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González-Arrué N, Inostroza I, Conejeros R, Rivas-Astroza M. Phenotype-specific estimation of metabolic fluxes using gene expression data. iScience 2023; 26:106201. [PMID: 36915687 PMCID: PMC10006673 DOI: 10.1016/j.isci.2023.106201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.
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Affiliation(s)
- Nicolás González-Arrué
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Isidora Inostroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Raúl Conejeros
- Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería Bioquímica, Valparaíso, 2362803, Chile
| | - Marcelo Rivas-Astroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
- Corresponding author
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2
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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3
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Vijayakumar S, Magazzù G, Moon P, Occhipinti A, Angione C. A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling. Methods Mol Biol 2022; 2399:87-122. [PMID: 35604554 DOI: 10.1007/978-1-0716-1831-8_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .
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Affiliation(s)
- Supreeta Vijayakumar
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Giuseppe Magazzù
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Pradip Moon
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Annalisa Occhipinti
- Computational Systems Biology and Data Analytics Research Group, Middlebrough, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK
| | - Claudio Angione
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK.
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK.
- Healthcare Innovation Centre, Teesside University, Middlesbrough, UK.
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4
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Kuriya Y, Inoue M, Yamamoto M, Murata M, Araki M. Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering. Biotechnol J 2021; 16:e2000443. [PMID: 34516717 DOI: 10.1002/biot.202000443] [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: 11/10/2020] [Revised: 09/01/2021] [Accepted: 09/10/2021] [Indexed: 11/10/2022]
Abstract
Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.
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Affiliation(s)
- Yuki Kuriya
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
| | - Mai Inoue
- Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan
| | - Masaki Yamamoto
- Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan
| | - Masahiro Murata
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
| | - Michihiro Araki
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan.,Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan.,Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, Japan
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5
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Chung CH, Lin DW, Eames A, Chandrasekaran S. Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms. Metabolites 2021; 11:606. [PMID: 34564422 PMCID: PMC8470976 DOI: 10.3390/metabo11090606] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 12/18/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.
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Affiliation(s)
- Carolina H. Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Da-Wei Lin
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Alec Eames
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (C.H.C.); (A.E.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA;
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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6
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Ritis D, Boulougouris GC. On the hierarchical design of biochemical-based digital computations. Comput Biol Med 2021; 135:104630. [PMID: 34311298 DOI: 10.1016/j.compbiomed.2021.104630] [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: 03/17/2021] [Revised: 07/02/2021] [Accepted: 07/02/2021] [Indexed: 11/18/2022]
Abstract
The understanding of the biochemical processes underpinning various biological systems has significantly increased in recent decades and has even prompted reverse engineering of certain of life's more complex processes. The most prominent example is modern computers designed to mimic neuron activity. This work forms part of growing endeavors to return advances in the theory of computation and electronics to biology. In this context, we present a set of requirements sufficient for the design of biochemical analogs of modern electronics in a hierarchical, modular fashion that mimics the design of modern computational devices. This theoretical approach is based on a simple enzymatic analog of the transistor and supported by numerical simulations of biochemical models of enzymatic networks equivalent to complex, and modular, interconnecting electronic circuitry (including clocks, Flip-Flops, adders, decoders, and multiplexers). Furthermore, the proposed approach has been implemented in the form of a Python library capable of creating and testing models of complex bio-analog digital computations based on the execution of an elementary universal logic gate. In tribute to Claude Shannon, our biochemical network materializes his example of a "password" recognition that moves the language of the modern theory of automata beyond combinatorial logic and towards sequential logic.
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Affiliation(s)
- Dimitrios Ritis
- Laboratory of Computational Physical Chemistry, Department of Molecular Biology and Genetics, Democritus University of Thrace, Greece
| | - Georgios C Boulougouris
- Laboratory of Computational Physical Chemistry, Department of Molecular Biology and Genetics, Democritus University of Thrace, Greece.
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7
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la-Rosa JDPD, García-Ramírez MA, Gschaedler-Mathis AC, Gómez-Guzmán AI, Solís-Pacheco JR, González-Reynoso O. Estimation of metabolic fluxes distribution in Saccharomyces cerevisiae during the production of volatile compounds of Tequila. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5094-5113. [PMID: 34517479 DOI: 10.3934/mbe.2021259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A stoichiometric model for Saccharomyces cerevisiae is reconstructed to analyze the continuous fermentation process of agave juice in Tequila production. The metabolic model contains 94 metabolites and 117 biochemical reactions. From the above set of reactions, 93 of them are linked to internal biochemical reactions and 24 are related to transport fluxes between the medium and the cell. The central metabolism of S. cerevisiae includes the synthesis for 20 amino-acids, carbohydrates, lipids, DNA and RNA. Using flux balance analysis (FBA), different physiological states of S. cerevisiae are shown during the fermentative process; these states are compared with experimental data under different dilution rates (0.04-0.12 h$ ^{-1} $). Moreover, the model performs anabolic and catabolic biochemical reactions for the production of higher alcohols. The importance of the Saccharomyces cerevisiae genomic model in the area of alcoholic beverage fermentation is due to the fact that it allows to estimate the metabolic fluxes during the beverage fermentation process and a physiology state of the microorganism.
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Affiliation(s)
| | - Mario Alberto García-Ramírez
- Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán # 1451, C.P. 44430, Guadalajara, Jalisco, México
| | | | | | - Josué R Solís-Pacheco
- Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán # 1451, C.P. 44430, Guadalajara, Jalisco, México
| | - Orfil González-Reynoso
- Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán # 1451, C.P. 44430, Guadalajara, Jalisco, México
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8
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Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava. Sci Rep 2021; 11:8758. [PMID: 33888810 PMCID: PMC8062692 DOI: 10.1038/s41598-021-88129-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 04/08/2021] [Indexed: 02/02/2023] Open
Abstract
The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.
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9
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Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism. Sci Rep 2021; 11:4787. [PMID: 33637852 PMCID: PMC7910480 DOI: 10.1038/s41598-021-84114-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023] Open
Abstract
Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 \documentclass[12pt]{minimal}
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\begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.
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10
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Dahal S, Zhao J, Yang L. Genome-scale Modeling of Metabolism and Macromolecular Expression and Their Applications. BIOTECHNOL BIOPROC E 2021. [DOI: 10.1007/s12257-020-0061-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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11
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A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation. Biochem Soc Trans 2020; 48:1889-1903. [DOI: 10.1042/bst20190840] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/16/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.
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12
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Dahal S, Yurkovich JT, Xu H, Palsson BO, Yang L. Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models. Proteomics 2020; 20:e1900282. [PMID: 32579720 PMCID: PMC7501203 DOI: 10.1002/pmic.201900282] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 06/13/2020] [Indexed: 12/18/2022]
Abstract
Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.
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Affiliation(s)
- Sanjeev Dahal
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | | | - Hao Xu
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Laurence Yang
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
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13
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Gale GAR, Schiavon Osorio AA, Mills LA, Wang B, Lea-Smith DJ, McCormick AJ. Emerging Species and Genome Editing Tools: Future Prospects in Cyanobacterial Synthetic Biology. Microorganisms 2019; 7:E409. [PMID: 31569579 PMCID: PMC6843473 DOI: 10.3390/microorganisms7100409] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 09/22/2019] [Accepted: 09/24/2019] [Indexed: 12/19/2022] Open
Abstract
Recent advances in synthetic biology and an emerging algal biotechnology market have spurred a prolific increase in the availability of molecular tools for cyanobacterial research. Nevertheless, work to date has focused primarily on only a small subset of model species, which arguably limits fundamental discovery and applied research towards wider commercialisation. Here, we review the requirements for uptake of new strains, including several recently characterised fast-growing species and promising non-model species. Furthermore, we discuss the potential applications of new techniques available for transformation, genetic engineering and regulation, including an up-to-date appraisal of current Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated protein (CRISPR/Cas) and CRISPR interference (CRISPRi) research in cyanobacteria. We also provide an overview of several exciting molecular tools that could be ported to cyanobacteria for more advanced metabolic engineering approaches (e.g., genetic circuit design). Lastly, we introduce a forthcoming mutant library for the model species Synechocystis sp. PCC 6803 that promises to provide a further powerful resource for the cyanobacterial research community.
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Affiliation(s)
- Grant A R Gale
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FF, UK.
| | - Alejandra A Schiavon Osorio
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
| | - Lauren A Mills
- School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
| | - Baojun Wang
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3FF, UK.
| | - David J Lea-Smith
- School of Biological Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
| | - Alistair J McCormick
- Institute of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
- Centre for Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.
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14
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Pusa T, Ferrarini MG, Andrade R, Mary A, Marchetti-Spaccamela A, Stougie L, Sagot MF. MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network. Bioinformatics 2019; 36:514-523. [PMID: 31504164 PMCID: PMC9883724 DOI: 10.1093/bioinformatics/btz584] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 07/16/2019] [Accepted: 08/19/2019] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. RESULTS In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. AVAILABILITY AND IMPLEMENTATION github.com/htpusa/moomin. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Taneli Pusa
- To whom correspondence should be addressed. or
| | - Mariana Galvão Ferrarini
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne 69622, France,Univ Lyon, INSA-Lyon, INRA, BF2i, UMR0203, F-69621, Villeurbanne 69622, France
| | - Ricardo Andrade
- INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France,Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne 69622, France
| | - Arnaud Mary
- INRIA Grenoble Rhône-Alpes, Montbonnot-Saint-Martin 38334, France,Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne 69622, France
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15
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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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16
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Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019; 15:e1007084. [PMID: 31295267 PMCID: PMC6622478 DOI: 10.1371/journal.pcbi.1007084] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
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Affiliation(s)
- Guido Zampieri
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
- Healthcare Innovation Centre, Teesside University, Middlesbrough, United Kingdom
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17
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Tran VDT, Moretti S, Coste AT, Amorim-Vaz S, Sanglard D, Pagni M. Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis. Bioinformatics 2019; 35:2258-2266. [PMID: 30445518 PMCID: PMC6596900 DOI: 10.1093/bioinformatics/bty929] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 10/16/2018] [Accepted: 11/09/2018] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging. RESULTS We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. AVAILABILITY AND IMPLEMENTATION The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Van Du T Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Sébastien Moretti
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
- Evolutionary Bioinformatics Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Alix T Coste
- Institute of Microbiology, University Hospital and University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Sara Amorim-Vaz
- Institute of Microbiology, University Hospital and University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Dominique Sanglard
- Institute of Microbiology, University Hospital and University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Marco Pagni
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
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18
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Grimbs A, Klosik DF, Bornholdt S, Hütt MT. A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli. PLoS Comput Biol 2019; 15:e1006962. [PMID: 31050661 PMCID: PMC6519848 DOI: 10.1371/journal.pcbi.1006962] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 05/15/2019] [Accepted: 03/18/2019] [Indexed: 11/19/2022] Open
Abstract
Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. Since the pioneering work of Covert and co-workers as well as Shlomi and co-workers it is debated, how regulation and metabolism synergistically characterize a coherent cellular state. The first approaches started from metabolic models, which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes, from metabolic processes via protein modification to a variety of regulatory events. Besides transcriptional regulation, we also take into account regulation of translation, enzyme activities and reactions. Our network model provides novel topological characterizations of system components based on their positions in the network. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks) instead of two. This new three-domain representation reveals the structural centrality of components with known high functional relevance. This integrated network can serve as a platform for understanding coherent cellular states as active subnetworks and to elucidate crossover effects between metabolism and gene regulation.
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Affiliation(s)
- Anne Grimbs
- Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany
| | - David F. Klosik
- Institute for Theoretical Physics, University of Bremen, Bremen, Germany
| | - Stefan Bornholdt
- Institute for Theoretical Physics, University of Bremen, Bremen, Germany
| | - Marc-Thorsten Hütt
- Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany
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19
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Shen F, Sun R, Yao J, Li J, Liu Q, Price ND, Liu C, Wang Z. OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling. PLoS Comput Biol 2019; 15:e1006835. [PMID: 30849073 PMCID: PMC6426274 DOI: 10.1371/journal.pcbi.1006835] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 03/20/2019] [Accepted: 02/01/2019] [Indexed: 02/07/2023] Open
Abstract
The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.
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Affiliation(s)
- Fangzhou Shen
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Renliang Sun
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Yao
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jian Li
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Qian Liu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Chenguang Liu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhuo Wang
- Bio-X Institutes, Key laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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20
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Santos-Merino M, Singh AK, Ducat DC. New Applications of Synthetic Biology Tools for Cyanobacterial Metabolic Engineering. Front Bioeng Biotechnol 2019; 7:33. [PMID: 30873404 PMCID: PMC6400836 DOI: 10.3389/fbioe.2019.00033] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/05/2019] [Indexed: 01/25/2023] Open
Abstract
Cyanobacteria are promising microorganisms for sustainable biotechnologies, yet unlocking their potential requires radical re-engineering and application of cutting-edge synthetic biology techniques. In recent years, the available devices and strategies for modifying cyanobacteria have been increasing, including advances in the design of genetic promoters, ribosome binding sites, riboswitches, reporter proteins, modular vector systems, and markerless selection systems. Because of these new toolkits, cyanobacteria have been successfully engineered to express heterologous pathways for the production of a wide variety of valuable compounds. Cyanobacterial strains with the potential to be used in real-world applications will require the refinement of genetic circuits used to express the heterologous pathways and development of accurate models that predict how these pathways can be best integrated into the larger cellular metabolic network. Herein, we review advances that have been made to translate synthetic biology tools into cyanobacterial model organisms and summarize experimental and in silico strategies that have been employed to increase their bioproduction potential. Despite the advances in synthetic biology and metabolic engineering during the last years, it is clear that still further improvements are required if cyanobacteria are to be competitive with heterotrophic microorganisms for the bioproduction of added-value compounds.
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Affiliation(s)
- María Santos-Merino
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, United States
| | - Amit K. Singh
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, United States
| | - Daniel C. Ducat
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
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21
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Barbosa S, Niebel B, Wolf S, Mauch K, Takors R. A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints. Biosystems 2018; 174:37-48. [PMID: 30312740 DOI: 10.1016/j.biosystems.2018.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 10/05/2018] [Accepted: 10/08/2018] [Indexed: 02/07/2023]
Abstract
The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.
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Affiliation(s)
- Sara Barbosa
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany.
| | - Bastian Niebel
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Sebastian Wolf
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Klaus Mauch
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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22
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Kim H, Kim S, Yoon SH. Metabolic network reconstruction and phenome analysis of the industrial microbe, Escherichia coli BL21(DE3). PLoS One 2018; 13:e0204375. [PMID: 30240424 PMCID: PMC6150544 DOI: 10.1371/journal.pone.0204375] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 09/06/2018] [Indexed: 01/25/2023] Open
Abstract
Escherichia coli BL21(DE3) is an industrial model microbe for the mass-production of bioproducts such as biofuels, biorefineries, and recombinant proteins. However, despite its important role in scientific research and biotechnological applications, a high-quality metabolic network model for metabolic engineering is yet to be developed. Here, we present the comprehensive metabolic network model of E. coli BL21(DE3), named iHK1487, based on the latest genome reannotation and phenome analysis. The metabolic model consists of 1,164 unique metabolites, 2,701 metabolic reactions, and 1,487 genes. The model was validated and improved by comparing the simulation results with phenome data from phenotype microarray tests. Previous transcriptome profile data was incorporated during model reconstruction, and flux prediction was simulated using the model. iHK1487 was simulated to explore the metabolic features of BL21(DE3) such as broad spectrum amino acid utilization and enhanced flux through the upper glycolytic pathway and TCA cycle. iHK1487 will contribute to systematic understanding of cellular physiology and metabolism of E. coli BL21(DE3) and highlight its biotechnological applications.
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Affiliation(s)
- Hanseol Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea
| | - Sinyeon Kim
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea
| | - Sung Ho Yoon
- Department of Bioscience and Biotechnology, Konkuk University, Seoul, Republic of Korea
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23
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Dunphy LJ, Papin JA. Biomedical applications of genome-scale metabolic network reconstructions of human pathogens. Curr Opin Biotechnol 2018; 51:70-79. [PMID: 29223465 PMCID: PMC5991985 DOI: 10.1016/j.copbio.2017.11.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/22/2017] [Accepted: 11/24/2017] [Indexed: 12/14/2022]
Abstract
The growing global threat of antibiotic resistant human pathogens has coincided with improved methods for developing and using genome-scale metabolic network reconstructions. Consequently, there has been an increase in the number of high-quality reconstructions of relevant human and zoonotic pathogens. Novel biomedical applications of pathogen reconstructions focus on three key aspects of pathogen behavior: the evolution of antibiotic resistance, virulence factor production, and host-pathogen interactions. New methods using these reconstructions aim to improve understanding of microbe pathogenicity and guide the development of new therapeutic strategies. This review summarizes the latest ways that genome-scale metabolic network reconstructions have been used to study human pathogens and suggests future applications with the potential to mitigate infectious disease.
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Affiliation(s)
- Laura J Dunphy
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA; Department of Medicine, Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22903, USA.
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24
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Xu N, Ye C, Liu L. Genome-scale biological models for industrial microbial systems. Appl Microbiol Biotechnol 2018; 102:3439-3451. [PMID: 29497793 DOI: 10.1007/s00253-018-8803-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 01/08/2023]
Abstract
The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.
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Affiliation(s)
- Nan Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,College of Bioscience and Biotechnology, Yangzhou University, Yangzhou, Jiangsu, 225009, China.,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China
| | - Chao Ye
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China. .,The Laboratory of Food Microbial-Manufacturing Engineering, Jiangnan University, Wuxi, 214122, China.
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25
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Fürtauer L, Weiszmann J, Weckwerth W, Nägele T. Mathematical Modeling Approaches in Plant Metabolomics. Methods Mol Biol 2018; 1778:329-347. [PMID: 29761450 DOI: 10.1007/978-1-4939-7819-9_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.
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Affiliation(s)
- Lisa Fürtauer
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Jakob Weiszmann
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - Thomas Nägele
- Department of Ecogenomics and Systems Biology, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria.
- Department Biology I, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Austria.
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26
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Vijayakumar S, Conway M, Lió P, Angione C. Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives. Methods Mol Biol 2018; 1716:389-408. [PMID: 29222764 DOI: 10.1007/978-1-4939-7528-0_18] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Genome-scale metabolic models are valuable tools for assessing the metabolic potential of living organisms. Being downstream of gene expression, metabolism is increasingly being used as an indicator of the phenotypic outcome for drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimization using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g., antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.
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Affiliation(s)
- Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK
| | - Max Conway
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Pietro Lió
- Computer Laboratory, University of Cambridge, Cambridge, UK
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK.
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27
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Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica. NPJ Syst Biol Appl 2017; 3:21. [PMID: 28955503 PMCID: PMC5554221 DOI: 10.1038/s41540-017-0024-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 07/07/2017] [Accepted: 07/13/2017] [Indexed: 12/14/2022] Open
Abstract
Complex phenotypes, such as lipid accumulation, result from cooperativity between regulators and the integration of multiscale information. However, the elucidation of such regulatory programs by experimental approaches may be challenging, particularly in context-specific conditions. In particular, we know very little about the regulators of lipid accumulation in the oleaginous yeast of industrial interest Yarrowia lipolytica. This lack of knowledge limits the development of this yeast as an industrial platform, due to the time-consuming and costly laboratory efforts required to design strains with the desired phenotypes. In this study, we aimed to identify context-specific regulators and mechanisms, to guide explorations of the regulation of lipid accumulation in Y. lipolytica. Using gene regulatory network inference, and considering the expression of 6539 genes over 26 time points from GSE35447 for biolipid production and a list of 151 transcription factors, we reconstructed a gene regulatory network comprising 111 transcription factors, 4451 target genes and 17048 regulatory interactions (YL-GRN-1) supported by evidence of protein-protein interactions. This study, based on network interrogation and wet laboratory validation (a) highlights the relevance of our proposed measure, the transcription factors influence, for identifying phases corresponding to changes in physiological state without prior knowledge (b) suggests new potential regulators and drivers of lipid accumulation and
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28
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Chiappino-Pepe A, Pandey V, Ataman M, Hatzimanikatis V. Integration of metabolic, regulatory and signaling networks towards analysis of perturbation and dynamic responses. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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29
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Kim M, Sun G, Lee DY, Kim BG. BeReTa: a systematic method for identifying target transcriptional regulators to enhance microbial production of chemicals. Bioinformatics 2016; 33:87-94. [PMID: 27605107 DOI: 10.1093/bioinformatics/btw557] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 08/04/2016] [Accepted: 08/21/2016] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Modulation of regulatory circuits governing the metabolic processes is a crucial step for developing microbial cell factories. Despite the prevalence of in silico strain design algorithms, most of them are not capable of predicting required modifications in regulatory networks. Although a few algorithms may predict relevant targets for transcriptional regulator (TR) manipulations, they have limited reliability and applicability due to their high dependency on the availability of integrated metabolic/regulatory models. RESULTS We present BeReTa (Beneficial Regulator Targeting), a new algorithm for prioritization of TR manipulation targets, which makes use of unintegrated network models. BeReTa identifies TR manipulation targets by evaluating regulatory strengths of interactions and beneficial effects of reactions, and subsequently assigning beneficial scores for the TRs. We demonstrate that BeReTa can predict both known and novel TR manipulation targets for enhanced production of various chemicals in Escherichia coli Furthermore, through a case study of antibiotics production in Streptomyces coelicolor, we successfully demonstrate its wide applicability to even less-studied organisms. To the best of our knowledge, BeReTa is the first strain design algorithm exclusively designed for predicting TR manipulation targets. AVAILABILITY AND IMPLEMENTATION MATLAB code is available at https://github.com/kms1041/BeReTa (github). CONTACT byungkim@snu.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Minsuk Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
| | - Gwanggyu Sun
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
| | - Dong-Yup Lee
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117576, Singapore.,Bioprocessing Technology Institute; Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore 138668, Singapore
| | - Byung-Gee Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
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Fitó M, Melander O, Martínez JA, Toledo E, Carpéné C, Corella D. Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention. Int J Mol Sci 2016; 17:E1469. [PMID: 27598147 PMCID: PMC5037747 DOI: 10.3390/ijms17091469] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 08/08/2016] [Accepted: 08/26/2016] [Indexed: 12/17/2022] Open
Abstract
Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition.
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Affiliation(s)
- Montserrat Fitó
- Cardiovascular Risk and Nutrition Research (REGICOR Group), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), 08003 Barcelona, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
| | - Olle Melander
- Department of Clinical Sciences, Lund University, 22100 Lund, Sweden.
- Department of Internal Medicine, Skåne University Hospital, 22241 Lund, Sweden.
| | - José Alfredo Martínez
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
- Department of Nutrition and Food Sciences, University of Navarra, 31009 Pamplona, Spain.
| | - Estefanía Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, University of Navarra, 31009 Pamplona, Spain.
| | - Christian Carpéné
- INSERM U1048, Institute of Metabolic and Cardiovascular Diseases (I2MC), Rangueil Hospital, 31442 Toulouse, France.
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), 28029 Madrid, Spain.
- Department of Preventive Medicine and Public Health, University of Valencia, 46010 Valencia, Spain.
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