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Gong X, Zhang J, Gan Q, Teng Y, Hou J, Lyu Y, Liu Z, Wu Z, Dai R, Zou Y, Wang X, Zhu D, Zhu H, Liu T, Yan Y. Advancing microbial production through artificial intelligence-aided biology. Biotechnol Adv 2024; 74:108399. [PMID: 38925317 DOI: 10.1016/j.biotechadv.2024.108399] [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: 01/03/2024] [Revised: 05/20/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.
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Affiliation(s)
- Xinyu Gong
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Jianli Zhang
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Qi Gan
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Yuxi Teng
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Jixin Hou
- School of ECAM, College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Yanjun Lyu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
| | - Zhengliang Liu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Zihao Wu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Runpeng Dai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yusong Zou
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Xianqiao Wang
- School of ECAM, College of Engineering, University of Georgia, Athens, GA 30602, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianming Liu
- School of Computing, The University of Georgia, Athens, GA 30602, USA
| | - Yajun Yan
- School of Chemical, Materials, and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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2
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Zhang X, Liu J, Yang F, Zhang Q, Yang Z, Shah HA. Planning biosynthetic pathways of target molecules based on metabolic reaction prediction and AND-OR tree search. Comput Biol Chem 2024; 111:108106. [PMID: 38833912 DOI: 10.1016/j.compbiolchem.2024.108106] [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: 08/17/2023] [Revised: 05/06/2024] [Accepted: 05/13/2024] [Indexed: 06/06/2024]
Abstract
Bioretrosynthesis problem is to predict synthetic routes using substrates for given natural products (NPs). However, the huge number of metabolic reactions leads to a combinatorial explosion of searching space, which is high time-consuming and costly. Here, we propose a framework called BioRetro to predict bioretrosynthesis pathways using a one-step bioretrosynthesis network, termed HybridMLP combined with AND-OR tree heuristic search. The HybridMLP predicts precursors that will produce the target NPs, while the AND-OR tree generates the iterative multi-step biosynthetic pathways. The one-step bioretrosynthesis prediction experiments are conducted on MetaNetX dataset by using HybridMLP, which achieves 46.5%, 74.6%, 81.6% in terms of the top-1, top-5, top-10 accuracies. The great performance demonstrates the effectiveness of HybridMLP in one-step bioretrosynthesis. Besides, the evaluation of two benchmark datasets reveals that BioRetro can significantly improve the speed and success rate in predicting biosynthesis pathways. In addition, the BioRetro is further shown to find the synthetic pathway of compounds, such as ginsenoside F1 with the same substrates as reported but different enzymes, which may be the novel potential enzyme to have better catalytic performance.
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Affiliation(s)
- Xiaolei Zhang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Juan Liu
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Qiang Zhang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Zhihui Yang
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
| | - Hayat Ali Shah
- Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China
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3
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Ryu G, Kim GB, Yu T, Lee SY. Deep learning for metabolic pathway design. Metab Eng 2023; 80:130-141. [PMID: 37734652 DOI: 10.1016/j.ymben.2023.09.012] [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: 08/19/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023]
Abstract
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.
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Affiliation(s)
- Gahyeon Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Taeho Yu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon, 34141, Republic of Korea.
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4
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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: 2.0] [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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5
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Coppens L, Tschirhart T, Leary DH, Colston SM, Compton JR, Hervey WJ, Dana KL, Vora GJ, Bordel S, Ledesma-Amaro R. Vibrio natriegens genome-scale modeling reveals insights into halophilic adaptations and resource allocation. Mol Syst Biol 2023; 19:e10523. [PMID: 36847213 PMCID: PMC10090949 DOI: 10.15252/msb.202110523] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 03/01/2023] Open
Abstract
Vibrio natriegens is a Gram-negative bacterium with an exceptional growth rate that has the potential to become a standard biotechnological host for laboratory and industrial bioproduction. Despite this burgeoning interest, the current lack of organism-specific qualitative and quantitative computational tools has hampered the community's ability to rationally engineer this bacterium. In this study, we present the first genome-scale metabolic model (GSMM) of V. natriegens. The GSMM (iLC858) was developed using an automated draft assembly and extensive manual curation and was validated by comparing predicted yields, central metabolic fluxes, viable carbon substrates, and essential genes with empirical data. Mass spectrometry-based proteomics data confirmed the translation of at least 76% of the enzyme-encoding genes predicted to be expressed by the model during aerobic growth in a minimal medium. iLC858 was subsequently used to carry out a metabolic comparison between the model organism Escherichia coli and V. natriegens, leading to an analysis of the model architecture of V. natriegens' respiratory and ATP-generating system and the discovery of a role for a sodium-dependent oxaloacetate decarboxylase pump. The proteomics data were further used to investigate additional halophilic adaptations of V. natriegens. Finally, iLC858 was utilized to create a Resource Balance Analysis model to study the allocation of carbon resources. Taken together, the models presented provide useful computational tools to guide metabolic engineering efforts in V. natriegens.
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Affiliation(s)
- Lucas Coppens
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
| | - Tanya Tschirhart
- US Naval Research Laboratory, Center for Bio/Molecular Science and Engineering, Washington, DC, USA
| | - Dagmar H Leary
- US Naval Research Laboratory, Center for Bio/Molecular Science and Engineering, Washington, DC, USA
| | - Sophie M Colston
- US Naval Research Laboratory, Center for Bio/Molecular Science and Engineering, Washington, DC, USA
| | - Jaimee R Compton
- US Naval Research Laboratory, Center for Bio/Molecular Science and Engineering, Washington, DC, USA
| | - William Judson Hervey
- US Naval Research Laboratory, Center for Bio/Molecular Science and Engineering, Washington, DC, USA
| | | | - Gary J Vora
- US Naval Research Laboratory, Center for Bio/Molecular Science and Engineering, Washington, DC, USA
| | - Sergio Bordel
- Department of Chemical Engineering and Environmental Technology, School of Industrial Engineering, University of Valladolid, Valladolid, Spain
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
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6
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Tao XB, LaFrance S, Xing Y, Nava AA, Martin H, Keasling J, Backman TH. ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design. Nucleic Acids Res 2022; 51:D532-D538. [PMID: 36416273 PMCID: PMC9825560 DOI: 10.1093/nar/gkac1075] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/14/2022] [Accepted: 10/25/2022] [Indexed: 11/24/2022] Open
Abstract
Megasynthase enzymes such as type I modular polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) play a central role in microbial chemical warfare because they can evolve rapidly by shuffling parts (catalytic domains) to produce novel chemicals. If we can understand the design rules to reshuffle these parts, PKSs and NRPSs will provide a systematic and modular way to synthesize millions of molecules including pharmaceuticals, biomaterials, and biofuels. However, PKS and NRPS engineering remains difficult due to a limited understanding of the determinants of PKS and NRPS fold and function. We developed ClusterCAD to streamline and simplify the process of designing and testing engineered PKS variants. Here, we present the highly improved ClusterCAD 2.0 release, available at https://clustercad.jbei.org. ClusterCAD 2.0 boasts support for PKS-NRPS hybrid and NRPS clusters in addition to PKS clusters; a vastly enlarged database of curated PKS, PKS-NRPS hybrid, and NRPS clusters; a diverse set of chemical 'starters' and loading modules; the new Domain Architecture Cluster Search Tool; and an offline Jupyter Notebook workspace, among other improvements. Together these features massively expand the chemical space that can be accessed by enzymes engineered with ClusterCAD.
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Affiliation(s)
- Xavier B Tao
- Department of Chemistry, University of California, Berkeley, CA 94720, USA,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Sarah LaFrance
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA,Biofuels and Bioproducts Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA,QB3 Institute, University of California, Berkeley, CA 94720, USA
| | - Yifei Xing
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
| | - Alberto A Nava
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA,Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA
| | - Hector Garcia Martin
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA,Biofuels and Bioproducts Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA,Department of Energy Agile BioFoundry, Emeryville, CA 94608, USA
| | - Jay D Keasling
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA,Biofuels and Bioproducts Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA,Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA,Department of Bioengineering, University of California, Berkeley, CA 94720, USA,QB3 Institute, University of California, Berkeley, CA 94720, USA,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark 2800Copenhagen, Denmark,Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
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7
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The automated Galaxy-SynBioCAD pipeline for synthetic biology design and engineering. Nat Commun 2022; 13:5082. [PMID: 36038542 PMCID: PMC9424320 DOI: 10.1038/s41467-022-32661-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/11/2022] [Indexed: 11/27/2022] Open
Abstract
Here we introduce the Galaxy-SynBioCAD portal, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology. The tools and workflows currently shared on the portal enables one to build libraries of strains producing desired chemical targets covering an end-to-end metabolic pathway design and engineering process from the selection of strains and targets, the design of DNA parts to be assembled, to the generation of scripts driving liquid handlers for plasmid assembly and strain transformations. Standard formats like SBML and SBOL are used throughout to enforce the compatibility of the tools. In a study carried out at four different sites, we illustrate the link between pathway design and engineering with the building of a library of E. coli lycopene-producing strains. We also benchmark our workflows on literature and expert validated pathways. Overall, we find an 83% success rate in retrieving the validated pathways among the top 10 pathways generated by the workflows.
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8
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Cho JS, Kim GB, Eun H, Moon CW, Lee SY. Designing Microbial Cell Factories for the Production of Chemicals. JACS AU 2022; 2:1781-1799. [PMID: 36032533 PMCID: PMC9400054 DOI: 10.1021/jacsau.2c00344] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/26/2022] [Accepted: 07/26/2022] [Indexed: 05/24/2023]
Abstract
The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.
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Affiliation(s)
- Jae Sung Cho
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
- BioProcess
Engineering Research Center and BioInformatics Research Center, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Hyunmin Eun
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Cheon Woo Moon
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
- BioProcess
Engineering Research Center and BioInformatics Research Center, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
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9
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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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10
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Wagh VS, Said MS, Bennale JS, Dastager SG. Isolation and structural characterization of exopolysaccharide from marine Bacillus sp. and its optimization by Microbioreactor. Carbohydr Polym 2022; 285:119241. [DOI: 10.1016/j.carbpol.2022.119241] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 01/29/2022] [Accepted: 02/07/2022] [Indexed: 11/02/2022]
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11
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Maithani D, Sharma A, Gangola S, Choudhary P, Bhatt P. Insights into applications and strategies for discovery of microbial bioactive metabolites. Microbiol Res 2022; 261:127053. [DOI: 10.1016/j.micres.2022.127053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 03/12/2022] [Accepted: 04/26/2022] [Indexed: 10/25/2022]
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12
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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: 0.5] [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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13
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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: 11] [Impact Index Per Article: 5.5] [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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14
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Suthers PF, Maranas CD. Examining organic acid production potential and growth-coupled strategies in Issatchenkia orientalis using constraint-based modeling. Biotechnol Prog 2022; 38:e3276. [PMID: 35603544 PMCID: PMC9786923 DOI: 10.1002/btpr.3276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 05/16/2022] [Accepted: 05/20/2022] [Indexed: 12/30/2022]
Abstract
Growth-coupling product formation can facilitate strain stability by aligning industrial objectives with biological fitness. Organic acids make up many building block chemicals that can be produced from sugars obtainable from renewable biomass. Issatchenkia orientalis is a yeast strain tolerant to acidic conditions and is thus a promising host for industrial production of organic acids. Here, we use constraint-based methods to assess the potential of computationally designing growth-coupled production strains for I. orientalis that produce 22 different organic acids under aerobic or microaerobic conditions. We explore native and engineered pathways using glucose or xylose as the carbon substrates as proxy constituents of hydrolyzed biomass. We identified growth-coupled production strategies for 37 of the substrate-product pairs, with 15 pairs achieving production for any growth rate. We systematically assess the strain design solutions and categorize the underlying principles involved.
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Affiliation(s)
- Patrick F. Suthers
- Department of Chemical EngineeringThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA,Center for Advanced Bioenergy and Bioproducts InnovationThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Costas D. Maranas
- Department of Chemical EngineeringThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA,Center for Advanced Bioenergy and Bioproducts InnovationThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
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15
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Vila-Santa A, Mendes FC, Ferreira FC, Prather KLJ, Mira NP. Implementation of Synthetic Pathways to Foster Microbe-Based Production of Non-Naturally Occurring Carboxylic Acids and Derivatives. J Fungi (Basel) 2021; 7:jof7121020. [PMID: 34947002 PMCID: PMC8706239 DOI: 10.3390/jof7121020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/15/2021] [Accepted: 11/20/2021] [Indexed: 11/20/2022] Open
Abstract
Microbially produced carboxylic acids (CAs) are considered key players in the implementation of more sustainable industrial processes due to their potential to replace a set of oil-derived commodity chemicals. Most CAs are intermediates of microbial central carbon metabolism, and therefore, a biochemical production pathway is described and can be transferred to a host of choice to enable/improve production at an industrial scale. However, for some CAs, the implementation of this approach is difficult, either because they do not occur naturally (as is the case for levulinic acid) or because the described production pathway cannot be easily ported (as it is the case for adipic, muconic or glucaric acids). Synthetic biology has been reshaping the range of molecules that can be produced by microbial cells by setting new-to-nature pathways that leverage on enzyme arrangements not observed in vivo, often in association with the use of substrates that are not enzymes’ natural ones. In this review, we provide an overview of how the establishment of synthetic pathways, assisted by computational tools for metabolic retrobiosynthesis, has been applied to the field of CA production. The translation of these efforts in bridging the gap between the synthesis of CAs and of their more interesting derivatives, often themselves non-naturally occurring molecules, is also reviewed using as case studies the production of methacrylic, methylmethacrylic and poly-lactic acids.
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Affiliation(s)
- Ana Vila-Santa
- Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Department of Bioengineering, University of Lisbon, 1049-001 Lisbon, Portugal; (A.V.-S.); (F.C.M.); (F.C.F.)
- Associate Laboratory i4HB—Institute for Health and Bioeconomy at Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
| | - Fernão C. Mendes
- Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Department of Bioengineering, University of Lisbon, 1049-001 Lisbon, Portugal; (A.V.-S.); (F.C.M.); (F.C.F.)
- Associate Laboratory i4HB—Institute for Health and Bioeconomy at Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
| | - Frederico C. Ferreira
- Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Department of Bioengineering, University of Lisbon, 1049-001 Lisbon, Portugal; (A.V.-S.); (F.C.M.); (F.C.F.)
- Associate Laboratory i4HB—Institute for Health and Bioeconomy at Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
| | - Kristala L. J. Prather
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;
| | - Nuno P. Mira
- Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Department of Bioengineering, University of Lisbon, 1049-001 Lisbon, Portugal; (A.V.-S.); (F.C.M.); (F.C.F.)
- Associate Laboratory i4HB—Institute for Health and Bioeconomy at Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
- Correspondence:
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16
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Schneider P, Mahadevan R, Klamt S. Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs. Biotechnol J 2021; 16:e2100236. [PMID: 34432943 DOI: 10.1002/biot.202100236] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/08/2022]
Abstract
A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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17
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18
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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: 0.7] [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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19
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The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes. Essays Biochem 2021; 65:261-275. [PMID: 33956071 DOI: 10.1042/ebc20200132] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/29/2021] [Accepted: 03/31/2021] [Indexed: 02/08/2023]
Abstract
Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.
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20
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Lan YJ, Tan SI, Cheng SY, Ting WW, Xue C, Lin TH, Cai MZ, Chen PT, Ng IS. Development of Escherichia coli Nissle 1917 derivative by CRISPR/Cas9 and application for gamma-aminobutyric acid (GABA) production in antibiotic-free system. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2021.107952] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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21
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Hafner J, Payne J, MohammadiPeyhani H, Hatzimanikatis V, Smolke C. A computational workflow for the expansion of heterologous biosynthetic pathways to natural product derivatives. Nat Commun 2021; 12:1760. [PMID: 33741955 PMCID: PMC7979880 DOI: 10.1038/s41467-021-22022-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 02/24/2021] [Indexed: 01/31/2023] Open
Abstract
Plant natural products (PNPs) and their derivatives are important but underexplored sources of pharmaceutical molecules. To access this untapped potential, the reconstitution of heterologous PNP biosynthesis pathways in engineered microbes provides a valuable starting point to explore and produce novel PNP derivatives. Here, we introduce a computational workflow to systematically screen the biochemical vicinity of a biosynthetic pathway for pharmaceutical compounds that could be produced by derivatizing pathway intermediates. We apply our workflow to the biosynthetic pathway of noscapine, a benzylisoquinoline alkaloid (BIA) with a long history of medicinal use. Our workflow identifies pathways and enzyme candidates for the production of (S)-tetrahydropalmatine, a known analgesic and anxiolytic, and three additional derivatives. We then construct pathways for these compounds in yeast, resulting in platforms for de novo biosynthesis of BIA derivatives and demonstrating the value of cheminformatic tools to predict reactions, pathways, and enzymes in synthetic biology and metabolic engineering.
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Affiliation(s)
- Jasmin Hafner
- Laboratory of Computational Systems Biotechnology, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - James Payne
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Homa MohammadiPeyhani
- Laboratory of Computational Systems Biotechnology, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
| | - Christina Smolke
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, USA.
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22
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Porokhin V, Amin SA, Nicks TB, Gopinarayanan VE, Nair NU, Hassoun S. Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity. Metab Eng Commun 2021; 12:e00170. [PMID: 33850714 PMCID: PMC8039717 DOI: 10.1016/j.mec.2021.e00170] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/22/2021] [Accepted: 03/01/2021] [Indexed: 11/30/2022] Open
Abstract
Increasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Transforming organisms such as Escherichia coli into microbial factories is achieved via several engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed Metabolic Disruption Workflow (MDFlow), for discovering interactions and network disruptions arising from enzyme promiscuity – the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We demonstrate how enzyme promiscuity may aid cells in adapting to disruptions of essential metabolic functions. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropionic acid (3-HP) production. Using MDFlow, we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. As we demonstrate, MDFlow provides an innovative workflow to systematically identify incompatibilities between the native metabolism of the host and its engineered modifications due to enzyme promiscuity. Engineering modifications to cellular hosts result in undesirable byproducts. Metabolic Disruption: changes in engineered host due to enzyme promiscuity. Metabolic Disruption Workflow (MDFlow) uncovers metabolic disruption. MDFlow corroborates previously experimentally verified promiscuous interactions. MDFlow compares disruption due to heterologous pathways targeting 3-HP production.
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Affiliation(s)
| | - Sara A Amin
- Department of Computer Science, Tufts University, Medford, MA, USA
| | - Trevor B Nicks
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | | | - Nikhil U Nair
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA.,Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
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23
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Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design. Metab Eng 2021; 65:79-87. [PMID: 33662575 DOI: 10.1016/j.ymben.2021.02.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/05/2021] [Accepted: 02/23/2021] [Indexed: 01/29/2023]
Abstract
Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. We developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.
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24
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Liu Y, Benitez MG, Chen J, Harrison E, Khusnutdinova AN, Mahadevan R. Opportunities and Challenges for Microbial Synthesis of Fatty Acid-Derived Chemicals (FACs). Front Bioeng Biotechnol 2021; 9:613322. [PMID: 33575251 PMCID: PMC7870715 DOI: 10.3389/fbioe.2021.613322] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/04/2021] [Indexed: 11/13/2022] Open
Abstract
Global warming and uneven distribution of fossil fuels worldwide concerns have spurred the development of alternative, renewable, sustainable, and environmentally friendly resources. From an engineering perspective, biosynthesis of fatty acid-derived chemicals (FACs) is an attractive and promising solution to produce chemicals from abundant renewable feedstocks and carbon dioxide in microbial chassis. However, several factors limit the viability of this process. This review first summarizes the types of FACs and their widely applications. Next, we take a deep look into the microbial platform to produce FACs, give an outlook for the platform development. Then we discuss the bottlenecks in metabolic pathways and supply possible solutions correspondingly. Finally, we highlight the most recent advances in the fast-growing model-based strain design for FACs biosynthesis.
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Affiliation(s)
- Yilan Liu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Mauricio Garcia Benitez
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Jinjin Chen
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Emma Harrison
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Anna N. Khusnutdinova
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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25
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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: 2.0] [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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26
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Motwalli O, Uludag M, Mijakovic I, Alazmi M, Bajic VB, Gojobori T, Gao X, Essack M. PATH cre8: A Tool That Facilitates the Searching for Heterologous Biosynthetic Routes. ACS Synth Biol 2020; 9:3217-3227. [PMID: 33198455 DOI: 10.1021/acssynbio.0c00058] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Developing computational tools that can facilitate the rational design of cell factories producing desired products at increased yields is challenging, as the tool needs to take into account that the preferred host organism usually has compounds that are consumed by competing reactions that reduce the yield of the desired product. On the other hand, the preferred host organisms may not have the native metabolic reactions needed to produce the compound of interest; thus, the computational tool needs to identify the metabolic reactions that will most efficiently produce the desired product. In this regard, we developed the generic tool PATHcre8 to facilitate an optimized search for heterologous biosynthetic pathway routes. PATHcre8 finds and ranks biosynthesis routes in a large number of organisms, including Cyanobacteria. The tool ranks the pathways based on feature scores that reflect reaction thermodynamics, the potentially toxic products in the pathway (compound toxicity), intermediate products in the pathway consumed by competing reactions (product consumption), and host-specific information such as enzyme copy number. A comparison with several other similar tools shows that PATHcre8 is more efficient in ranking functional pathways. To illustrate the effectiveness of PATHcre8, we further provide case studies focused on isoprene production and the biodegradation of cocaine. PATHcre8 is free for academic and nonprofit users and can be accessed at https://www.cbrc.kaust.edu.sa/pathcre8/.
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Affiliation(s)
- Olaa Motwalli
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Saudi Electronic University (SEU), College of Computing and Informatics, Madinah 41538-53307, Kingdom of Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Ivan Mijakovic
- Chalmers University of Technology, Division of Systems & Synthetic Biology, Department of Biology and Biological Engineering, Kemivägen 10, 41296 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Meshari Alazmi
- Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, P.O. Box 2440, Ha’il 81411, Kingdom of Saudi Arabia
| | - Vladimir B. Bajic
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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27
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Otero-Muras I, Carbonell P. Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng 2020; 63:61-80. [PMID: 33316374 DOI: 10.1016/j.ymben.2020.11.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/15/2020] [Accepted: 11/20/2020] [Indexed: 12/19/2022]
Abstract
Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.
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Affiliation(s)
- Irene Otero-Muras
- BioProcess Engineering Group, IIM-CSIC, Spanish National Research Council, Vigo, 36208, Spain.
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (ai2), Universitat Politècnica de València, 46022, Spain.
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Ranjbar S, Shahmansouri M, Attri P, Bogaerts A. Effect of plasma-induced oxidative stress on the glycolysis pathway of Escherichia coli. Comput Biol Med 2020; 127:104064. [PMID: 33171288 DOI: 10.1016/j.compbiomed.2020.104064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 01/01/2023]
Abstract
Antibiotic resistance is one of the world's most urgent public health problems. Due to its antibacterial properties, cold atmospheric plasma (CAP) may serve as an alternative method to antibiotics. It is claimed that oxidative stress caused by CAP is the main reason of bacteria inactivation. In this work, we computationally investigated the effect of plasma-induced oxidation on various glycolysis metabolites, by monitoring the production of the biomass. We observed that in addition to the significant reduction in biomass production, the rate of some reactions has increased. These reactions produce anti-oxidant products, showing the bacterial defense mechanism to escape the oxidative damage. Nevertheless, the simulations show that the plasma-induced oxidation effect is much stronger than the defense mechanism, causing killing of the bacteria.
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Affiliation(s)
- S Ranjbar
- Department of Physics, Faculty of Science, Arak University, Arak, 38156-8-8349, Iran; Research Group PLASMANT, University of Antwerp, Department of Chemistry, Universiteitsplein 1, Wilrijk-Antwerp, B-2610, Belgium.
| | - M Shahmansouri
- Department of Physics, Faculty of Science, Arak University, Arak, 38156-8-8349, Iran
| | - P Attri
- Research Group PLASMANT, University of Antwerp, Department of Chemistry, Universiteitsplein 1, Wilrijk-Antwerp, B-2610, Belgium; Center of Plasma Nano-interface Engineering, Kyushu University, Fukuoka, 819-0395, Japan
| | - A Bogaerts
- Research Group PLASMANT, University of Antwerp, Department of Chemistry, Universiteitsplein 1, Wilrijk-Antwerp, B-2610, Belgium
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Carbonell P, Le Feuvre R, Takano E, Scrutton NS. In silico design and automated learning to boost next-generation smart biomanufacturing. Synth Biol (Oxf) 2020; 5:ysaa020. [PMID: 33344778 PMCID: PMC7737007 DOI: 10.1093/synbio/ysaa020] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/08/2020] [Accepted: 09/28/2020] [Indexed: 02/07/2023] Open
Abstract
The increasing demand for bio-based compounds produced from waste or sustainable sources is driving biofoundries to deliver a new generation of prototyping biomanufacturing platforms. Integration and automation of the design, build, test and learn (DBTL) steps in centers like SYNBIOCHEM in Manchester and across the globe (Global Biofoundries Alliance) are helping to reduce the delivery time from initial strain screening and prototyping towards industrial production. Notably, a portfolio of producer strains for a suite of material monomers was recently developed, some approaching industrial titers, in a tour de force by the Manchester Centre that was achieved in less than 90 days. New in silico design tools are providing significant contributions to the front end of the DBTL pipelines. At the same time, the far-reaching initiatives of modern biofoundries are generating a large amount of high-dimensional data and knowledge that can be integrated through automated learning to expedite the DBTL cycle. In this Perspective, the new design tools and the role of the learning component as an enabling technology for the next generation of automated biofoundries are discussed. Future biofoundries will operate under completely automated DBTL cycles driven by in silico optimal experimental planning, full biomanufacturing devices connectivity, virtualization platforms and cloud-based design. The automated generation of robotic build worklists and the integration of machine-learning algorithms will collectively allow high levels of adaptability and rapid design changes toward fully automated smart biomanufacturing.
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Affiliation(s)
- Pablo Carbonell
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.,Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Rosalind Le Feuvre
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Eriko Takano
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
| | - Nigel S Scrutton
- Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK
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30
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Chen F, Yuan L, Ding S, Tian Y, Hu QN. Data-driven rational biosynthesis design: from molecules to cell factories. Brief Bioinform 2020; 21:1238-1248. [PMID: 31243440 DOI: 10.1093/bib/bbz065] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 04/28/2019] [Accepted: 05/08/2019] [Indexed: 11/12/2022] Open
Abstract
A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.
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Affiliation(s)
- Fu Chen
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin, People's Republic of China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.,CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Le Yuan
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.,University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Shaozhen Ding
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Yu Tian
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China.,University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
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Ding S, Tian Y, Cai P, Zhang D, Cheng X, Sun D, Yuan L, Chen J, Tu W, Wei DQ, Hu QN. novoPathFinder: a webserver of designing novel-pathway with integrating GEM-model. Nucleic Acids Res 2020; 48:W477-W487. [PMID: 32313937 PMCID: PMC7319456 DOI: 10.1093/nar/gkaa230] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 03/16/2020] [Accepted: 03/28/2020] [Indexed: 12/14/2022] Open
Abstract
To increase the number of value-added chemicals that can be produced by metabolic engineering and synthetic biology, constructing metabolic space with novel reactions/pathways is crucial. However, with the large number of reactions that existed in the metabolic space and complicated metabolisms within hosts, identifying novel pathways linking two molecules or heterologous pathways when engineering a host to produce a target molecule is an arduous task. Hence, we built a user-friendly web server, novoPathFinder, which has several features: (i) enumerate novel pathways between two specified molecules without considering hosts; (ii) construct heterologous pathways with known or putative reactions for producing target molecule within Escherichia coli or yeast without giving precursor; (iii) estimate novel pathways with considering several categories, including enzyme promiscuity, Synthetic Complex Score (SCScore) and LD50 of intermediates, overall stoichiometric conversions, pathway length, theoretical yields and thermodynamic feasibility. According to the results, novoPathFinder is more capable to recover experimentally validated pathways when comparing other rule-based web server tools. Besides, more efficient pathways with novel reactions could also be retrieved for further experimental exploration. novoPathFinder is available at http://design.rxnfinder.org/novopathfinder/.
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Affiliation(s)
- Shaozhen Ding
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Yu Tian
- School of Biology and Pharmaceutical Engineering, Wuhan Polytechnic University, Wuhan, Hubei 430023, China
| | - Pengli Cai
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, People's Republic of China
| | - Dachuan Zhang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Xingxiang Cheng
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Dandan Sun
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
| | - Le Yuan
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden
| | - Junni Chen
- Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, People's Republic of China
| | - Weizhong Tu
- Wuhan LifeSynther Science and Technology Co. Limited, Wuhan 430070, People's Republic of China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism (Shanghai Jiao Tong University), Shanghai 200240, China
| | - Qian-Nan Hu
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Shanghai 200031, People's Republic of China
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32
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Schneider P, Klamt S. Characterizing and ranking computed metabolic engineering strategies. Bioinformatics 2020; 35:3063-3072. [PMID: 30649194 PMCID: PMC6735923 DOI: 10.1093/bioinformatics/bty1065] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 11/28/2018] [Accepted: 01/07/2019] [Indexed: 01/06/2023] Open
Abstract
MOTIVATION The computer-aided design of metabolic intervention strategies has become a key component of an integrated metabolic engineering approach and a broad range of methods and algorithms has been developed for this task. Many of these algorithms enforce coupling of growth with product synthesis and may return thousands of possible intervention strategies from which the most suitable strategy must then be selected. RESULTS This work focuses on how to evaluate and rank, in a meaningful way, a given pool of computed metabolic engineering strategies for growth-coupled product synthesis. Apart from straightforward criteria, such as a preferably small number of necessary interventions, a reasonable growth rate and a high product yield, we present several new criteria useful to pick the most suitable intervention strategy. Among others, we investigate the robustness of the intervention strategies by searching for metabolites that may disrupt growth coupling when accumulated or secreted and by checking whether the interventions interrupt pathways at their origin (preferable) or at downstream steps. We also assess thermodynamic properties of the pathway(s) favored by the intervention strategy. Furthermore, strategies that have a significant overlap with alternative solutions are ranked higher because they provide flexibility in implementation. We also introduce the notion of equivalence classes for grouping intervention strategies with identical solution spaces. Our ranking procedure involves in total ten criteria and we demonstrate its applicability by assessing knockout-based intervention strategies computed in a genome-scale model of E.coli for the growth-coupled synthesis of l-methionine and of the heterologous product 1,4-butanediol. AVAILABILITY AND IMPLEMENTATION The MATLAB scripts that were used to characterize and rank the example intervention strategies are available at http://www2.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany
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Whitehead TA, Banta S, Bentley WE, Betenbaugh MJ, Chan C, Clark DS, Hoesli CA, Jewett MC, Junker B, Koffas M, Kshirsagar R, Lewis A, Li CT, Maranas C, Terry Papoutsakis E, Prather KLJ, Schaffer S, Segatori L, Wheeldon I. The importance and future of biochemical engineering. Biotechnol Bioeng 2020; 117:2305-2318. [PMID: 32343367 DOI: 10.1002/bit.27364] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/24/2020] [Accepted: 04/26/2020] [Indexed: 02/06/2023]
Abstract
Today's Biochemical Engineer may contribute to advances in a wide range of technical areas. The recent Biochemical and Molecular Engineering XXI conference focused on "The Next Generation of Biochemical and Molecular Engineering: The role of emerging technologies in tomorrow's products and processes". On the basis of topical discussions at this conference, this perspective synthesizes one vision on where investment in research areas is needed for biotechnology to continue contributing to some of the world's grand challenges.
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Affiliation(s)
- Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado, Boulder, Colorado
| | - Scott Banta
- Department of Chemical Engineering, Columbia University, New York, New York
| | - William E Bentley
- Fischell Department of Bioengineering, University of Maryland, College Park, Maryland
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Christina Chan
- Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan
| | - Douglas S Clark
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California
| | - Corinne A Hoesli
- Department of Chemical Engineering & Department of Biological and Biomedical Engineering, McGill University, Montreal, Québec, Canada
| | - Michael C Jewett
- Department of Chemical and Biological Engineering and Center for Synthetic Biology, Northwestern University, Evanston, Illinois
| | - Beth Junker
- BioProcess Advantage LLC, Middesex, New Jersey
| | - Mattheos Koffas
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | | | | | - Chien-Ting Li
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Costas Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania
| | - E Terry Papoutsakis
- Department of Chemical & Biomolecular Engineering & the Delaware Biotechnology Institute, University of Delaware, Newark, Delaware
| | - Kristala L J Prather
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | | | - Laura Segatori
- Department of Bioengineering, Rice University, Houston, Texas
| | - Ian Wheeldon
- Department of Chemical and Environmental Engineering, University of California, Riverside, California
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Abstract
Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines.
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Wiltschi B, Cernava T, Dennig A, Galindo Casas M, Geier M, Gruber S, Haberbauer M, Heidinger P, Herrero Acero E, Kratzer R, Luley-Goedl C, Müller CA, Pitzer J, Ribitsch D, Sauer M, Schmölzer K, Schnitzhofer W, Sensen CW, Soh J, Steiner K, Winkler CK, Winkler M, Wriessnegger T. Enzymes revolutionize the bioproduction of value-added compounds: From enzyme discovery to special applications. Biotechnol Adv 2020; 40:107520. [DOI: 10.1016/j.biotechadv.2020.107520] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 10/18/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
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Markina NM, Kotlobay AA, Tsarkova AS. Heterologous Metabolic Pathways: Strategies for Optimal Expression in Eukaryotic Hosts. Acta Naturae 2020; 12:28-39. [PMID: 32742725 PMCID: PMC7385092 DOI: 10.32607/actanaturae.10966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 04/29/2020] [Indexed: 11/20/2022] Open
Abstract
Heterologous pathways are linked series of biochemical reactions occurring in a host organism after the introduction of foreign genes. Incorporation of metabolic pathways into host organisms is a major strategy used to increase the production of valuable secondary metabolites. Unfortunately, simple introduction of the pathway genes into the heterologous host in most cases does not result in successful heterologous expression. Extensive modification of heterologous genes and the corresponding enzymes on many different levels is required to achieve high target metabolite production rates. This review summarizes the essential techniques used to create heterologous biochemical pathways, with a focus on the key challenges arising in the process and the major strategies for overcoming them.
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Affiliation(s)
- N. M. Markina
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997 Russia
- Planta LLC, Moscow, 121205 Russia
| | - A. A. Kotlobay
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997 Russia
| | - A. S. Tsarkova
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997 Russia
- Pirogov Russian National Research Medical University, Moscow, 117997 Russia
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Abstract
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.
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Affiliation(s)
- Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
- iSSB Laboratory, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057 Evry, France
- SYNBIOCHEM Center, School of Chemistry, University of Manchester, Manchester M13 9PL, U.K
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Kim SM, Peña MI, Moll M, Bennett GN, Kavraki LE. Improving the organization and interactivity of metabolic pathfinding with precomputed pathways. BMC Bioinformatics 2020; 21:13. [PMID: 31924164 PMCID: PMC6954563 DOI: 10.1186/s12859-019-3328-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 12/18/2019] [Indexed: 11/11/2022] Open
Abstract
Background The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others.To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways. Results A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest. Conclusions This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.
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Affiliation(s)
- Sarah M Kim
- Department of Computer Science, Rice University, Houston, Texas, USA
| | - Matthew I Peña
- Department of BioSciences, Rice University, Houston, Texas, USA
| | - Mark Moll
- Department of Computer Science, Rice University, Houston, Texas, USA.
| | | | - Lydia E Kavraki
- Department of Computer Science, Rice University, Houston, Texas, USA
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Yang X, Yuan Q, Luo H, Li F, Mao Y, Zhao X, Du J, Li P, Ju X, Zheng Y, Chen Y, Liu Y, Jiang H, Yao Y, Ma H, Ma Y. Systematic design and in vitro validation of novel one-carbon assimilation pathways. Metab Eng 2019; 56:142-153. [DOI: 10.1016/j.ymben.2019.09.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 08/17/2019] [Accepted: 09/01/2019] [Indexed: 11/24/2022]
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40
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Tyzack JD, Ribeiro AJM, Borkakoti N, Thornton JM. Exploring Chemical Biosynthetic Design Space with Transform-MinER. ACS Synth Biol 2019; 8:2494-2506. [PMID: 31647630 DOI: 10.1021/acssynbio.9b00105] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Transform-MinER (Transforming Molecules in Enzyme Reactions) is a web application facilitating the exploration of chemical biosynthetic space, guiding the user toward promising start points for enzyme design projects or directed evolution experiments. Two types of search are possible: Molecule Search allows a user to submit a source substrate enabling Transform-MinER to search for enzyme reactions acting on similar substrates, whereas Path Search additionally allows a user to submit a target molecule enabling Transform-MinER to search for a path of enzyme reactions acting on similar substrates to link source and target. Transform-MinER searches for potential reaction centers in the source substrate and uses chemoinformatic fingerprints to identify those that are situated in molecular environments similar to native counterparts, prioritizing steps that move closer to the target using reactions most similar to native in its exploration of search space. The ligand-based methodology behind Transform-MinER is presented, and its performance is validated yielding 90% success rates: first, on a data set of native pathways from the KEGG database, and second, on a data set of de novo enzyme reactions.
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Affiliation(s)
- Jonathan D. Tyzack
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Antonio J. M. Ribeiro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Neera Borkakoti
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
| | - Janet M. Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom
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Systems biology based metabolic engineering for non-natural chemicals. Biotechnol Adv 2019; 37:107379. [DOI: 10.1016/j.biotechadv.2019.04.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 02/23/2019] [Accepted: 04/01/2019] [Indexed: 12/17/2022]
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Gilbert J, Pearcy N, Norman R, Millat T, Winzer K, King J, Hodgman C, Minton N, Twycross J. Gsmodutils: a python based framework for test-driven genome scale metabolic model development. Bioinformatics 2019; 35:3397-3403. [PMID: 30759197 PMCID: PMC6748746 DOI: 10.1093/bioinformatics/btz088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Revised: 01/29/2019] [Accepted: 02/12/2019] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle. RESULTS As part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions. AVAILABILITY AND IMPLEMENTATION The software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- James Gilbert
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Nicole Pearcy
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Rupert Norman
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
- School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, UK
| | - Thomas Millat
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Klaus Winzer
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - John King
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Charlie Hodgman
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
- School of Biosciences, University of Nottingham, Sutton Bonington, Loughborough, UK
| | - Nigel Minton
- Synthetic Biology Research Centre, University of Nottingham, Nottingham, UK
| | - Jamie Twycross
- School of Computer Science, University of Nottingham, Nottingham, UK
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Whitmore LS, Nguyen B, Pinar A, George A, Hudson CM. RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms. BMC Bioinformatics 2019; 20:461. [PMID: 31500573 PMCID: PMC6734243 DOI: 10.1186/s12859-019-3025-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 08/12/2019] [Indexed: 11/24/2022] Open
Abstract
Background The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Results Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. Conclusions The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production. Electronic supplementary material The online version of this article (10.1186/s12859-019-3025-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Bernard Nguyen
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA
| | - Ali Pinar
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA
| | - Anthe George
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA
| | - Corey M Hudson
- Sandia National Laboratories, East Avenue, Livermore, 94550, USA.
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Vijayakumar S, Conway M, Lió P, Angione C. Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Brief Bioinform 2019; 19:1218-1235. [PMID: 28575143 DOI: 10.1093/bib/bbx053] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Indexed: 11/13/2022] Open
Abstract
Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.
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Affiliation(s)
| | - Max Conway
- Computer Laboratory, University of Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, UK
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Presnell KV, Alper HS. Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering. Biotechnol J 2019; 14:e1800416. [PMID: 30927499 DOI: 10.1002/biot.201800416] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/20/2019] [Indexed: 12/30/2022]
Abstract
The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.
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Affiliation(s)
- Kristin V Presnell
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA
| | - Hal S Alper
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, 100 E 24 St., Austin, TX, 78712, USA
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Lin GM, Warden-Rothman R, Voigt CA. Retrosynthetic design of metabolic pathways to chemicals not found in nature. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.04.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc Natl Acad Sci U S A 2019; 116:7298-7307. [PMID: 30910961 PMCID: PMC6462048 DOI: 10.1073/pnas.1818877116] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them “orphan.” A large number of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, the computational method BridgIT identified potential enzymes of orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. The BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze nonnatural transformations. Thousands of biochemical reactions with characterized activities are “orphan,” meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.
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Wehrs M, Tanjore D, Eng T, Lievense J, Pray TR, Mukhopadhyay A. Engineering Robust Production Microbes for Large-Scale Cultivation. Trends Microbiol 2019; 27:524-537. [PMID: 30819548 DOI: 10.1016/j.tim.2019.01.006] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/11/2019] [Accepted: 01/23/2019] [Indexed: 11/27/2022]
Abstract
Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.
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Affiliation(s)
- Maren Wehrs
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Institut für Genetik, Technische Universität Braunschweig, Braunschweig, Germany; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA
| | - Deepti Tanjore
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Thomas Eng
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA
| | | | - Todd R Pray
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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Contador CA, Rodríguez V, Andrews BA, Asenjo JA. Use of genome-scale models to get new insights into the marine actinomycete genus Salinispora. BMC SYSTEMS BIOLOGY 2019; 13:11. [PMID: 30665399 PMCID: PMC6341766 DOI: 10.1186/s12918-019-0683-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 01/11/2019] [Indexed: 11/10/2022]
Abstract
BACKGROUND There is little published regarding metabolism of Salinispora species. In continuation with efforts performed towards this goal, this study is focused on new insights into the metabolism of the three-identified species of Salinispora using constraints-based modeling. At present, only one manually curated genome-scale metabolic model (GSM) for Salinispora tropica strain CNB-440T has been built despite the role of Salinispora strains in drug discovery. RESULTS Here, we updated, and expanded the scope of the model of Salinispora tropica CNB-440T, and GSMs were constructed for two sequenced type strains covering the three-identified species. We also constructed a Salinispora core model that contains the genes shared by 93 sequenced strains and a few non-conserved genes associated with essential reactions. The models predicted no auxotrophies for essential amino acids, which was corroborated experimentally using a defined minimal medium (DMM). Experimental observations suggest possible sulfur accumulation. The Core metabolic content shows that the biosynthesis of specialised metabolites is the less conserved subsystem. Sets of reactions were analyzed to explore the differences between the reconstructions. Unique reactions associated to each GSM were mainly due to genome sequence data except for the ST-CNB440 reconstruction. In this case, additional reactions were added from experimental evidence. This reveals that by reaction content the ST-CNB440 model is different from the other species models. The differences identified in reaction content between models gave rise to different functional predictions of essential nutrient usage by each species in DMM. Furthermore, models were used to evaluate in silico single gene knockouts under DMM and complex medium. Cluster analysis of these results shows that ST-CNB440, and SP-CNR114 models are more similar when considering predicted essential genes. CONCLUSIONS Models were built for each of the three currently identified Salinispora species, and a core model representing the conserved metabolic capabilities of Salinispora was constructed. Models will allow in silico metabolism studies of Salinispora strains, and help researchers to guide and increase the production of specialised metabolites. Also, models can be used as templates to build GSMs models of closely related organisms with high biotechnology potential.
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Affiliation(s)
- Carolina A. Contador
- Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Beauchef 851, Santiago, Chile
- Centre for Soybean Research, State Key Laboratory of Agrobiotechnology, Shatin, Hong Kong
- School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Vida Rodríguez
- Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Beauchef 851, Santiago, Chile
| | - Barbara A. Andrews
- Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Beauchef 851, Santiago, Chile
| | - Juan A. Asenjo
- Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Beauchef 851, Santiago, Chile
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Dinh HV, King ZA, Palsson BO, Feist AM. Identification of growth-coupled production strains considering protein costs and kinetic variability. Metab Eng Commun 2018; 7:e00080. [PMID: 30370222 PMCID: PMC6199775 DOI: 10.1016/j.mec.2018.e00080] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 09/25/2018] [Accepted: 10/07/2018] [Indexed: 12/13/2022] Open
Abstract
Conversion of renewable biomass to useful molecules in microbial cell factories can be approached in a rational and systematic manner using constraint-based reconstruction and analysis. Filtering for high confidence in silico designs is critical because in vivo construction and testing of strains is expensive and time consuming. As such, a workflow was devised to analyze the robustness of growth-coupled production when considering the biosynthetic costs of the proteome and variability in enzyme kinetic parameters using a genome-scale model of metabolism and gene expression (ME-model). A collection of 2632 unfiltered knockout designs in Escherichia coli was evaluated by the workflow. A ME-model was used in the workflow to test the designs' growth-coupled production in addition to a less complex genome-scale metabolic model (M-model). The workflow identified 634 M-model growth-coupled designs which met the filtering criteria and 42 robust designs, which met growth-coupled production criteria using both M and ME-models. Knockouts were found to follow a pattern of controlling intermediate metabolite consumption such as pyruvate consumption and high flux subsystems such as glycolysis. Kinetic parameter sampling using the ME-model revealed how enzyme efficiency and pathway tradeoffs can affect growth-coupled production phenotypes.
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Affiliation(s)
- Hoang V. Dinh
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
| | - Zachary A. King
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, DK-2800 Kongens, Lyngby, Denmark
| | - Adam M. Feist
- Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive #0412, La Jolla, CA 92093-0412, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, DK-2800 Kongens, Lyngby, Denmark
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