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Passi A, Tibocha-Bonilla JD, Kumar M, Tec-Campos D, Zengler K, Zuniga C. Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data. Metabolites 2021; 12:14. [PMID: 35050136 PMCID: PMC8778254 DOI: 10.3390/metabo12010014] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 11/16/2022] Open
Abstract
Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.
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Affiliation(s)
- Anurag Passi
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA;
| | - Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
| | - Diego Tec-Campos
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Facultad de Ingeniería Química, Campus de Ciencias Exactas e Ingenierías, Universidad Autónoma de Yucatán, Merida 97203, Yucatan, Mexico
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA
- Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0403, USA
| | - Cristal Zuniga
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760, USA; (A.P.); (M.K.); (D.T.-C.); (K.Z.)
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Dhakar K, Zarecki R, van Bommel D, Knossow N, Medina S, Öztürk B, Aly R, Eizenberg H, Ronen Z, Freilich S. Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling. Front Bioeng Biotechnol 2021; 9:602464. [PMID: 33937210 PMCID: PMC8084104 DOI: 10.3389/fbioe.2021.602464] [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: 09/03/2020] [Accepted: 02/22/2021] [Indexed: 01/16/2023] Open
Abstract
Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.
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Affiliation(s)
- Kusum Dhakar
- Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishai, Israel.,Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beersheba, Israel
| | - Raphy Zarecki
- Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishai, Israel.,Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beersheba, Israel
| | - Daniella van Bommel
- lbert Katz School for Desert Studies Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beersheba, Israel
| | - Nadav Knossow
- Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beersheba, Israel
| | - Shlomit Medina
- Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishai, Israel
| | - Basak Öztürk
- Junior Research Group Microbial Biotechnology, Leibniz Institute DSMZ, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
| | - Radi Aly
- Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishai, Israel
| | - Hanan Eizenberg
- Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishai, Israel
| | - Zeev Ronen
- Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beersheba, Israel
| | - Shiri Freilich
- Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishai, Israel
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Botero D, Monk J, Rodríguez Cubillos MJ, Rodríguez Cubillos A, Restrepo M, Bernal-Galeano V, Reyes A, González Barrios A, Palsson BØ, Restrepo S, Bernal A. Genome-Scale Metabolic Model of Xanthomonas phaseoli pv. manihotis: An Approach to Elucidate Pathogenicity at the Metabolic Level. Front Genet 2020; 11:837. [PMID: 32849823 PMCID: PMC7432306 DOI: 10.3389/fgene.2020.00837] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 07/10/2020] [Indexed: 01/05/2023] Open
Abstract
Xanthomonas phaseoli pv. manihotis (Xpm) is the causal agent of cassava bacterial blight, the most important bacterial disease in this crop. There is a paucity of knowledge about the metabolism of Xanthomonas and its relevance in the pathogenic process, with the exception of the elucidation of the xanthan biosynthesis route. Here we report the reconstruction of the genome-scale model of Xpm metabolism and the insights it provides into plant-pathogen interactions. The model, iXpm1556, displayed 1,556 reactions, 1,527 compounds, and 890 genes. Metabolic maps of central amino acid and carbohydrate metabolism, as well as xanthan biosynthesis of Xpm, were reconstructed using Escher (https://escher.github.io/) to guide the curation process and for further analyses. The model was constrained using the RNA-seq data of a mutant of Xpm for quorum sensing (QS), and these data were used to construct context-specific models (CSMs) of the metabolism of the two strains (wild type and QS mutant). The CSMs and flux balance analysis were used to get insights into pathogenicity, xanthan biosynthesis, and QS mechanisms. Between the CSMs, 653 reactions were shared; unique reactions belong to purine, pyrimidine, and amino acid metabolism. Alternative objective functions were used to demonstrate a trade-off between xanthan biosynthesis and growth and the re-allocation of resources in the process of biosynthesis. Important features altered by QS included carbohydrate metabolism, NAD(P)+ balance, and fatty acid elongation. In this work, we modeled the xanthan biosynthesis and the QS process and their impact on the metabolism of the bacterium. This model will be useful for researchers studying host-pathogen interactions and will provide insights into the mechanisms of infection used by this and other Xanthomonas species.
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Affiliation(s)
- David Botero
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
- Max Planck Tandem Group in Computational Biology, Universidad de Los Andes, Bogotá, Colombia
- Grupo de Biología Computacional y Ecología Microbiana, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Jonathan Monk
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
| | - María Juliana Rodríguez Cubillos
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
| | | | - Mariana Restrepo
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Vivian Bernal-Galeano
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Alejandro Reyes
- Max Planck Tandem Group in Computational Biology, Universidad de Los Andes, Bogotá, Colombia
- Grupo de Biología Computacional y Ecología Microbiana, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Andrés González Barrios
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Bernhard Ø. Palsson
- Systems Biology Research Group, Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
| | - Silvia Restrepo
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Chemical and Food Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Adriana Bernal
- Laboratory of Molecular Interactions of Agricultural Microbes, LIMMA, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
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Zoledowska S, Presta L, Fondi M, Decorosi F, Giovannetti L, Mengoni A, Lojkowska E. Metabolic Modeling of Pectobacterium parmentieri SCC3193 Provides Insights into Metabolic Pathways of Plant Pathogenic Bacteria. Microorganisms 2019; 7:E101. [PMID: 30959803 PMCID: PMC6518042 DOI: 10.3390/microorganisms7040101] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 03/26/2019] [Accepted: 04/02/2019] [Indexed: 12/17/2022] Open
Abstract
Understanding plant⁻microbe interactions is crucial for improving plants' productivity and protection. Constraint-based metabolic modeling is one of the possible ways to investigate the bacterial adaptation to different ecological niches and may give insights into the metabolic versatility of plant pathogenic bacteria. We reconstructed a raw metabolic model of the emerging plant pathogenic bacterium Pectobacterium parmentieri SCC3193 with the use of KBase. The model was curated by using inParanoind and phenotypic data generated with the use of the OmniLog system. Metabolic modeling was performed through COBRApy Toolbox v. 0.10.1. The curated metabolic model of P. parmentieri SCC3193 is highly reliable, as in silico obtained results overlapped up to 91% with experimental data on carbon utilization phenotypes. By mean of flux balance analysis (FBA), we predicted the metabolic adaptation of P. parmentieri SCC3193 to two different ecological niches, relevant for the persistence and plant colonization by this bacterium: soil and the rhizosphere. We performed in silico gene deletions to predict the set of essential core genes for this bacterium to grow in such environments. We anticipate that our metabolic model will be a valuable element for defining a set of metabolic targets to control infection and spreading of this plant pathogen.
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Affiliation(s)
- Sabina Zoledowska
- Department of Biotechnology, Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, 58 Abrahama Street, 80-307 Gdansk, Poland.
| | - Luana Presta
- Department of Biology, University of Florence, via Madonna del Piano 6, Sesto Fiorentino, 50019 Florence, Italy.
| | - Marco Fondi
- Department of Biology, University of Florence, via Madonna del Piano 6, Sesto Fiorentino, 50019 Florence, Italy.
| | - Francesca Decorosi
- Department of Agri-food Production and Environmental Sciences, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy.
| | - Luciana Giovannetti
- Department of Agri-food Production and Environmental Sciences, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy.
| | - Alessio Mengoni
- Department of Biology, University of Florence, via Madonna del Piano 6, Sesto Fiorentino, 50019 Florence, Italy.
| | - Ewa Lojkowska
- Department of Biotechnology, Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, 58 Abrahama Street, 80-307 Gdansk, Poland.
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Sotelo-Boyás ME, Correa-Pacheco Z, Corona-Rangel ML, Villanueva-Arce R, Bautista-Baños S. Cellular alterations in Pectobacterium carotovorum treated with nanostructured formulations during the incubation time. Arch Microbiol 2019; 201:615-622. [DOI: 10.1007/s00203-019-01628-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 01/16/2019] [Accepted: 02/04/2019] [Indexed: 11/30/2022]
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Botero D, Alvarado C, Bernal A, Danies G, Restrepo S. Network Analyses in Plant Pathogens. Front Microbiol 2018; 9:35. [PMID: 29441045 PMCID: PMC5797656 DOI: 10.3389/fmicb.2018.00035] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Accepted: 01/09/2018] [Indexed: 11/14/2022] Open
Abstract
Even in the age of big data in Biology, studying the connections between the biological processes and the molecular mechanisms behind them is a challenging task. Systems biology arose as a transversal discipline between biology, chemistry, computer science, mathematics, and physics to facilitate the elucidation of such connections. A scenario, where the application of systems biology constitutes a very powerful tool, is the study of interactions between hosts and pathogens using network approaches. Interactions between pathogenic bacteria and their hosts, both in agricultural and human health contexts are of great interest to researchers worldwide. Large amounts of data have been generated in the last few years within this area of research. However, studies have been relatively limited to simple interactions. This has left great amounts of data that remain to be utilized. Here, we review the main techniques in network analysis and their complementary experimental assays used to investigate bacterial-plant interactions. Other host-pathogen interactions are presented in those cases where few or no examples of plant pathogens exist. Furthermore, we present key results that have been obtained with these techniques and how these can help in the design of new strategies to control bacterial pathogens. The review comprises metabolic simulation, protein-protein interactions, regulatory control of gene expression, host-pathogen modeling, and genome evolution in bacteria. The aim of this review is to offer scientists working on plant-pathogen interactions basic concepts around network biology, as well as an array of techniques that will be useful for a better and more complete interpretation of their data.
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Affiliation(s)
- David Botero
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Diseño de Productos y Procesos, Department of Chemical Engineering, Universidad de Los Andes, Bogotá, Colombia.,Grupo de Biología Computacional y Ecología Microbiana, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Camilo Alvarado
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Adriana Bernal
- Laboratory of Molecular Interactions of Agricultural Microbes, LIMMA, Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
| | - Giovanna Danies
- Department of Design, Universidad de Los Andes, Bogotá, Colombia
| | - Silvia Restrepo
- Laboratory of Mycology and Plant Pathology (LAMFU), Department of Biological Sciences, Universidad de Los Andes, Bogotá, Colombia
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Schatschneider S, Schneider J, Blom J, Létisse F, Niehaus K, Goesmann A, Vorhölter FJ. Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris. Microbiology (Reading) 2017; 163:1117-1144. [DOI: 10.1099/mic.0.000473] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Sarah Schatschneider
- Abteilung für Proteom und Metabolomforschung, Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Bielefeld, Germany
- Present address: Evonik Nutrition and Care GmbH, Kantstr. 2, 33790 Halle-Künsebeck, Germany
| | - Jessica Schneider
- Bioinformatics Resource Facility, Centrum für Biotechnologie, Universität Bielefeld, Germany
- Present address: Evonik Nutrition and Care GmbH, Kantstr. 2, 33790 Halle-Künsebeck, Germany
| | - Jochen Blom
- Bioinformatics and Systems Biology, Justus-Liebig-University Gießen, Germany
| | - Fabien Létisse
- LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France
| | - Karsten Niehaus
- Abteilung für Proteom und Metabolomforschung, Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Bielefeld, Germany
| | - Alexander Goesmann
- Bioinformatics and Systems Biology, Justus-Liebig-University Gießen, Germany
| | - Frank-Jörg Vorhölter
- Institut für Genomforschung und Systembiologie, Centrum für Biotechnology (CeBiTec), Universität Bielefeld, Bielefeld, Germany
- Present address: MVZ Dr. Eberhard & Partner Dortmund, Dortmund, Germany
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Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates. Metab Eng 2016; 38:251-263. [PMID: 27637318 DOI: 10.1016/j.ymben.2016.09.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Revised: 06/07/2016] [Accepted: 09/11/2016] [Indexed: 01/22/2023]
Abstract
Pseudomonas aeruginosa is a metabolically versatile wide-ranging opportunistic pathogen. In humans P. aeruginosa causes infections of the skin, urinary tract, blood, and the lungs of Cystic Fibrosis patients. In addition, P. aeruginosa's broad environmental distribution, relatedness to biotechnologically useful species, and ability to form biofilms have made it the focus of considerable interest. We used 13C metabolic flux analysis (MFA) and flux balance analysis to understand energy and redox production and consumption and to explore the metabolic phenotypes of one reference strain and five strains isolated from the lungs of cystic fibrosis patients. Our results highlight the importance of the oxidative pentose phosphate and Entner-Doudoroff pathways in P. aeruginosa growth. Among clinical strains we report two divergent metabolic strategies and identify changes between genetically related strains that have emerged during a chronic infection of the same patient. MFA revealed that the magnitude of fluxes through the glyoxylate cycle correlates with growth rates.
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Babaei P, Marashi SA, Asad S. Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501. MOLECULAR BIOSYSTEMS 2016; 11:3022-32. [PMID: 26302703 DOI: 10.1039/c5mb00086f] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Pseudomonas stutzeri A1501 is an endophytic bacterium capable of nitrogen fixation. This strain has been isolated from the rice rhizosphere and provides the plant with fixed nitrogen and phytohormones. These interesting features encouraged us to study the metabolism of this microorganism at the systems-level. In this work, we present the first genome-scale metabolic model (iPB890) for P. stutzeri, involving 890 genes, 1135 reactions, and 813 metabolites. A combination of automatic and manual approaches was used in the reconstruction process. Briefly, using the metabolic networks of Pseudomonas aeruginosa and Pseudomonas putida as templates, a draft metabolic network of P. stutzeri was reconstructed. Then, the draft network was driven through an iterative and curative process of gap filling. In the next step, the model was evaluated using different experimental data such as specific growth rate, Biolog substrate utilization data and other experimental observations. In most of the evaluation cases, the model was successful in correctly predicting the cellular phenotypes. Thus, we posit that the iPB890 model serves as a suitable platform to explore the metabolism of P. stutzeri.
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Affiliation(s)
- Parizad Babaei
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
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