1
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Sveshnikova A, Oftadeh O, Hatzimanikatis V. Designing pathways for bioproducing complex chemicals by combining tools for pathway extraction and ranking. Nat Commun 2025; 16:4839. [PMID: 40413182 PMCID: PMC12103536 DOI: 10.1038/s41467-025-59827-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 05/06/2025] [Indexed: 05/27/2025] Open
Abstract
The synthesis of many important biochemicals involves complex molecules and numerous reactions. The design and optimization of whole-cell biocatalysts for the production of these molecules requires metabolic modeling to extract production pathways from biochemical databases and integrate them into genome-scale models of the host. However, the synthesis of such complex molecules often requires reactions from multiple pathways operating in balanced subnetworks that are not assembled in existing databases. Here, we present SubNetX, a computational algorithm that extracts reactions from a database and assembles balanced subnetworks to produce a target biochemical from selected precursor metabolites, energy currencies, and cofactors. These subnetworks can be integrated into whole-cell models, allowing the reconstruction and ranking of alternative biosynthetic pathways based on yield, length, and other design goals. We apply SubNetX to 70 industrially relevant natural and synthetic chemicals to demonstrate the application of this pipeline.
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Affiliation(s)
- Anastasia Sveshnikova
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Omid Oftadeh
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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2
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Chen A, Peng X, Shen T, Zheng L, Wu D, Wang S. Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis. MLIFE 2025; 4:107-125. [PMID: 40313979 PMCID: PMC12042125 DOI: 10.1002/mlf2.70009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2024] [Revised: 02/06/2025] [Accepted: 02/13/2025] [Indexed: 05/03/2025]
Abstract
Biosynthesis-a process utilizing biological systems to synthesize chemical compounds-has emerged as a revolutionary solution to 21st-century challenges due to its environmental sustainability, scalability, and high stereoselectivity and regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, and optimization of enzymatic reactions and biological systems. We first introduce the molecular retrosynthesis route planning in biochemical pathway design, including single-step retrosynthesis algorithms and AI-based chemical retrosynthesis route design tools. We highlight the advantages and challenges of large language models in addressing the sparsity of chemical data. Furthermore, we review enzyme discovery methods based on sequence and structure alignment techniques. Breakthroughs in AI-based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. We also summarize methods for de novo enzyme generation for nonnatural or orphan reactions, focusing on AI-based enzyme functional annotation and enzyme discovery techniques based on reaction or small molecule similarity. Turning to enzyme engineering, we discuss strategies to improve enzyme thermostability, solubility, and activity, as well as the applications of AI in these fields. The shift from traditional experiment-driven models to data-driven and computationally driven intelligent models is already underway. Finally, we present potential challenges and provide a perspective on future research directions. We envision expanded applications of biocatalysis in drug development, green chemistry, and complex molecule synthesis.
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Affiliation(s)
- Ancheng Chen
- Shanghai Zelixir Biotech Company Ltd.ShanghaiChina
| | - Xiangda Peng
- Shanghai Zelixir Biotech Company Ltd.ShanghaiChina
| | - Tao Shen
- Shanghai Zelixir Biotech Company Ltd.ShanghaiChina
| | | | - Dong Wu
- Shanghai Zelixir Biotech Company Ltd.ShanghaiChina
| | - Sheng Wang
- Shanghai Zelixir Biotech Company Ltd.ShanghaiChina
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3
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Basnet BB, Zhou ZY, Wei B, Wang H. Advances in AI-based strategies and tools to facilitate natural product and drug development. Crit Rev Biotechnol 2025:1-32. [PMID: 40159111 DOI: 10.1080/07388551.2025.2478094] [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: 10/20/2024] [Revised: 02/11/2025] [Accepted: 02/16/2025] [Indexed: 04/02/2025]
Abstract
Natural products and their derivatives have been important for treating diseases in humans, animals, and plants. However, discovering new structures from natural sources is still challenging. In recent years, artificial intelligence (AI) has greatly aided the discovery and development of natural products and drugs. AI facilitates to: connect genetic data to chemical structures or vice-versa, repurpose known natural products, predict metabolic pathways, and design and optimize metabolites biosynthesis. More recently, the emergence and improvement in neural networks such as deep learning and ensemble automated web based bioinformatics platforms have sped up the discovery process. Meanwhile, AI also improves the identification and structure elucidation of unknown compounds from raw data like mass spectrometry and nuclear magnetic resonance. This article reviews these AI-driven methods and tools, highlighting their practical applications and guide for efficient natural product discovery and drug development.
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Affiliation(s)
- Buddha Bahadur Basnet
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, China
- Central Department of Biotechnology, Tribhuvan University, Kathmandu, Nepal
| | - Zhen-Yi Zhou
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, China
| | - Bin Wei
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, China
| | - Hong Wang
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Marine Fishery Resources Exploitment, Utilization of Zhejiang Province, Zhejiang University of Technology, Hangzhou, China
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4
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De Florio M, Zou Z, Schiavazzi DE, Karniadakis GE. Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2025; 383:20240221. [PMID: 40078150 DOI: 10.1098/rsta.2024.0221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 10/23/2024] [Accepted: 11/11/2024] [Indexed: 03/14/2025]
Abstract
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty due to noise in the data, epistemic uncertainty induced by insufficient data or inadequate parameterization and model-form uncertainty related to the use of misspecified model equations. In addition, recently proposed approaches provide flexible ways to combine information from data with full or partial satisfaction of equations that typically encode physical principles. Physics-based regularization interacts in non-trivial ways with aleatoric, epistemic and model-form uncertainty and their combination, and a better understanding of this interaction is needed to improve the predictive performance of physics-informed digital twins that operate under real conditions. To better understand this interaction, with a specific focus on biological and physiological models, this study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC, a new physics-informed approach for uncertainty quantification based on random projections and Monte Carlo sampling. After an introductory comparison between approaches for physics-informed estimation, MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology. The system is first analysed by progressively removing data while estimating an increasing number of parameters, and subsequently by investigating total uncertainty under model-form misspecification of nonlinear resistance in the pulmonary compartment. In particular, we focus on the interaction between the formulation of the discrepancy term and quantification of model-form uncertainty, and show how additional physics can help in the estimation process. The method demonstrates robustness and efficiency in estimating unknown states and parameters, even with limited, sparse and noisy data. It also offers great flexibility in integrating data with physics for improved estimation, even in cases of model misspecification.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.
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Affiliation(s)
- Mario De Florio
- Division of Applied Mathematics, Brown University, Providence, RI 02906, USA
| | - Zongren Zou
- Division of Applied Mathematics, Brown University, Providence, RI 02906, USA
| | - Daniele E Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA
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5
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Yook S, Alper HS. Recent advances in genetic engineering and chemical production in yeast species. FEMS Yeast Res 2025; 25:foaf009. [PMID: 40082732 PMCID: PMC11963765 DOI: 10.1093/femsyr/foaf009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2025] [Revised: 03/07/2025] [Accepted: 03/11/2025] [Indexed: 03/16/2025] Open
Abstract
Yeasts have emerged as well-suited microbial cell factory for the sustainable production of biofuels, organic acids, terpenoids, and specialty chemicals. This ability is bolstered by advances in genetic engineering tools, including CRISPR-Cas systems and modular cloning in both conventional (Saccharomyces cerevisiae) and non-conventional (Yarrowia lipolytica, Rhodotorula toruloides, Candida krusei) yeasts. Additionally, genome-scale metabolic models and machine learning approaches have accelerated efforts to create a broad range of compounds that help reduce dependency on fossil fuels, mitigate climate change, and offer sustainable alternatives to petrochemical-derived counterparts. In this review, we highlight the cutting-edge genetic tools driving yeast metabolic engineering and then explore the diverse applications of yeast-based platforms for producing value-added products. Collectively, this review underscores the pivotal role of yeast biotechnology in efforts to build a sustainable bioeconomy.
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Affiliation(s)
- Sangdo Yook
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, United States
| | - Hal S Alper
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, United States
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, 78712, United States
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6
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Scott H, Segrè D. Metabolic Flux Modeling in Marine Ecosystems. ANNUAL REVIEW OF MARINE SCIENCE 2025; 17:593-620. [PMID: 39259978 DOI: 10.1146/annurev-marine-032123-033718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Ocean metabolism constitutes a complex, multiscale ensemble of biochemical reaction networks harbored within and between the boundaries of a myriad of organisms. Gaining a quantitative understanding of how these networks operate requires mathematical tools capable of solving in silico the resource allocation problem each cell faces in real life. Toward this goal, stoichiometric modeling of metabolism, such as flux balance analysis, has emerged as a powerful computational tool for unraveling the intricacies of metabolic processes in microbes, microbial communities, and multicellular organisms. Here, we provide an overview of this approach and its applications, future prospects, and practical considerations in the context of marine sciences. We explore how flux balance analysis has been employed to study marine organisms, help elucidate nutrient cycling, and predict metabolic capabilities within diverse marine environments, and highlight future prospects for this field in advancing our knowledge of marine ecosystems and their sustainability.
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Affiliation(s)
- Helen Scott
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, Massachusetts, USA; ,
| | - Daniel Segrè
- Department of Biology, Department of Physics, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Faculty of Computing and Data Science, Boston University, Boston, Massachusetts, USA; ,
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7
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Guo F, Liu K, Qiao Y, Zheng Y, Liu C, Wu Y, Zhang Z, Jiang W, Jiang Y, Xin F, Jiang M, Zhang W. Evolutionary engineering of Saccharomyces cerevisiae: Crafting a synthetic methylotroph via self-reprogramming. SCIENCE ADVANCES 2024; 10:eadq3484. [PMID: 39705340 DOI: 10.1126/sciadv.adq3484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 11/18/2024] [Indexed: 12/22/2024]
Abstract
Methanol, as a non-edible feedstock, offers a promising sustainable alternative to sugar-based substrates in biochemical production. Despite progress in engineering methanol assimilation in nonmethylotrophs, the full transformation into methanol-dependent synthetic methylotrophs remains a formidable challenge. Here, moving beyond the conventional rational design principle, we engineered a synthetic methylotrophic Saccharomyces cerevisiae through genome rearrangement and adaptive laboratory evolution. This evolutionarily advanced strain unexpectedly shed the heterologous methanol assimilation pathway and demonstrated the robust growth on sole methanol. We discovered that the evolved strain likely realized methanol assimilation through a previously unidentified Adh2-Sfa1-rGly (ASrG) pathway, facilitating the concurrent assimilation of formate and CO2. Furthermore, the incorporation of electron transfer material C3N4 quantum dots obviously enhanced methanol-dependent growth, emphasizing the role of energy availability in the ASrG pathway. This breakthrough introduces a previously unidentified C1 utilization pathway and highlights the exceptional adaptability and self-evolving capacity of the S. cerevisiae metabolic network.
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Affiliation(s)
- Feng Guo
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - Kang Liu
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - Yangyi Qiao
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - YongMin Zheng
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - Chenguang Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200241, China
| | - Yi Wu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Zhonghai Zhang
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200240, China
| | - Wankui Jiang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - Yujia Jiang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - Fengxue Xin
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - Min Jiang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
| | - Wenming Zhang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211800, China
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8
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Hosoda S, Iwata H, Miura T, Tanabe M, Okada T, Mochizuki A, Sato M. BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks. BMC Bioinformatics 2024; 25:297. [PMID: 39256657 PMCID: PMC11389226 DOI: 10.1186/s12859-024-05921-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 09/04/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. RESULTS We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. CONCLUSIONS We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.
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Affiliation(s)
- Shion Hosoda
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan.
| | - Hisashi Iwata
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
| | - Takuya Miura
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
| | - Maiko Tanabe
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
| | - Takashi Okada
- Laboratory of Mathematical Biology, Institute for Life and Medical Sciences, Kyoto University, Kyoto-shi, Kyoto, 606-8507, Japan
| | - Atsushi Mochizuki
- Laboratory of Mathematical Biology, Institute for Life and Medical Sciences, Kyoto University, Kyoto-shi, Kyoto, 606-8507, Japan
| | - Miwa Sato
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan
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9
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Huang Y, Ma T, Wan Z, Zhong C, Wang J. AFP: Finding pathways accounting for stoichiometry along with atom group tracking in metabolic network. J Biotechnol 2024; 392:139-151. [PMID: 39009230 DOI: 10.1016/j.jbiotec.2024.07.004] [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: 08/15/2023] [Revised: 04/29/2024] [Accepted: 07/08/2024] [Indexed: 07/17/2024]
Abstract
Automatically finding novel pathways plays an important role in the initial designs of metabolic pathways in synthetic biology and metabolic engineering. Although path-finding methods have been successfully applied in identifying valuable synthetic pathways, few efforts have been made in fusing atom group tracking into building stoichiometry model to search metabolic pathways from arbitrary start compound via Mixed Integer Linear Programming (MILP). We propose a novel method called AFP to find metabolic pathways by incorporating atom group tracking into reaction stoichiometry via MILP. AFP tracks the movements of atom groups in the reaction stoichiometry to construct MILP model to search the pathways containing atom groups exchange in the reactions and adapts the MILP model to provide the options of searching pathways from an arbitrary or given compound to the target compound. Combining atom group tracking with reaction stoichiometry to build MILP model for pathfinding may promote the search of well-designed alternative pathways at the stoichiometric modeling level. The experimental comparisons to the known pathways show that our proposed method AFP is more effective to recover the known pathways than other existing methods and is capable of discovering biochemically feasible pathways producing the metabolites of interest.
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Affiliation(s)
- Yiran Huang
- School of Computer and Electronics and Information, Guangxi University, Nanning, China; Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China; Key Laboratory of Parallel, Distributed and Intelligent Computing, (Guangxi University), Education Department of Guangxi Zhuang Autonomous Region, Guangxi University, Nanning, China; Guangxi Intelligent Digital Services Research Center of Engineering Technology, Guangxi University, Nanning, China.
| | - Tao Ma
- School of Computer and Electronics and Information, Guangxi University, Nanning, China
| | - Zhiyuan Wan
- School of Computer and Electronics and Information, Guangxi University, Nanning, China
| | - Cheng Zhong
- School of Computer and Electronics and Information, Guangxi University, Nanning, China; Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China; Key Laboratory of Parallel, Distributed and Intelligent Computing, (Guangxi University), Education Department of Guangxi Zhuang Autonomous Region, Guangxi University, Nanning, China; Guangxi Intelligent Digital Services Research Center of Engineering Technology, Guangxi University, Nanning, China
| | - Jianyi Wang
- Medical College, Guangxi University, Nanning, China
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10
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De Florio M, Zou Z, Schiavazzi DE, Karniadakis GE. Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology. ARXIV 2024:arXiv:2408.07201v1. [PMID: 39184536 PMCID: PMC11343238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model is accurate. Possible sources include irreducible aleatoric uncertainty due to noise in the data, epistemic uncertainty induced by insufficient data or inadequate parameterization, and model-form uncertainty related to the use of misspecified model equations. In addition, recently proposed approaches provide flexible ways to combine information from data with full or partial satisfaction of equations that typically encode physical principles. Physics-based regularization interacts in nontrivial ways with aleatoric, epistemic and model-form uncertainty and their combination, and a better understanding of this interaction is needed to improve the predictive performance of physics-informed digital twins that operate under real conditions. To better understand this interaction, with a specific focus on biological and physiological models, this study investigates the decomposition of total uncertainty in the estimation of states and parameters of a differential system simulated with MC X-TFC, a new physics-informed approach for uncertainty quantification based on random projections and Monte-Carlo sampling. After an introductory comparison between approaches for physics-informed estimation, MC X-TFC is applied to a six-compartment stiff ODE system, the CVSim-6 model, developed in the context of human physiology. The system is first analyzed by progressively removing data while estimating an increasing number of parameters, and subsequently by investigating total uncertainty under model-form misspecification of non-linear resistance in the pulmonary compartment. In particular, we focus on the interaction between the formulation of the discrepancy term and quantification of model-form uncertainty, and show how additional physics can help in the estimation process. The method demonstrates robustness and efficiency in estimating unknown states and parameters, even with limited, sparse, and noisy data. It also offers great flexibility in integrating data with physics for improved estimation, even in cases of model misspecification.
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Affiliation(s)
- Mario De Florio
- Division of Applied Mathematics, Brown University, Providence, 02906 RI, USA
| | - Zongren Zou
- Division of Applied Mathematics, Brown University, Providence, 02906 RI, USA
| | - Daniele E. Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556 IN, USA
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11
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Ferreira S, Balola A, Sveshnikova A, Hatzimanikatis V, Vilaça P, Maia P, Carreira R, Stoney R, Carbonell P, Souza CS, Correia J, Lousa D, Soares CM, Rocha I. Computer-aided design and implementation of efficient biosynthetic pathways to produce high added-value products derived from tyrosine in Escherichia coli. Front Bioeng Biotechnol 2024; 12:1360740. [PMID: 38978715 PMCID: PMC11228882 DOI: 10.3389/fbioe.2024.1360740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/03/2024] [Indexed: 07/10/2024] Open
Abstract
Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthesis of commercially attractive molecules often involves promiscuous enzymes or lacks well-established routes. To address these challenges, we developed a computational workflow integrating enumeration/retrosynthesis algorithms, a toolbox for pathway analysis, enzyme selection tools, and a gene discovery pipeline, supported by manual curation and literature review. Our focus has been on implementing biosynthetic pathways for tyrosine-derived compounds, specifically L-3,4-dihydroxyphenylalanine (L-DOPA) and dopamine, with significant applications in health and nutrition. We selected one pathway to produce L-DOPA and two different pathways for dopamine-one already described in the literature and a novel pathway. Our goal was either to identify the most suitable gene candidates for expression in Escherichia coli for the known pathways or to discover innovative pathways. Although not all implemented pathways resulted in the accumulation of target compounds, in our shake-flask experiments we achieved a maximum L-DOPA titer of 0.71 g/L and dopamine titers of 0.29 and 0.21 g/L for known and novel pathways, respectively. In the case of L-DOPA, we utilized, for the first time, a mutant version of tyrosinase from Ralstonia solanacearum. Production of dopamine via the known biosynthesis route was accomplished by coupling the L-DOPA pathway with the expression of DOPA decarboxylase from Pseudomonas putida, resulting in a unique biosynthetic pathway never reported in literature before. In the context of the novel pathway, dopamine was produced using tyramine as the intermediate compound. To achieve this, tyrosine was initially converted into tyramine by expressing TDC from Levilactobacillus brevis, which, in turn, was converted into dopamine through the action of the enzyme encoded by ppoMP from Mucuna pruriens. This marks the first time that an alternative biosynthetic pathway for dopamine has been validated in microbes. These findings underscore the effectiveness of our computational workflow in facilitating pathway enumeration and selection, offering the potential to uncover novel biosynthetic routes, thus paving the way for other target compounds of biotechnological interest.
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Affiliation(s)
- Sofia Ferreira
- Systems and Synthetic Biology Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Alexandra Balola
- Systems and Synthetic Biology Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Anastasia Sveshnikova
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFL, Lausanne, Switzerland
| | - Paulo Vilaça
- SilicoLife-Computational Biology Solutions for the Life Sciences, Braga, Portugal
| | - Paulo Maia
- SilicoLife-Computational Biology Solutions for the Life Sciences, Braga, Portugal
| | - Rafael Carreira
- SilicoLife-Computational Biology Solutions for the Life Sciences, Braga, Portugal
| | - Ruth Stoney
- Manchester Institute of Biotechnology, School of Chemistry, Faculty of Science and Engineering, University of Manchester, Manchester, United Kingdom
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
- Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC: Consejo Superior de Investigaciones Científicas, Paterna, Spain
| | - Caio Silva Souza
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - João Correia
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Diana Lousa
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Cláudio M Soares
- Protein Modelling Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
| | - Isabel Rocha
- Systems and Synthetic Biology Laboratory, ITQB Nova-Instituto de Tecnologia Química e Biológica António Xavier, Oeiras, Portugal
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12
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Siddharth T, Lewis NE. Predicting pathways for old and new metabolites through clustering. J Theor Biol 2024; 578:111684. [PMID: 38048983 PMCID: PMC11139542 DOI: 10.1016/j.jtbi.2023.111684] [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: 06/08/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 12/06/2023]
Abstract
The diverse metabolic pathways are fundamental to all living organisms, as they harvest energy, synthesize biomass components, produce molecules to interact with the microenvironment, and neutralize toxins. While the discovery of new metabolites and pathways continues, the prediction of pathways for new metabolites can be challenging. It can take vast amounts of time to elucidate pathways for new metabolites; thus, according to HMDB (Human Metabolome Database), only 60% of metabolites get assigned to pathways. Here, we present an approach to identify pathways based on metabolite structure. We extracted 201 features from SMILES annotations and identified new metabolites from PubMed abstracts and HMDB. After applying clustering algorithms to both groups of features, we quantified correlations between metabolites, and found the clusters accurately linked 92% of known metabolites to their respective pathways. Thus, this approach could be valuable for predicting metabolic pathways for new metabolites.
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Affiliation(s)
- Thiru Siddharth
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Bhopal, MP 462003, India
| | - Nathan E Lewis
- Department of Pediatrics and Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.
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13
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Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [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: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
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Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China; (W.M.); (H.P.); (Y.S.); (X.Z.); (A.X.)
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China; (W.M.); (H.P.); (Y.S.); (X.Z.); (A.X.)
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China; (W.M.); (H.P.); (Y.S.); (X.Z.); (A.X.)
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China; (W.M.); (H.P.); (Y.S.); (X.Z.); (A.X.)
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China; (W.M.); (H.P.); (Y.S.); (X.Z.); (A.X.)
| | | | - Srinivasa R. Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA;
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China; (W.M.); (H.P.); (Y.S.); (X.Z.); (A.X.)
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14
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Kumari P, Deepa N, Trivedi PK, Singh BK, Srivastava V, Singh A. Plants and endophytes interaction: a "secret wedlock" for sustainable biosynthesis of pharmaceutically important secondary metabolites. Microb Cell Fact 2023; 22:226. [PMID: 37925404 PMCID: PMC10625306 DOI: 10.1186/s12934-023-02234-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 10/19/2023] [Indexed: 11/06/2023] Open
Abstract
Many plants possess immense pharmacological properties because of the presence of various therapeutic bioactive secondary metabolites that are of great importance in many pharmaceutical industries. Therefore, to strike a balance between meeting industry demands and conserving natural habitats, medicinal plants are being cultivated on a large scale. However, to enhance the yield and simultaneously manage the various pest infestations, agrochemicals are being routinely used that have a detrimental impact on the whole ecosystem, ranging from biodiversity loss to water pollution, soil degradation, nutrient imbalance and enormous health hazards to both consumers and agricultural workers. To address the challenges, biological eco-friendly alternatives are being looked upon with high hopes where endophytes pitch in as key players due to their tight association with the host plants. The intricate interplay between plants and endophytic microorganisms has emerged as a captivating subject of scientific investigation, with profound implications for the sustainable biosynthesis of pharmaceutically important secondary metabolites. This review delves into the hidden world of the "secret wedlock" between plants and endophytes, elucidating their multifaceted interactions that underpin the synthesis of bioactive compounds with medicinal significance in their plant hosts. Here, we briefly review endophytic diversity association with medicinal plants and highlight the potential role of core endomicrobiome. We also propose that successful implementation of in situ microbiome manipulation through high-end techniques can pave the way towards a more sustainable and pharmaceutically enriched future.
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Affiliation(s)
- Poonam Kumari
- Division of Crop Production and Protection, Central Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India
| | - Nikky Deepa
- Division of Crop Production and Protection, Central Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Prabodh Kumar Trivedi
- Division of Plant Biotechnology, Central Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Brajesh K Singh
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, 2753, Australia
- Global Centre for Land-Based Innovation, Western Sydney University, Penrith, NSW, 2751, Australia
| | - Vaibhav Srivastava
- Division of Glycoscience, Department of Chemistry, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, AlbaNova University Center, 106 91, Stockholm, Sweden.
| | - Akanksha Singh
- Division of Crop Production and Protection, Central Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India.
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15
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Qiu S, Yang A, Zeng H. Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook. PLoS Comput Biol 2023; 19:e1011391. [PMID: 37619239 PMCID: PMC10449171 DOI: 10.1371/journal.pcbi.1011391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023] Open
Abstract
In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.
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Affiliation(s)
- Sizhe Qiu
- School of Food and Health, Beijing Technology and Business University, Bejing, China
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Aidong Yang
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Hong Zeng
- School of Food and Health, Beijing Technology and Business University, Bejing, China
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16
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Upadhyay V, Boorla VS, Maranas CD. Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network. Metab Eng 2023; 78:171-182. [PMID: 37301359 DOI: 10.1016/j.ymben.2023.06.001] [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: 12/15/2022] [Revised: 05/19/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023]
Abstract
Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent retro-biosynthetic algorithms increasingly use novel conversions that require altering the substrate or cofactor specificities of existing enzymes while connecting pathways leading to a target metabolite. However, identifying and re-engineering enzymes for desired novel conversions are currently the bottlenecks in implementing such designed pathways. Herein, we present EnzRank, a convolutional neural network (CNN) based approach, to rank-order existing enzymes in terms of their suitability to undergo successful protein engineering through directed evolution or de novo design towards a desired specific substrate activity. We train the CNN model on 11,800 known active enzyme-substrate pairs from the BRENDA database as positive samples and data generated by scrambling these pairs as negative samples using substrate dissimilarity between an enzyme's native substrate and all other molecules present in the dataset using Tanimoto similarity score. EnzRank achieves an average recovery rate of 80.72% and 73.08% for positive and negative pairs on test data after using a 10-fold holdout method for training and cross-validation. We further developed a web-based user interface (available at https://huggingface.co/spaces/vuu10/EnzRank) to predict enzyme-substrate activity using SMILES strings of substrates and enzyme sequence as input to allow convenient and easy-to-use access to EnzRank. In summary, this effort can aid de novo pathway design tools to prioritize starting enzyme re-engineering candidates for novel reactions as well as in predicting the potential secondary activity of enzymes in cell metabolism.
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Affiliation(s)
- Vikas Upadhyay
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
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17
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Gurdo N, Volke DC, McCloskey D, Nikel PI. Automating the design-build-test-learn cycle towards next-generation bacterial cell factories. N Biotechnol 2023; 74:1-15. [PMID: 36736693 DOI: 10.1016/j.nbt.2023.01.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/15/2023] [Accepted: 01/22/2023] [Indexed: 02/04/2023]
Abstract
Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.
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Affiliation(s)
- Nicolás Gurdo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Daniel C Volke
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Douglas McCloskey
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Pablo Iván Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark.
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18
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Tan Z, Li J, Hou J, Gonzalez R. Designing artificial pathways for improving chemical production. Biotechnol Adv 2023; 64:108119. [PMID: 36764336 DOI: 10.1016/j.biotechadv.2023.108119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023]
Abstract
Metabolic engineering exploits manipulation of catalytic and regulatory elements to improve a specific function of the host cell, often the synthesis of interesting chemicals. Although naturally occurring pathways are significant resources for metabolic engineering, these pathways are frequently inefficient and suffer from a series of inherent drawbacks. Designing artificial pathways in a rational manner provides a promising alternative for chemicals production. However, the entry barrier of designing artificial pathway is relatively high, which requires researchers a comprehensive and deep understanding of physical, chemical and biological principles. On the other hand, the designed artificial pathways frequently suffer from low efficiencies, which impair their further applications in host cells. Here, we illustrate the concept and basic workflow of retrobiosynthesis in designing artificial pathways, as well as the most currently used methods including the knowledge- and computer-based approaches. Then, we discuss how to obtain desired enzymes for novel biochemistries, and how to trim the initially designed artificial pathways for further improving their functionalities. Finally, we summarize the current applications of artificial pathways from feedstocks utilization to various products synthesis, as well as our future perspectives on designing artificial pathways.
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Affiliation(s)
- Zaigao Tan
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Department of Bioengineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jian Li
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, China; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Department of Bioengineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jin Hou
- State Key Laboratory of Microbial Technology, Shandong University, Qingdao, China
| | - Ramon Gonzalez
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA.
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19
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Petroleum Hydrocarbon Catabolic Pathways as Targets for Metabolic Engineering Strategies for Enhanced Bioremediation of Crude-Oil-Contaminated Environments. FERMENTATION-BASEL 2023. [DOI: 10.3390/fermentation9020196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Anthropogenic activities and industrial effluents are the major sources of petroleum hydrocarbon contamination in different environments. Microbe-based remediation techniques are known to be effective, inexpensive, and environmentally safe. In this review, the metabolic-target-specific pathway engineering processes used for improving the bioremediation of hydrocarbon-contaminated environments have been described. The microbiomes are characterised using environmental genomics approaches that can provide a means to determine the unique structural, functional, and metabolic pathways used by the microbial community for the degradation of contaminants. The bacterial metabolism of aromatic hydrocarbons has been explained via peripheral pathways by the catabolic actions of enzymes, such as dehydrogenases, hydrolases, oxygenases, and isomerases. We proposed that by using microbiome engineering techniques, specific pathways in an environment can be detected and manipulated as targets. Using the combination of metabolic engineering with synthetic biology, systemic biology, and evolutionary engineering approaches, highly efficient microbial strains may be utilised to facilitate the target-dependent bioprocessing and degradation of petroleum hydrocarbons. Moreover, the use of CRISPR-cas and genetic engineering methods for editing metabolic genes and modifying degradation pathways leads to the selection of recombinants that have improved degradation abilities. The idea of growing metabolically engineered microbial communities, which play a crucial role in breaking down a range of pollutants, has also been explained. However, the limitations of the in-situ implementation of genetically modified organisms pose a challenge that needs to be addressed in future research.
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20
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Yu T, Boob AG, Volk MJ, Liu X, Cui H, Zhao H. Machine learning-enabled retrobiosynthesis of molecules. Nat Catal 2023. [DOI: 10.1038/s41929-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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21
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Qin Y, Li Q, Fan L, Ning X, Wei X, You C. Biomanufacturing by In Vitro Biotransformation (ivBT) Using Purified Cascade Multi-enzymes. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2023; 186:1-27. [PMID: 37455283 DOI: 10.1007/10_2023_231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development.
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Affiliation(s)
- Yanmei Qin
- University of Chinese Academy of Sciences, Beijing, China
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Qiangzi Li
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Lin Fan
- University of Chinese Academy of Sciences, Beijing, China
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- University of Chinese Academy of Sciences Sino-Danish College, Beijing, China
| | - Xiao Ning
- University of Chinese Academy of Sciences, Beijing, China
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Xinlei Wei
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
- National Technology Innovation Center of Synthetic Biology, Tianjin, China.
| | - Chun You
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
- National Technology Innovation Center of Synthetic Biology, Tianjin, China.
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22
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Shrivastava A, Pal M, Sharma RK. Pichia as Yeast Cell Factory for Production of Industrially Important Bio-Products: Current Trends, Challenges, and Future Prospects. JOURNAL OF BIORESOURCES AND BIOPRODUCTS 2023. [DOI: 10.1016/j.jobab.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
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23
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Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
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Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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24
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From degrader to producer: reversing the gallic acid metabolism of Pseudomonas putida KT2440. Int Microbiol 2022; 26:243-255. [PMID: 36357545 PMCID: PMC9649394 DOI: 10.1007/s10123-022-00282-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 08/18/2022] [Accepted: 10/06/2022] [Indexed: 11/12/2022]
Abstract
Gallic acid is a powerful antioxidant with multiple therapeutic applications, usually obtained from the acidic hydrolysis of tannins produced by many plants. As this process generates a considerable amount of toxic waste, the use of tannases or tannase-producing microorganisms has become a greener alternative over the last years. However, their high costs still impose some barriers for industrial scalability, requiring solutions that could be both greener and cost-effective. Since Pseudomonas putida KT2440 is a powerful degrader of gallic acid, its metabolism offers pathways that can be engineered to produce it from cheap and renewable carbon sources, such as the crude glycerol generated in biodiesel units. In this study, a synthetic operon with the heterologous genes aroG4, quiC and pobA* was developed and expressed in P. putida, based on an in silico analysis of possible metabolic routes, resulting in no production. Then, the sequences pcaHG and galTAPR were deleted from the genome of this strain to avoid the degradation of gallic acid and its main intermediate, the protocatechuic acid. This mutant was transformed with the vector containing the synthetic operon and was finally able to convert glycerol into gallic acid. Production assays in shaker showed a final concentration of 346.7 ± 0.004 mg L-1 gallic acid after 72 h.
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25
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Xu Z, Mahadevan R. Efficient Enumeration of Branched Novel Biochemical Pathways Using a Probabilistic Technique. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c02211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhiqing Xu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
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26
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Kellman BP, Richelle A, Yang JY, Chapla D, Chiang AWT, Najera JA, Liang C, Fürst A, Bao B, Koga N, Mohammad MA, Bruntse AB, Haymond MW, Moremen KW, Bode L, Lewis NE. Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration. Nat Commun 2022; 13:2455. [PMID: 35508452 PMCID: PMC9068700 DOI: 10.1038/s41467-022-29867-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 04/04/2022] [Indexed: 12/18/2022] Open
Abstract
Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jeong-Yeh Yang
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Digantkumar Chapla
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Julia A Najera
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Chenguang Liang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Annalee Fürst
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Natalia Koga
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Mahmoud A Mohammad
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Anders Bech Bruntse
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Morey W Haymond
- Department of Pediatrics, Children's Nutrition Research Center, US Department of Agriculture/Agricultural Research Service, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kelley W Moremen
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Lars Bode
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Larsson-Rosenquist Foundation Mother-Milk-Infant Center of Research Excellence (MOMI CORE), University of California, San Diego, La Jolla, CA, 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
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27
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Kovács SC, Szappanos B, Tengölics R, Notebaart RA, Papp B. Underground metabolism as a rich reservoir for pathway engineering. Bioinformatics 2022; 38:3070-3077. [PMID: 35441658 PMCID: PMC9154287 DOI: 10.1093/bioinformatics/btac282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/12/2022] [Accepted: 04/14/2022] [Indexed: 11/25/2022] Open
Abstract
Motivation Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level ‘underground’ enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering. Results Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development. Availability and implementation The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Szabolcs Cselgő Kovács
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
| | - Balázs Szappanos
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary.,Department of Biotechnology, University of Szeged, Szeged, Hungary
| | - Roland Tengölics
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
| | - Richard A Notebaart
- Food Microbiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Balázs Papp
- HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary.,Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary
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28
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PyMiner: A method for metabolic pathway design based on the uniform similarity of substrate-product pairs and conditional search. PLoS One 2022; 17:e0266783. [PMID: 35404943 PMCID: PMC9000129 DOI: 10.1371/journal.pone.0266783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 03/26/2022] [Indexed: 11/30/2022] Open
Abstract
Metabolic pathway design is an essential step in the course of constructing an efficient microbial cell factory to produce high value-added chemicals. Meanwhile, the computational design of biologically meaningful metabolic pathways has been attracting much attention to produce natural and non-natural products. However, there has been a lack of effective methods to perform metabolic network reduction automatically. In addition, comprehensive evaluation indexes for metabolic pathway are still relatively scarce. Here, we define a novel uniform similarity to calculate the main substrate-product pairs of known biochemical reactions, and develop further an efficient metabolic pathway design tool named PyMiner. As a result, the redundant information of general metabolic network (GMN) is eliminated, and the number of substrate-product pairs is shown to decrease by 81.62% on average. Considering that the nodes in the extracted metabolic network (EMN) constructed in this work is large in scale but imbalanced in distribution, we establish a conditional search strategy (CSS) that cuts search time in 90.6% cases. Compared with state-of-the-art methods, PyMiner shows obvious advantages and demonstrates equivalent or better performance on 95% cases of experimentally verified pathways. Consequently, PyMiner is a practical and effective tool for metabolic pathway design.
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29
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Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx. Nat Commun 2022; 13:1560. [PMID: 35322036 PMCID: PMC8943196 DOI: 10.1038/s41467-022-29238-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/07/2022] [Indexed: 12/23/2022] Open
Abstract
Metabolic “dark matter” describes currently unknown metabolic processes, which form a blind spot in our general understanding of metabolism and slow down the development of biosynthetic cell factories and naturally derived pharmaceuticals. Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 489 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of biochemical pathways and evaluates the biochemical vicinity of molecule classes (https://lcsb-databases.epfl.ch/Atlas2). “Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. Here the authors present ATLASx, a repository of known and predicted enzymatic reaction, connecting millions of compounds to help synthetic biologists and metabolic engineers to design and explore metabolic pathways.”
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30
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Khana DB, Callaghan MM, Amador-Noguez D. Novel computational and experimental approaches for investigating the thermodynamics of metabolic networks. Curr Opin Microbiol 2021; 66:21-31. [PMID: 34974376 DOI: 10.1016/j.mib.2021.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 11/18/2022]
Abstract
Thermodynamic analysis of metabolic networks has emerged as a useful new tool for pathway design and metabolic engineering. Understanding the relationship between the thermodynamic driving force of biochemical reactions and metabolic flux has generated new insights regarding the design principles of microbial carbon metabolism. This review summarizes the various lessons that can be obtained from the thermodynamic analysis of metabolic pathways, illustrates concepts of computational thermodynamic tools, and highlights recent applications of thermodynamic analysis to pathway design in industrially relevant microbes.
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Affiliation(s)
- Daven B Khana
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA; Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA; Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Melanie M Callaghan
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Daniel Amador-Noguez
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA; Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
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31
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Sleight TW, Sexton CN, Mpourmpakis G, Gilbertson LM, Ng CA. A Classification Model to Identify Direct-Acting Mutagenic Polycyclic Aromatic Hydrocarbon Transformation Products. Chem Res Toxicol 2021; 34:2273-2286. [PMID: 34662518 DOI: 10.1021/acs.chemrestox.1c00187] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are a complex group of environmental contaminants, many having long environmental half-lives. As these compounds degrade, the changes in their structure can result in a substantial increase in mutagenicity compared to the parent compound. Over time, each individual PAH can potentially degrade into several thousand unique transformation products, creating a complex, constantly evolving set of intermediates. Microbial degradation is the primary mechanism of their transformation and ultimate removal from the environment, and this process can result in mutagenic activation similar to the metabolic activation that can occur in multicellular organisms. The diversity of the potential intermediate structures in PAH-contaminated environments renders hazard assessment difficult for both remediation professionals and regulators. A mixture of structural and energetic descriptors has proven effective in existing studies for classifying which PAH transformation products will be mutagenic. However, most existing studies of environmental PAH mutagens primarily focus on nitrogenated derivatives, which are prevalent in the atmosphere and not as relevant in soil. Additionally, PAH products commonly found in the environment can range from as large as five rings to as small as a single ring, requiring a broadly inclusive methodology to comprehensively evaluate mutagenic potential. We developed a combination of supervised and unsupervised machine learning methods to predict environmentally induced PAH mutagenicity with improved performance over currently available tools. K-means clustering with principal component analysis allows us to identify molecular clusters that we hypothesize to have similar mechanisms of action. Recursive feature elimination identifies the most influential descriptors. The cluster-specific regression outperforms available classifiers in predicting direct-acting mutagens resulting from the microbial biodegradation of PAHs and provides direction for future studies evaluating the environmental hazards resulting from PAH biodegradation.
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Affiliation(s)
- Trevor W Sleight
- Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Caitlin N Sexton
- Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Giannis Mpourmpakis
- Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Leanne M Gilbertson
- Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.,Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Carla A Ng
- Civil & Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.,Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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32
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Wang L, Upadhyay V, Maranas CD. dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design. PLoS Comput Biol 2021; 17:e1009448. [PMID: 34570771 PMCID: PMC8496854 DOI: 10.1371/journal.pcbi.1009448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/07/2021] [Accepted: 09/13/2021] [Indexed: 11/19/2022] Open
Abstract
Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs energy change (ΔrG′o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has comparable prediction accuracy compared to existing GC methods and can capture Gibbs energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic for safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor). The standard Gibbs energy change is commonly used to check for the feasibility of enzyme-catalyzed reactions as thermodynamics plays a crucial role in pathway design for biochemical synthesis. The group contribution methods using expert-defined functional groups have been extensively used for estimating standard Gibbs energy change. Here, we introduce a molecular fingerprint-based thermodynamic tool, dGPredictor, that enables distinguishing between (stereo)isomers in metabolic reactions leading to improved reaction coverage and comparable prediction accuracy as GC methods. dGPredictor can also be used alongside de novo pathway design tools to ensure the correct directionality of chosen reaction steps. We applied and tested dGPredictor on reactions from the KEGG database and applied it to screen an isobutanol synthesis pathway design. An open-source, user-friendly web interface is provided to facilitate easy access for standard Gibbs energy change of reactions at different pH values. (https://github.com/maranasgroup/dGPredictor).
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Vikas Upadhyay
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States America
- * E-mail:
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33
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Nakazawa S, Imaichi O, Kogure T, Kubota T, Toyoda K, Suda M, Inui M, Ito K, Shirai T, Araki M. History-Driven Genetic Modification Design Technique Using a Domain-Specific Lexical Model for the Acceleration of DBTL Cycles for Microbial Cell Factories. ACS Synth Biol 2021; 10:2308-2317. [PMID: 34351735 DOI: 10.1021/acssynbio.1c00234] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The development of microbes for conducting bioprocessing via synthetic biology involves design-build-test-learn (DBTL) cycles. To aid the designing step, we developed a computational technique that suggests next genetic modifications on the basis of relatedness to the user's design history of genetic modifications accumulated through former DBTL cycles conducted by the user. This technique, which comprehensively retrieves well-known designs related to the history, involves searching text for previous literature and then mining genes that frequently co-occur in the literature with those modified genes. We further developed a domain-specific lexical model that weights literature that is more related to the domain of metabolic engineering to emphasize genes modified for bioprocessing. Our technique made a suggestion by using a history of creating a Corynebacterium glutamicum strain producing shikimic acid that had 18 genetic modifications. Inspired by the suggestion, eight genes were considered by biologists for further modification, and modifying four of these genes proved experimentally efficient in increasing the production of shikimic acid. These results indicated that our proposed technique successfully utilized the former cycles to suggest relevant designs that biologists considered worth testing. Comprehensive retrieval of well-tested designs will help less-experienced researchers overcome the entry barrier as well as inspire experienced researchers to formulate design concepts that have been overlooked or suspended. This technique will aid DBTL cycles by feeding histories back to the next genetic design, thereby complementing the designing step.
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Affiliation(s)
- Shiori Nakazawa
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Osamu Imaichi
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Takahisa Kogure
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Takeshi Kubota
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Koichi Toyoda
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Masako Suda
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Masayuki Inui
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Kiyoto Ito
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Tomokazu Shirai
- Riken, 1-6 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 240-0035, Japan
| | - Michihiro Araki
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
- Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo 162-8638, Japan
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34
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Recent advances in biocatalysis of nitrogen-containing heterocycles. Biotechnol Adv 2021; 54:107813. [PMID: 34450199 DOI: 10.1016/j.biotechadv.2021.107813] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/27/2021] [Accepted: 08/08/2021] [Indexed: 12/20/2022]
Abstract
Nitrogen-containing heterocycles (N-heterocycles) are ubiquitous in both organisms and pharmaceutical products. Biocatalysts are providing green approaches for synthesizing various N-heterocycles under mild reaction conditions. This review summarizes the recent advances in the biocatalysis of N-heterocycles through the discovery and engineering of natural N-heterocycle synthetic pathway, and the design of artificial synthetic routes, with an emphasis on biocatalysts applied in retrosynthetic design for preparing complex N-heterocycles. Furthermore, this review discusses the future prospects and challenges of biocatalysts involved in the synthesis of N-heterocycles.
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35
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36
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Khattab AR, Farag MA. Marine and terrestrial endophytic fungi: a mine of bioactive xanthone compounds, recent progress, limitations, and novel applications. Crit Rev Biotechnol 2021; 42:403-430. [PMID: 34266351 DOI: 10.1080/07388551.2021.1940087] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Endophytic fungi are a kind of fungi that colonizes living plant tissues presenting a myriad of microbial adaptations that have been developed in such a hidden environment. Owing to its large diversity and particular habituation, they present a golden mine for research in the field of drug discovery. Endophytic fungal communities possess unique biocatalytic machinery that furnishes a myriad of complex natural product scaffolds. Xanthone compounds are examples of endophytic secondary metabolic products with pronounced biological activity to include: antioxidant, antimicrobial, anti-inflammatory, antithrombotic, antiulcer, choleretic, diuretic, and monoamine oxidase inhibiting activity.The current review compiles the recent progress made on the microbiological production of xanthones using fungal endophytes obtained from both marine and terrestrial origins, with comparisons being made among both natural resources. The biosynthesis of xanthones in endophytic fungi is outlined along with its decoding enzymes. Biotransformation reactions reported to be carried out using different endophytic microbial models are also outlined for xanthones structural modification purposes and the production of novel molecules.A promising application of novel computational tools is presented as a future direction for the goal of optimizing microbial xanthones production to include establishing metabolic pathway databases and the in silico analysis of microbial interactions. Metagenomics methods and related bioinformatics platforms are highlighted as unexplored tools for the biodiversity analysis of endophytic microbial communities that are difficult to be cultured.
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Affiliation(s)
- Amira R Khattab
- Pharmacognosy Department, College of Pharmacy, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
| | - Mohamed A Farag
- Pharmacognosy Department, College of Pharmacy, Cairo University, Cairo, Egypt.,Chemistry Department, School of Sciences & Engineering, The American University in Cairo, New Cairo, Egypt
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37
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Shah HA, Liu J, Yang Z, Feng J. Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways. Front Mol Biosci 2021; 8:634141. [PMID: 34222327 PMCID: PMC8247443 DOI: 10.3389/fmolb.2021.634141] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 06/01/2021] [Indexed: 11/13/2022] Open
Abstract
Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.
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Affiliation(s)
- Hayat Ali Shah
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Zhihui Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
| | - Jing Feng
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, China
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38
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Dixon TA, Williams TC, Pretorius IS. Bioinformational trends in grape and wine biotechnology. Trends Biotechnol 2021; 40:124-135. [PMID: 34108075 DOI: 10.1016/j.tibtech.2021.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 02/08/2023]
Abstract
The creative destruction caused by the coronavirus pandemic is yielding immense opportunity for collaborative innovation networks. The confluence of biosciences, information sciences, and the engineering of biology, is unveiling promising bioinformational futures for a vibrant and sustainable bioeconomy. Bioinformational engineering, underpinned by DNA reading, writing, and editing technologies, has become a beacon of opportunity in a world paralysed by uncertainty. This article draws on lessons from the current pandemic and previous agricultural blights, and explores bioinformational research directions aimed at future-proofing the grape and wine industry against biological shocks from global blights and climate change.
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Affiliation(s)
- Thomas A Dixon
- Department of Modern History, Politics and International Relations, Macquarie University, Sydney, NSW 2109, Australia.
| | - Thomas C Williams
- Department of Molecular Sciences and ARC Centre of Excellence in Synthetic Biology, Centre Headquarters, Macquarie University, Sydney, NSW 2109, Australia
| | - Isak S Pretorius
- Department of Molecular Sciences and ARC Centre of Excellence in Synthetic Biology, Centre Headquarters, Macquarie University, Sydney, NSW 2109, Australia; Chancellery, Macquarie University, Sydney, NSW 2109, Australia.
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39
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Wang L, Maranas CD. Computationally Prospecting Potential Pathways from Lignin Monomers and Dimers toward Aromatic Compounds. ACS Synth Biol 2021; 10:1064-1076. [PMID: 33877818 DOI: 10.1021/acssynbio.0c00598] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The heterogeneity of the aromatic products originating from lignin catalytic depolymerization remains one of the major challenges associated with lignin valorization. Microbes have evolved catabolic pathways that can funnel heterogeneous intermediates to a few central aromatic products. These aromatic compounds can subsequently undergo intra- or extradiol ring opening to produce value-added chemicals. However, such funneling pathways are only partially characterized for a few organisms such as Sphingobium sp. SYK-6 and Pseudomonas putida KT2440. Herein, we apply the de novo pathway design tool (novoStoic) to computationally prospect possible ways of funneling lignin-derived mono- and biaryls. novoStoic employs reaction rules between molecular moieties to hypothesize de novo conversions by flagging known enzymes that carry out the same biotransformation on the most similar substrate. Both reaction rules and known reactions are then deployed by novoStoic to identify a mass-balanced biochemical network that converts a source to a target metabolite while minimizing the number of de novo steps. We demonstrate the application of novoStoic for (i) designing alternative pathways of funneling S, G, and H lignin monomers, and (ii) exploring cleavage pathways of β-1 and β-β dimers. By exploring the uncharted chemical space afforded by enzyme promiscuity, novoStoic can help predict previously unknown native pathways leveraging enzyme promiscuity and propose new carbon/energy efficient lignin funneling pathways with few heterologous enzymes.
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Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Costas D. Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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40
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Hafner J, Hatzimanikatis V. Finding metabolic pathways in large networks through atom-conserving substrate-product pairs. Bioinformatics 2021; 37:3560-3568. [PMID: 34003971 PMCID: PMC8545321 DOI: 10.1093/bioinformatics/btab368] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/22/2021] [Accepted: 05/17/2021] [Indexed: 11/29/2022] Open
Abstract
Motivation Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in biosynthetic pathway design is the extraction of novel metabolic pathways from big networks that integrate known biological, as well as novel, predicted biotransformations. However, the efficient analysis and the navigation of big biochemical networks remain a challenge. Results Here, we propose the construction of searchable graph representations of metabolic networks. Each reaction is decomposed into pairs of reactants and products, and each pair is assigned a weight, which is calculated from the number of conserved atoms between the reactant and the product molecule. We test our method on a biochemical network that spans 6546 known enzymatic reactions to show how our approach elegantly extracts biologically relevant metabolic pathways from biochemical networks, and how the proposed network structure enables the application of efficient graph search algorithms that improve navigation and pathway identification in big metabolic networks. The weighted reactant–product pairs of an example network and the corresponding graph search algorithm are available online. The proposed method extracts metabolic pathways fast and reliably from big biochemical networks, which is inherently important for all applications involving the engineering of metabolic networks. Availability and implementation https://github.com/EPFL-LCSB/nicepath. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jasmin Hafner
- Laboratory of Computational Systems Biotechnology (LCSB), Institute of Chemical Sciences and Engineering (ISIC), School of Basic Sciences (SB), Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), Institute of Chemical Sciences and Engineering (ISIC), School of Basic Sciences (SB), Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland
- To whom correspondence should be addressed.
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Constraint-based metabolic control analysis for rational strain engineering. Metab Eng 2021; 66:191-203. [PMID: 33895366 DOI: 10.1016/j.ymben.2021.03.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/10/2021] [Accepted: 03/02/2021] [Indexed: 11/20/2022]
Abstract
The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.
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Bourgade B, Minton NP, Islam MA. Genetic and metabolic engineering challenges of C1-gas fermenting acetogenic chassis organisms. FEMS Microbiol Rev 2021; 45:fuab008. [PMID: 33595667 PMCID: PMC8351756 DOI: 10.1093/femsre/fuab008] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 01/15/2021] [Indexed: 12/11/2022] Open
Abstract
Unabated mining and utilisation of petroleum and petroleum resources and their conversion to essential fuels and chemicals have drastic environmental consequences, contributing to global warming and climate change. In addition, fossil fuels are finite resources, with a fast-approaching shortage. Accordingly, research efforts are increasingly focusing on developing sustainable alternatives for chemicals and fuels production. In this context, bioprocesses, relying on microorganisms, have gained particular interest. For example, acetogens use the Wood-Ljungdahl pathway to grow on single carbon C1-gases (CO2 and CO) as their sole carbon source and produce valuable products such as acetate or ethanol. These autotrophs can, therefore, be exploited for large-scale fermentation processes to produce industrially relevant chemicals from abundant greenhouse gases. In addition, genetic tools have recently been developed to improve these chassis organisms through synthetic biology approaches. This review will focus on the challenges of genetically and metabolically modifying acetogens. It will first discuss the physical and biochemical obstacles complicating successful DNA transfer in these organisms. Current genetic tools developed for several acetogens, crucial for strain engineering to consolidate and expand their catalogue of products, will then be described. Recent tool applications for metabolic engineering purposes to allow redirection of metabolic fluxes or production of non-native compounds will lastly be covered.
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Affiliation(s)
- Barbara Bourgade
- Department of Chemical Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, UK
| | - Nigel P Minton
- BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, University Park, University of Nottingham, Nottingham, Nottinghamshire, NG7 2RD, UK
| | - M Ahsanul Islam
- Department of Chemical Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, UK
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Tiwari P, Khare T, Shriram V, Bae H, Kumar V. Plant synthetic biology for producing potent phyto-antimicrobials to combat antimicrobial resistance. Biotechnol Adv 2021; 48:107729. [PMID: 33705914 DOI: 10.1016/j.biotechadv.2021.107729] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/22/2021] [Accepted: 03/04/2021] [Indexed: 12/14/2022]
Abstract
Inappropriate and injudicious use of antimicrobial drugs in human health, hygiene, agriculture, animal husbandry and food industries has contributed significantly to rapid emergence and persistence of antimicrobial resistance (AMR), one of the serious global public health threats. The crisis of AMR versus slower discovery of newer antibiotics put forth a daunting task to control these drug-resistant superbugs. Several phyto-antimicrobials have been identified in recent years with direct-killing (bactericidal) and/or drug-resistance reversal (re-sensitization of AMR phenotypes) potencies. Phyto-antimicrobials may hold the key in combating AMR owing to their abilities to target major microbial drug-resistance determinants including cell membrane, drug-efflux pumps, cell communication and biofilms. However, limited distribution, low intracellular concentrations, eco-geographical variations, beside other considerations like dynamic environments, climate change and over-exploitation of plant-resources are major blockades in full potential exploration phyto-antimicrobials. Synthetic biology (SynBio) strategies integrating metabolic engineering, RNA-interference, genome editing/engineering and/or systems biology approaches using plant chassis (as engineerable platforms) offer prospective tools for production of phyto-antimicrobials. With expanding SynBio toolkit, successful attempts towards introduction of entire gene cluster, reconstituting the metabolic pathway or transferring an entire metabolic (or synthetic) pathway into heterologous plant systems highlight the potential of this field. Through this perspective review, we are presenting herein the current situation and options for addressing AMR, emphasizing on the significance of phyto-antimicrobials in this apparently post-antibiotic era, and effective use of plant chassis for phyto-antimicrobial production at industrial scales along with major SynBio tools and useful databases. Current knowledge, recent success stories, associated challenges and prospects of translational success are also discussed.
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Affiliation(s)
- Pragya Tiwari
- Molecular Metabolic Engineering Lab, Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea
| | - Tushar Khare
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune 411016, India; Department of Environmental Science, Savitribai Phule Pune University, Pune 411007, India
| | - Varsha Shriram
- Department of Botany, Prof. Ramkrishna More Arts, Commerce and Science College, Savitribai Phule Pune University, Akurdi, Pune 411044, India
| | - Hanhong Bae
- Molecular Metabolic Engineering Lab, Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea.
| | - Vinay Kumar
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune 411016, India; Department of Environmental Science, Savitribai Phule Pune University, Pune 411007, India.
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44
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Fuji T, Nakazawa S, Ito K. Feasible-metabolic-pathway-exploration technique using chemical latent space. Bioinformatics 2021; 36:i770-i778. [PMID: 33381845 PMCID: PMC8454040 DOI: 10.1093/bioinformatics/btaa809] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Motivation Exploring metabolic pathways is one of the key techniques for developing highly
productive microbes for the bioproduction of chemical compounds. To explore feasible
pathways, not only examining a combination of well-known enzymatic reactions but also
finding potential enzymatic reactions that can catalyze the desired structural changes
are necessary. To achieve this, most conventional techniques use manually
predefined-reaction rules, however, they cannot sufficiently find potential reactions
because the conventional rules cannot comprehensively express structural changes before
and after enzymatic reactions. Evaluating the feasibility of the explored pathways is
another challenge because there is no way to validate the reaction possibility of
unknown enzymatic reactions by these rules. Therefore, a technique for comprehensively
capturing the structural changes in enzymatic reactions and a technique for evaluating
the pathway feasibility are still necessary to explore feasible metabolic pathways. Results We developed a feasible-pathway-exploration technique using chemical latent space
obtained from a deep generative model for compound structures. With this technique, an
enzymatic reaction is regarded as a difference vector between the main substrate and the
main product in chemical latent space acquired from the generative model. Features of
the enzymatic reaction are embedded into the fixed-dimensional vector, and it is
possible to express structural changes of enzymatic reactions comprehensively. The
technique also involves differential-evolution-based reaction selection to design
feasible candidate pathways and pathway scoring using neural-network-based
reaction-possibility prediction. The proposed technique was applied to the
non-registered pathways relevant to the production of 2-butanone, and successfully
explored feasible pathways that include such reactions.
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Affiliation(s)
- Taiki Fuji
- Center for Exploratory Research, Research and Development Group , Hitachi, Ltd., Kokubunji-shi, Tokyo 185-8601, Japan
| | - Shiori Nakazawa
- Center for Exploratory Research, Research and Development Group , Hitachi, Ltd., Kokubunji-shi, Tokyo 185-8601, Japan
| | - Kiyoto Ito
- Center for Exploratory Research, Research and Development Group , Hitachi, Ltd., Kokubunji-shi, Tokyo 185-8601, Japan
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45
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Bernstein DB, Sulheim S, Almaas E, Segrè D. Addressing uncertainty in genome-scale metabolic model reconstruction and analysis. Genome Biol 2021; 22:64. [PMID: 33602294 PMCID: PMC7890832 DOI: 10.1186/s13059-021-02289-z] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.
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Affiliation(s)
- David B Bernstein
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA
| | - Snorre Sulheim
- Bioinformatics Program, Boston University, Boston, MA, USA
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Biotechnology and Nanomedicine, SINTEF Industry, Trondheim, Norway
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Daniel Segrè
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA, USA.
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biology and Department of Physics, Boston University, Boston, MA, USA.
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46
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Lipid Traffic Analysis reveals the impact of high paternal carbohydrate intake on offsprings' lipid metabolism. Commun Biol 2021; 4:163. [PMID: 33547386 PMCID: PMC7864968 DOI: 10.1038/s42003-021-01686-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/08/2021] [Indexed: 12/12/2022] Open
Abstract
In this paper we present an investigation of parental-diet-driven metabolic programming in offspring using a novel computational network analysis tool. The impact of high paternal carbohydrate intake on offsprings’ phospholipid and triglyceride metabolism in F1 and F2 generations is described. Detailed lipid profiles were acquired from F1 neonate (3 weeks), F1 adult (16 weeks) and F2 neonate offspring in serum, liver, brain, heart and abdominal adipose tissues by MS and NMR. Using a purpose-built computational tool for analysing both phospholipid and fat metabolism as a network, we characterised the number, type and abundance of lipid variables in and between tissues (Lipid Traffic Analysis), finding a variety of reprogrammings associated with paternal diet. These results are important because they describe the long-term metabolic result of dietary intake by fathers. This analytical approach is important because it offers unparalleled insight into possible mechanisms for alterations in lipid metabolism throughout organisms. Furse et al. use a purpose-built computational tool called Lipid Traffic Analysis to determine the spatial distribution of lipids throughout an organism. They use it to show that high paternal carbohydrate intake influences lipid metabolism in offspring two generations hence.
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47
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Kim Y, Ryu JY, Kim HU, Jang WD, Lee SY. A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis. Biotechnol J 2021; 16:e2000605. [PMID: 33386776 DOI: 10.1002/biot.202000605] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 12/30/2020] [Indexed: 12/29/2022]
Abstract
Retrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective method that can reduce a large number of the initially predicted enzymatic reactions has been needed. Here, we present Deep learning-based Reaction Feasibility Checker (DeepRFC) to classify the feasibility of a given enzymatic reaction with high performance and speed. DeepRFC is designed to receive Simplified Molecular-Input Line-Entry System (SMILES) strings of a reactant pair, which is defined as a substrate and a product of a reaction, as an input, and evaluates whether the input reaction is feasible. A deep neural network is selected for DeepRFC as it leads to better classification performance than five other representative machine learning methods examined. For validation, the performance of DeepRFC is compared with another in-house reaction feasibility checker that uses the concept of reaction similarity. Finally, the use of DeepRFC is demonstrated for the retrobiosynthesis-based design of novel one-carbon assimilation pathways. DeepRFC will allow retrobiosynthesis to be more practical for metabolic engineering applications by efficiently screening a large number of retrobiosynthesis-derived enzymatic reactions. DeepRFC is freely available at https://bitbucket.org/kaistsystemsbiology/deeprfc.
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Affiliation(s)
- Yeji Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
| | - Jae Yong Ryu
- Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
| | - Hyun Uk Kim
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea.,Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, KAIST, Daejeon, Republic of Korea
| | - Woo Dae Jang
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, Republic of Korea.,KAIST Institute for Artificial Intelligence, BioProcess Engineering Research Center and Bioinformatics Research Center, KAIST, Daejeon, Republic of Korea
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48
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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49
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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50
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Desmet S, Brouckaert M, Boerjan W, Morreel K. Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks. Comput Struct Biotechnol J 2020; 19:72-85. [PMID: 33384856 PMCID: PMC7753198 DOI: 10.1016/j.csbj.2020.11.050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/26/2020] [Accepted: 11/28/2020] [Indexed: 02/06/2023] Open
Abstract
Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.
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Affiliation(s)
- Sandrien Desmet
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Marlies Brouckaert
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Wout Boerjan
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Kris Morreel
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
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