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Roell G, Schenk C, Anthony WE, Carr RR, Ponukumati A, Kim J, Akhmatskaya E, Foston M, Dantas G, Moon TS, Tang YJ, García Martín H. A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism. ACS Synth Biol 2023; 12:1632-1644. [PMID: 37186551 PMCID: PMC10278598 DOI: 10.1021/acssynbio.2c00618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Indexed: 05/17/2023]
Abstract
Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.
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Affiliation(s)
- Garrett
W. Roell
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Christina Schenk
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
| | - Winston E. Anthony
- The Edison
Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, United States
- Department
of Pathology and Immunology, Washington
University in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
| | - Rhiannon R. Carr
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Aditya Ponukumati
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Joonhoon Kim
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
- DOE
Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Elena Akhmatskaya
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
- IKERBASQUE,
Basque Foundation for Science, Bilbao 48009, Spain
| | - Marcus Foston
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Gautam Dantas
- The Edison
Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, United States
- Department
of Pathology and Immunology, Washington
University in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
- Department
of Biomedical Engineering, Washington University
in St. Louis, St Louis, Missouri 63130, United States
- Department
of Molecular Microbiology, Washington University
in St. Louis School of Medicine, St. Louis, Missouri 63108, United States
- Department
of Pediatrics, Washington University School
of Medicine in St Louis, St Louis, Missouri 63110, United States
| | - Tae Seok Moon
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Yinjie J. Tang
- Department
of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Hector García Martín
- BCAM
- Basque Center for Applied Mathematics, Bilbao 48009, Spain
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Lab, Berkeley, California 94720, United States
- DOE
Joint BioEnergy Institute, Emeryville, California 94608, United States
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Lara AR, Galindo J, Jaén KE, Juárez M, Sigala JC. Physiological Response of Escherichia coli W3110 and BL21 to the Aerobic Expression of Vitreoscilla Hemoglobin. J Microbiol Biotechnol 2020; 30:1592-1596. [PMID: 32699196 PMCID: PMC9728183 DOI: 10.4014/jmb.2004.04030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 07/03/2020] [Accepted: 07/13/2020] [Indexed: 12/15/2022]
Abstract
The aerobic growth and metabolic performance of Escherichia coli strains BL21 and W3110 were studied when the Vitreoscilla hemoglobin (VHb) was constitutively expressed in the chromosome. When VHb was expressed, acetate production decreased in both strains and was nearly eliminated in BL21. Transcriptional levels of the glyoxylate shunt genes decreased in both strains when VHb was expressed. However, higher transcription of the α-ketoglutarate dehydrogenase genes were observed for W3110, while for BL21 transcription levels decreased. VHb expression reduced the transcription of the cytochrome bo3 genes only in BL21. These results are useful for better selecting a production host.
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Affiliation(s)
- Alvaro R. Lara
- Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana-Cuajimalpa, Vasco de Quiroga 4871, Santa Fe, CP 05348, Mexico City, Mexico,Corresponding author Phone: +52-55-58146500 Fax: +52-55-58146500 E-mail:
| | - Janet Galindo
- Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana-Cuajimalpa, Vasco de Quiroga 4871, Santa Fe, CP 05348, Mexico City, Mexico
| | - Karim E. Jaén
- Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana-Cuajimalpa, Vasco de Quiroga 4871, Santa Fe, CP 05348, Mexico City, Mexico
| | - Mariana Juárez
- Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana-Cuajimalpa, Vasco de Quiroga 4871, Santa Fe, CP 05348, Mexico City, Mexico
| | - Juan-Carlos Sigala
- Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana-Cuajimalpa, Vasco de Quiroga 4871, Santa Fe, CP 05348, Mexico City, Mexico
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Jenior ML, Moutinho TJ, Dougherty BV, Papin JA. Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments. PLoS Comput Biol 2020; 16:e1007099. [PMID: 32298268 PMCID: PMC7188308 DOI: 10.1371/journal.pcbi.1007099] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 04/28/2020] [Accepted: 02/24/2020] [Indexed: 11/18/2022] Open
Abstract
The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell’s investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities. Transcriptomic analyses of bacteria have become instrumental to our understanding of their responses to changes in their environment. While traditional analyses have been informative, leveraging these datasets within genome-scale metabolic network reconstructions (GENREs) can provide greatly improved context for shifts in pathway utilization and downstream/upstream ramifications for changes in metabolic regulation. Many previous techniques for GENRE transcript integration have focused on creating maximum consensus with input datasets, but these approaches were recently shown to generate less accurate metabolic predictions than a transcript-agnostic method of flux minimization (pFBA), which identifies the most efficient/economic patterns of metabolism given certain growth constraints. Despite this success, growth conditions are not always easily quantifiable and highlights the need for novel platforms that build from these findings. Our new method, RIPTiDe, combines these concepts and utilizes overall minimization of flux weighted by transcriptomic analysis to identify the most energy efficient pathways to achieve growth that include more highly transcribed enzymes, without previous insight into extracellular conditions. Utilizing a well-studied GENRE from Escherichia coli, we demonstrate that this new approach correctly predicts patterns of metabolism utilizing a variety of both in vitro and in vivo transcriptomes. This platform could be important for revealing context-specific bacterial phenotypes in line with governing principles of adaptive evolution, that drive disease manifestation or interactions between microbes.
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Affiliation(s)
- Matthew L. Jenior
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Thomas J. Moutinho
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Bonnie V. Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
| | - Jason A. Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, Division of Infectious Diseases & International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
- * E-mail:
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Tian M, Reed JL. Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis. Bioinformatics 2019; 34:3882-3888. [PMID: 29878053 DOI: 10.1093/bioinformatics/bty445] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 06/01/2018] [Indexed: 11/13/2022] Open
Abstract
Motivation Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models. Results In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA. Availability and implementation Code is available in the Supplementary data available at Bioinformatics online. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mingyuan Tian
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.,Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Jennifer L Reed
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.,Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
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5
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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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6
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Liu R, Bassalo MC, Zeitoun RI, Gill RT. Genome scale engineering techniques for metabolic engineering. Metab Eng 2015; 32:143-154. [PMID: 26453944 DOI: 10.1016/j.ymben.2015.09.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 08/15/2015] [Accepted: 09/02/2015] [Indexed: 12/18/2022]
Abstract
Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.
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Affiliation(s)
- Rongming Liu
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Marcelo C Bassalo
- Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ramsey I Zeitoun
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ryan T Gill
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
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7
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Kim B, Kim WJ, Kim DI, Lee SY. Applications of genome-scale metabolic network model in metabolic engineering. ACTA ACUST UNITED AC 2015; 42:339-48. [DOI: 10.1007/s10295-014-1554-9] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 11/19/2014] [Indexed: 12/11/2022]
Abstract
Abstract
Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.
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Affiliation(s)
- Byoungjin Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Won Jun Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Dong In Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Sang Yup Lee
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
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8
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Kim MK, Lun DS. Methods for integration of transcriptomic data in genome-scale metabolic models. Comput Struct Biotechnol J 2014; 11:59-65. [PMID: 25379144 PMCID: PMC4212280 DOI: 10.1016/j.csbj.2014.08.009] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Several computational methods have been developed that integrate transcriptomic data with genome-scale metabolic reconstructions to infer condition-specific system-wide intracellular metabolic flux distributions. In this mini-review, we describe each of these methods published to date with categorizing them based on four different grouping criteria (requirement for multiple gene expression datasets as input, requirement for a threshold to define a gene's high and low expression, requirement for a priori assumption of an appropriate objective function, and validation of predicted fluxes directly against measured intracellular fluxes). Then, we recommend which group of methods would be more suitable from a practical perspective.
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Affiliation(s)
- Min Kyung Kim
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Desmond S Lun
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA ; Phenomics and Bioinformatics Research Centre and School of Mathematics and Statistics, University of South Australia, Mawson Lakes, SA 5095, Australia
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9
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Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 2014; 10:e1003580. [PMID: 24762745 PMCID: PMC3998872 DOI: 10.1371/journal.pcbi.1003580] [Citation(s) in RCA: 264] [Impact Index Per Article: 26.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2013] [Accepted: 03/05/2014] [Indexed: 11/19/2022] Open
Abstract
Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.
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10
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Long CP, Antoniewicz MR. Metabolic flux analysis of Escherichia coli knockouts: lessons from the Keio collection and future outlook. Curr Opin Biotechnol 2014; 28:127-33. [PMID: 24686285 DOI: 10.1016/j.copbio.2014.02.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Revised: 02/07/2014] [Accepted: 02/10/2014] [Indexed: 12/11/2022]
Abstract
Cellular metabolic and regulatory systems are of fundamental interest to biologists and engineers. Incomplete understanding of these complex systems remains an obstacle to progress in biotechnology and metabolic engineering. An established method for obtaining new information on network structure, regulation and dynamics is to study the cellular system following a perturbation such as a genetic knockout. The Keio collection of all viable Escherichia coli single-gene knockouts is facilitating a systematic investigation of the regulation and metabolism of E. coli. Of all omics measurements available, the metabolic flux profile (the fluxome) provides the most direct and relevant representation of the cellular phenotype. Recent advances in (13)C-metabolic flux analysis are now permitting highly precise and accurate flux measurements for investigating cellular systems and guiding metabolic engineering efforts.
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Affiliation(s)
- Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
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11
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Mattanovich D, Hatzimanikatis V. Editorial: metabolic modeling in biotechnology and medical research. Biotechnol J 2014; 8:962-3. [PMID: 24031032 DOI: 10.1002/biot.201300378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Metabolic Modeling and Simulation: This special issue of Biotechnology Journal is edited by Diethard Mattanovich and Vassily Hatzimanikatis and covers the state-of-the-art in metabolic modeling, including the major themes of methods in metabolic modeling, modeling of human and microbial metabolism, and modeling of bioprocesses.
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Affiliation(s)
- Diethard Mattanovich
- Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, Austria; Austrian Centre of Industrial Biotechnology, Vienna, Austria.
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12
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Production of 4-hydroxybutyric acid by metabolically engineered Mannheimia succiniciproducens and its conversion to γ-butyrolactone by acid treatment. Metab Eng 2013; 20:73-83. [DOI: 10.1016/j.ymben.2013.09.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 08/30/2013] [Accepted: 09/09/2013] [Indexed: 02/03/2023]
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