biosciences, Inc., 432N Canal St.; Ste. 20, South San Francisco, CA, 94080, USA
| | - Jean-Loup Faulon
- Micalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France.,Genomique Metabolique, Genoscope, Institut Francois Jacob, CEA, CNRS, Univ Evry, University of Paris-Saclay, Evry, France.,Manchester Institute of Biotechnology, SYNBIOCHEM center, School of Chemistry, The University of Manchester, Manchester, UK
| | - Tobias J Erb
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany. .,SYNMIKRO Center of Synthetic Microbiology, Marburg, Germany.
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29
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Liao X, Ma H, Tang YJ. Artificial intelligence: a solution to involution of design–build–test–learn cycle. Curr Opin Biotechnol 2022; 75:102712. [DOI: 10.1016/j.copbio.2022.102712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/05/2022] [Accepted: 03/01/2022] [Indexed: 01/08/2023]
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30
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A Semi-Automated 3D-Printed Chainmail Design Algorithm with Preprogrammed Directional Functions for Hand Exoskeleton. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The problem of computerising the design and development of 3D-printed chainmail with programmed directional functions provides a basis for further research, including the automation of medical devices. The scope of the present research was focused on computational optimisation of the selection of materials and shapes for 3D printing, including the design of medical devices, which constitutes a significant scientific, technical, and clinical problem. The aim of this article was to solve the scientific problem of automated or semi-automated efficient and practical design of 3D-printed chainmail with programmed directional functions (variable stiffness/elasticity depending on the direction). We demonstrate for the first time that 3D-printed particles can be arranged into single-layer chainmail with a tunable one- or two-directional bending modulus for use in a medical hand exoskeleton. In the present work, we accomplished this in two ways: based on traditional programming and based on machine learning. This paper presents the novel results of our research, including 3D printouts, providing routes toward the wider implementation of adaptive chainmails. Our research resulted in an automated or semi-automated efficient and practical 3D printed chainmail design with programmed directional functions for a wrist exoskeleton with variable stiffness/flexibility, depending on the direction. We also compared two methodologies of planning and construction: the use of traditional software and machine-learning-based software, with the latter being more efficient for more complex chainmail designs.
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31
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Abstract
Background: security has become a major concern for smartphone users in line with the increasing use of mobile applications, which can be downloaded from unofficial sources. These applications make users vulnerable to penetration and viruses. Malicious software (malware) is unwanted software that is frequently used by cybercriminals to launch cyber-attacks. Therefore, the motive of the research was to detect malware early before infection by discovering it at the application-design level and not at the code level, where the virus will have already damaged the system. Methods: in this article, we proposed a malware detection method at the design level based on reverse engineering, the unified modeling language (UML) environment, and the web ontology language (OWL). The proposed method detected “Data_Send_Trojan” malware by designing a UML model that simulated the structure of the malware. Then, by generating the ontology of the model, and using RDF query language (SPARQL) to create certain queries, the malware was correctly detected. In addition, we proposed a new classification of malware that was suitable for design detection. Results: the proposed method detected Trojan malware that appeared 552 times in a sample of 600 infected android application packages (APK). The experimental results showed a good performance in detecting malware at the design level with precision and recall of 92% and 91%, respectively. As the dataset increased, the accuracy of detection increased significantly, which made this methodology promising.
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32
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Hong Y, Nguyen T, Arbter P, Utesch T, Zeng A. Phenotype analysis of cultivation processes via unsupervised machine learning: Demonstration for Clostridium pasteurianum. Eng Life Sci 2022; 22:85-99. [PMID: 35140556 PMCID: PMC8811730 DOI: 10.1002/elsc.202100114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/02/2021] [Accepted: 11/19/2021] [Indexed: 11/23/2022] Open
Abstract
A novel approach of phenotype analysis of fermentation-based bioprocesses based on unsupervised learning (clustering) is presented. As a prior identification of phenotypes and conditional interrelations is desired to control fermentation performance, an automated learning method to output reference phenotypes (defined as vector of biomass-specific rates) was developed and the necessary computing process and parameters were assessed. For its demonstration, time series data of 90 Clostridium pasteurianum cultivations were used which feature a broad spectrum of solventogenic and acidogenic phenotypes, while 14 clusters of phenotypic manifestations were identified. The analysis of reference phenotypes showed distinct differences, where potential conditionalities were exemplary isolated. Further, cluster-based balancing of carbon and ATP or the use of reference phenotypes as indicator for bioprocess monitoring were demonstrated to highlight the perks of this approach. Overall, such analysis depends strongly on the quality of the data and experimental validations will be required before conclusions. However, the automated, streamlined and abstracted approach diminishes the need of individual evaluation of all noisy dataset and showed promising results, which could be transferred to strains with comparably wide-ranging phenotypic manifestations or as indicators for repeated bioprocesses with clearly defined target.
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Affiliation(s)
- Yaeseong Hong
- Institute of Bioprocess and Biosystems EngineeringHamburg University of TechnologyTUHHHamburgGermany
| | - Tom Nguyen
- Institute of Bioprocess and Biosystems EngineeringHamburg University of TechnologyTUHHHamburgGermany
| | - Philipp Arbter
- Institute of Bioprocess and Biosystems EngineeringHamburg University of TechnologyTUHHHamburgGermany
| | - Tyll Utesch
- Institute of Bioprocess and Biosystems EngineeringHamburg University of TechnologyTUHHHamburgGermany
| | - An‐Ping Zeng
- Institute of Bioprocess and Biosystems EngineeringHamburg University of TechnologyTUHHHamburgGermany
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33
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Raghushaker CR, Rodrigues J, Nayak SG, Ray S, Urala AS, Satyamoorthy K, Mahato KK. Fluorescence and Photoacoustic Spectroscopy-Based Assessment of Mitochondrial Dysfunction in Oral Cancer Together with Machine Learning: A Pilot Study. Anal Chem 2021; 93:16520-16527. [PMID: 34846862 DOI: 10.1021/acs.analchem.1c03650] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The current study reports an integrated approach of machine learning and tryptophan fluorescence and photoacoustic spectral properties to assess the mitochondrial status under oral pathological conditions. The mitochondria in the study were isolated from oral cancer tissues and adjacent normal counterparts, and the corresponding fluorescence and photoacoustic spectra of tryptophan were recorded at 281 nm pulsed laser excitations. A set of features were selected from the pre-processed spectra and were used to classify the data using support vector machine (SVM) learning in the MATLAB platform. SVM analysis demonstrated clear differentiation between mitochondria isolated from normal and cancer tissues for fluorescence (sensitivity, 86.6%; specificity, 90%) and photoacoustic (sensitivity, 86.6%; specificity, 96.6%) measurements. Further investigation into the influence of change in protein conformation on the nature of tryptophan spectral properties was evaluated by 8-anilino-1-naphthalene sulfonic acid (ANS) fluorescence assay. The impact of protein structural changes on the mitochondrial functions was also estimated by mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and cytochrome c oxidase (COX) assays, suggesting an altered mitochondrial function. The findings indicate that tryptophan fluorescence and photoacoustic spectral properties together with machine learning algorithms may delineate the mitochondrial functional status in vitro, indicating its translational potential.
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Affiliation(s)
| | - Jackson Rodrigues
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Subramanya G Nayak
- Department of Electronics & Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Satadru Ray
- Department of Surgery, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Mangalore 575001, India
| | - Arun S Urala
- Department of Orthodontics and Dentofacial Orthopaedics, Manipal College of Dental Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Kapaettu Satyamoorthy
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal 576104, India
| | - Krishna Kishore Mahato
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal 576104, India
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34
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Bathe U, Leong BJ, McCarty DR, Henry CS, Abraham PE, Wilson MA, Hanson AD. The Moderately (D)efficient Enzyme: Catalysis-Related Damage In Vivo and Its Repair. Biochemistry 2021; 60:3555-3565. [PMID: 34729986 DOI: 10.1021/acs.biochem.1c00613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Enzymes have in vivo life spans. Analysis of life spans, i.e., lifetime totals of catalytic turnovers, suggests that nonsurvivable collateral chemical damage from the very reactions that enzymes catalyze is a common but underdiagnosed cause of enzyme death. Analysis also implies that many enzymes are moderately deficient in that their active-site regions are not naturally as hardened against such collateral damage as they could be, leaving room for improvement by rational design or directed evolution. Enzyme life span might also be improved by engineering systems that repair otherwise fatal active-site damage, of which a handful are known and more are inferred to exist. Unfortunately, the data needed to design and execute such improvements are lacking: there are too few measurements of in vivo life span, and existing information about the extent, nature, and mechanisms of active-site damage and repair during normal enzyme operation is too scarce, anecdotal, and speculative to act on. Fortunately, advances in proteomics, metabolomics, cheminformatics, comparative genomics, and structural biochemistry now empower a systematic, data-driven approach for identifying, predicting, and validating instances of active-site damage and its repair. These capabilities would be practically useful in enzyme redesign and improvement of in-use stability and could change our thinking about which enzymes die young in vivo, and why.
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Affiliation(s)
- Ulschan Bathe
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, United States
| | - Bryan J Leong
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, United States
| | - Donald R McCarty
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, United States
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Paul E Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Mark A Wilson
- Department of Biochemistry and Redox Biology Center, University of Nebraska, Lincoln, Nebraska 68588, United States
| | - Andrew D Hanson
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, United States
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35
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Liu Z, Wang J, Nielsen J. Yeast synthetic biology advances biofuel production. Curr Opin Microbiol 2021; 65:33-39. [PMID: 34739924 DOI: 10.1016/j.mib.2021.10.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/10/2021] [Accepted: 10/11/2021] [Indexed: 01/24/2023]
Abstract
Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production.
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Affiliation(s)
- Zihe Liu
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Junyang Wang
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China
| | - Jens Nielsen
- College of Life Science and Technology, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, 100029 Beijing, China; Department of Biology and Biological Engineering, Chalmers University of Technology, SE412 96 Gothenburg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen N, Denmark.
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36
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Mey F, Clauwaert J, Van Huffel K, Waegeman W, De Mey M. Improving the performance of machine learning models for biotechnology: The quest for deus ex machina. Biotechnol Adv 2021; 53:107858. [PMID: 34695560 DOI: 10.1016/j.biotechadv.2021.107858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 11/24/2022]
Abstract
Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues.
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Affiliation(s)
- Friederike Mey
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000 Ghent, Belgium
| | - Jim Clauwaert
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
| | - Kirsten Van Huffel
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000 Ghent, Belgium
| | - Willem Waegeman
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
| | - Marjan De Mey
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000 Ghent, Belgium.
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37
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Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers 2021; 26:1893-1913. [PMID: 34686947 PMCID: PMC8536481 DOI: 10.1007/s11030-021-10326-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.
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Affiliation(s)
- Chandrabose Selvaraj
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
| | - Ishwar Chandra
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Sanjeev Kumar Singh
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
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Pleiss J. Standardized Data, Scalable Documentation, Sustainable Storage – EnzymeML As A Basis For FAIR Data Management In Biocatalysis. ChemCatChem 2021. [DOI: 10.1002/cctc.202100822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry University of Stuttgart Allmandring 31 70569 Stuttgart Germany
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39
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Kell DB. The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes. Molecules 2021; 26:5629. [PMID: 34577099 PMCID: PMC8470029 DOI: 10.3390/molecules26185629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/03/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK;
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs Lyngby, Denmark
- Mellizyme Biotechnology Ltd., IC1, Liverpool Science Park, Mount Pleasant, Liverpool L3 5TF, UK
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40
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Khaleghi MK, Savizi ISP, Lewis NE, Shojaosadati SA. Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters. Biotechnol J 2021; 16:e2100212. [PMID: 34390201 DOI: 10.1002/biot.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/06/2022]
Abstract
Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mohammad Karim Khaleghi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Iman Shahidi Pour Savizi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, USA.,Department of Pediatrics, University of California, San Diego, USA
| | - Seyed Abbas Shojaosadati
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
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41
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Tinte MM, Chele KH, van der Hooft JJJ, Tugizimana F. Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview. Metabolites 2021; 11:445. [PMID: 34357339 PMCID: PMC8305945 DOI: 10.3390/metabo11070445] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 12/27/2022] Open
Abstract
Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.
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Affiliation(s)
- Morena M. Tinte
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | - Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | | | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa
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42
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Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction. Metab Eng 2021; 67:227-236. [PMID: 34242777 DOI: 10.1016/j.ymben.2021.07.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 06/17/2021] [Accepted: 07/05/2021] [Indexed: 01/14/2023]
Abstract
Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R2 = 0.87). However, the model had reduced predictability for low performance strains (0.01-1 g/L, R2 = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.
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Kumar P, Adamczyk PA, Zhang X, Andrade RB, Romero PA, Ramanathan P, Reed JL. Active and machine learning-based approaches to rapidly enhance microbial chemical production. Metab Eng 2021; 67:216-226. [PMID: 34229079 DOI: 10.1016/j.ymben.2021.06.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/12/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)-requiring many experimental datasets for their parameterization-while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.
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Affiliation(s)
- Prashant Kumar
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA; ZS Associates, 1560 Sherman Ave, Evanston, IL, 60201, USA
| | - Paul A Adamczyk
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA
| | - Xiaolin Zhang
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA
| | - Ramon Bonela Andrade
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA
| | - Philip A Romero
- Department of Biochemistry, University of Wisconsin-Madison, 440 Henry Mall, Madison, WI, 53706, USA
| | - Parameswaran Ramanathan
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA.
| | - Jennifer L Reed
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Dr., Madison, WI, 53706, USA
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Wong M, Badri A, Gasparis C, Belfort G, Koffas M. Modular optimization in metabolic engineering. Crit Rev Biochem Mol Biol 2021; 56:587-602. [PMID: 34180323 DOI: 10.1080/10409238.2021.1937928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
There is an increasing demand for bioproducts produced by metabolically engineered microbes, such as pharmaceuticals, biofuels, biochemicals and other high value compounds. In order to meet this demand, modular optimization, the optimizing of subsections instead of the whole system, has been adopted to engineer cells to overproduce products. Research into modularity has focused on traditional approaches such as DNA, RNA, and protein-level modularity of intercellular machinery, by optimizing metabolic pathways for enhanced production. While research into these traditional approaches continues, limitations such as scale-up and time cost hold them back from wider use, while at the same time there is a shift to more novel methods, such as moving from episomal expression to chromosomal integration. Recently, nontraditional approaches such as co-culture systems and cell-free metabolic engineering (CFME) are being investigated for modular optimization. Co-culture modularity looks to optimally divide the metabolic burden between different hosts. CFME seeks to modularly optimize metabolic pathways in vitro, both speeding up the design of such systems and eliminating the issues associated with live hosts. In this review we will examine both traditional and nontraditional approaches for modular optimization, examining recent developments and discussing issues and emerging solutions for future research in metabolic engineering.
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Affiliation(s)
- Matthew Wong
- Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Abinaya Badri
- Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Christopher Gasparis
- Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Georges Belfort
- Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Mattheos Koffas
- Howard P. Isermann Department of Chemical and Biological Engineering and the Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA
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45
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Runguphan W, Sae-Tang K, Tanapongpipat S. Recent advances in the microbial production of isopentanol (3-Methyl-1-butanol). World J Microbiol Biotechnol 2021; 37:107. [PMID: 34043086 DOI: 10.1007/s11274-021-03074-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/17/2021] [Indexed: 10/21/2022]
Abstract
As the effects of climate change become increasingly severe, metabolic engineers and synthetic biologists are looking towards greener sources for transportation fuels. The design and optimization of microorganisms to produce gasoline, diesel, and jet fuel compounds from renewable feedstocks can significantly reduce dependence on fossil fuels and thereby produce fewer emissions. Over the past two decades, a tremendous amount of research has contributed to the development of microbial strains to produce advanced fuel compounds, including branched-chain higher alcohols (BCHAs) such as isopentanol (3-methyl-1-butanol; 3M1B) and isobutanol (2-methyl-1-propanol). In this review, we provide an overview of recent advances in the development of microbial strains for the production of isopentanol in both conventional and non-conventional hosts. We also highlight metabolic engineering strategies that may be employed to enhance product titers, reduce end-product toxicity, and broaden the substrate range to non-sugar carbon sources. Finally, we offer glimpses into some promising future directions in the development of isopentanol producing microbial strains.
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Affiliation(s)
- Weerawat Runguphan
- National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, 12120, Pathumthani, Thailand.
| | - Kittapong Sae-Tang
- National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, 12120, Pathumthani, Thailand
| | - Sutipa Tanapongpipat
- National Center for Genetic Engineering and Biotechnology, 113 Thailand Science Park, Paholyothin Road, Klong 1, Klong Luang, 12120, Pathumthani, Thailand
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46
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Wu Z, Johnston KE, Arnold FH, Yang KK. Protein sequence design with deep generative models. Curr Opin Chem Biol 2021; 65:18-27. [PMID: 34051682 DOI: 10.1016/j.cbpa.2021.04.004] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/02/2021] [Accepted: 04/07/2021] [Indexed: 12/20/2022]
Abstract
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.
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Affiliation(s)
- Zachary Wu
- Division of Chemistry and Chemical Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA
| | - Kadina E Johnston
- Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA
| | - Frances H Arnold
- Division of Chemistry and Chemical Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA; Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, 91125, CA, USA
| | - Kevin K Yang
- Microsoft Research New England, 1 Memorial Drive, Cambridge, 02142, MA, USA.
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Ali SA, Mittal D, Kaur G. In-situ monitoring of xenobiotics using genetically engineered whole-cell-based microbial biosensors: recent advances and outlook. World J Microbiol Biotechnol 2021; 37:81. [PMID: 33843020 DOI: 10.1007/s11274-021-03024-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 02/25/2021] [Indexed: 02/07/2023]
Abstract
Industrialisation, directly or indirectly, exposes humans to various xenobiotics. The increased magnitude of chemical pesticides and toxic heavy metals in the environment, as well as their intrusion into the food chain, seriously threatens human health. Therefore, the surveillance of xenobiotics is crucial for social safety and security. Online investigation by traditional methods is not sufficient for the detection and identification of such compounds because of the high costs and their complexity. Advancement in the field of genetic engineering provides a potential opportunity to use genetically modified microorganisms. In this regard, whole-cell-based microbial biosensors (WCBMB) represent an essential tool that couples genetically engineered organisms with an operator/promoter derived from a heavy metal-resistant operon combined with a regulatory protein in the gene circuit. The plasmid controls the expression of the reporter gene, such as gfp, luc, lux and lacZ, to an inducible gene promoter and has been widely applied to assay toxicity and bioavailability. This review summarises the recent trends in the development and application of microbial biosensors and the use of mobile genes for biomedical and environmental safety concerns.
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Affiliation(s)
- Syed Azmal Ali
- Proteomics and Cell Biology Lab, Animal Biotechnology Center, National Dairy Research Institute, Karnal, Haryana, India. .,Proteomics and Cell Biology Lab, Animal Biotechnology Center, ICAR-National Dairy Research Institute, 132001, Karnal, Haryana, India.
| | - Deepti Mittal
- Animal Biochemistry Division, National Dairy Research Institute, Karnal, Haryana, India
| | - Gurjeet Kaur
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, 2052, Sydney, NSW, Australia
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Affiliation(s)
- Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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49
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Tan X, Letendre JH, Collins JJ, Wong WW. Synthetic biology in the clinic: engineering vaccines, diagnostics, and therapeutics. Cell 2021; 184:881-898. [PMID: 33571426 PMCID: PMC7897318 DOI: 10.1016/j.cell.2021.01.017] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/17/2022]
Abstract
Synthetic biology is a design-driven discipline centered on engineering novel biological functions through the discovery, characterization, and repurposing of molecular parts. Several synthetic biological solutions to critical biomedical problems are on the verge of widespread adoption and demonstrate the burgeoning maturation of the field. Here, we highlight applications of synthetic biology in vaccine development, molecular diagnostics, and cell-based therapeutics, emphasizing technologies approved for clinical use or in active clinical trials. We conclude by drawing attention to recent innovations in synthetic biology that are likely to have a significant impact on future applications in biomedicine.
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Affiliation(s)
- Xiao Tan
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Division of Gastroenterology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA; Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA; Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
| | - Justin H Letendre
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Biological Design Center, Boston University, Boston, MA 02215, USA
| | - James J Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Synthetic Biology Center, MIT, 77 Massachusetts Ave., Cambridge, MA 02139, USA; Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA.
| | - Wilson W Wong
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Biological Design Center, Boston University, Boston, MA 02215, USA.
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50
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Calzini MA, Malico AA, Mitchler MM, Williams GJ. Protein engineering for natural product biosynthesis and synthetic biology applications. Protein Eng Des Sel 2021; 34:gzab015. [PMID: 34137436 PMCID: PMC8209613 DOI: 10.1093/protein/gzab015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/12/2021] [Accepted: 05/17/2021] [Indexed: 11/14/2022] Open
Abstract
As protein engineering grows more salient, many strategies have emerged to alter protein structure and function, with the goal of redesigning and optimizing natural product biosynthesis. Computational tools, including machine learning and molecular dynamics simulations, have enabled the rational mutagenesis of key catalytic residues for enhanced or altered biocatalysis. Semi-rational, directed evolution and microenvironment engineering strategies have optimized catalysis for native substrates and increased enzyme promiscuity beyond the scope of traditional rational approaches. These advances are made possible using novel high-throughput screens, including designer protein-based biosensors with engineered ligand specificity. Herein, we detail the most recent of these advances, focusing on polyketides, non-ribosomal peptides and isoprenoids, including their native biosynthetic logic to provide clarity for future applications of these technologies for natural product synthetic biology.
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Affiliation(s)
- Miles A Calzini
- Department of Chemistry, NC State University, Raleigh, NC 27695-8204, USA
| | - Alexandra A Malico
- Department of Chemistry, NC State University, Raleigh, NC 27695-8204, USA
| | - Melissa M Mitchler
- Department of Chemistry, NC State University, Raleigh, NC 27695-8204, USA
| | - Gavin J Williams
- Department of Chemistry, NC State University, Raleigh, NC 27695-8204, USA
- Comparative Medicine Institute, NC State University Raleigh, Raleigh, NC 27695-8204, USA
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