Dataset Information

Kim2007 - Genome-scale metabolic network of Mannheimia succiniciproducens (iTY425)

ABSTRACT: Kim2007 - Genome-scale metabolic network of Mannheimia succiniciproducens (iTY425) This model is described in the article: Genome-scale analysis of Mannheimia succiniciproducens metabolism. Kim TY, Kim HU, Park JM, Song H, Kim JS, Lee SY. Biotechnol. Bioeng. 2007 Jul; 97(4): 657-671 Abstract: Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement. This model is hosted on BioModels Database and identified by: MODEL1507180062. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Nicolas Le Novère

PROVIDER: MODEL1507180062 | BioModels | 2015-07-30

REPOSITORIES: BioModels

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Publications

Genome-scale analysis of Mannheimia succiniciproducens metabolism.

Kim Tae Yong TY Kim Hyun Uk HU Park Jong Myoung JM Song Hyohak H Kim Jin Sik JS Lee Sang Yup SY

Biotechnology and bioengineering 20070701 4

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was empl ...[more]

PMID: 17405177

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Project description:Lee2010 - Genome-scale metabolic network of Zymomonas mobilis (iZmobMBEL601) This model is described in the article: The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies. Lee KY, Park JM, Kim TY, Yun H, Lee SY. Microb. Cell Fact. 2010; 9: 94 Abstract: BACKGROUND: Zymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions. RESULTS: The genome-scale metabolic model of Z. mobilis ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of Z. mobilis were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of Z. mobilis as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum in silico yields of ethanol for Z. mobilis and Escherichia coli were compared and analyzed. Furthermore, the substrate utilization range of Z. mobilis was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow Z. mobilis to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of Z. mobilis. CONCLUSION: The genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of Z. mobilis under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of Z. mobilis metabolism and consequently designing metabolic engineering strategies for various biotechnological applications. This model is hosted on BioModels Database and identified by: MODEL1507180031. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Hong2004 - Genome-scale metabolic network of Mannheimia succiniciproducens (iSH335) This model is described in the article: The genome sequence of the capnophilic rumen bacterium Mannheimia succiniciproducens. Hong SH, Kim JS, Lee SY, In YH, Choi SS, Rih JK, Kim CH, Jeong H, Hur CG, Kim JJ. Nat. Biotechnol. 2004 Oct; 22(10): 1275-1281 Abstract: The rumen represents the first section of a ruminant animal's stomach, where feed is collected and mixed with microorganisms for initial digestion. The major gas produced in the rumen is CO(2) (65.5 mol%), yet the metabolic characteristics of capnophilic (CO(2)-loving) microorganisms are not well understood. Here we report the 2,314,078 base pair genome sequence of Mannheimia succiniciproducens MBEL55E, a recently isolated capnophilic Gram-negative bacterium from bovine rumen, and analyze its genome contents and metabolic characteristics. The metabolism of M. succiniciproducens was found to be well adapted to the oxygen-free rumen by using fumarate as a major electron acceptor. Genome-scale metabolic flux analysis indicated that CO(2) is important for the carboxylation of phosphoenolpyruvate to oxaloacetate, which is converted to succinic acid by the reductive tricarboxylic acid cycle and menaquinone systems. This characteristic metabolism allows highly efficient production of succinic acid, an important four-carbon industrial chemical. This model is hosted on BioModels Database and identified by: MODEL1507180025. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Kim2009 - Genome-scale metabolic network of Acinetobacter baumannii (AbyMBEL891) This model is described in the article: Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Kim HU, Kim TY, Lee SY. Mol Biosyst 2010 Feb; 6(2): 339-348 Abstract: Acinetobacter baumannii has emerged as a new clinical threat to human health, particularly to ill patients in the hospital environment. Current lack of effective clinical solutions to treat this pathogen urges us to carry out systems-level studies that could contribute to the development of an effective therapy. Here we report the development of a strategy for identifying drug targets by combined genome-scale metabolic network and essentiality analyses. First, a genome-scale metabolic network of A. baumannii AYE, a drug-resistant strain, was reconstructed based on its genome annotation data, and biochemical knowledge from literatures and databases. In order to evaluate the performance of the in silico model, constraints-based flux analysis was carried out with appropriate constraints. Simulations were performed from both reaction (gene)- and metabolite-centric perspectives, each of which identifies essential genes/reactions and metabolites critical to the cell growth. The gene/reaction essentiality enables validation of the model and its comparative study with other known organisms' models. The metabolite essentiality approach was undertaken to predict essential metabolites that are critical to the cell growth. The EMFilter, a framework that filters initially predicted essential metabolites to find the most effective ones as drug targets, was also developed. EMFilter considers metabolite types, number of total and consuming reaction linkage with essential metabolites, and presence of essential metabolites and their relevant enzymes in human metabolism. Final drug target candidates obtained by this system framework are presented along with implications of this approach. This model is hosted on BioModels Database and identified by: MODEL1507180029. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Selvarasu2009 - Genome-scale metabolic network of Mus Musculus (iSS724) This model is described in the article: Genome-scale modeling and in silico analysis of mouse cell metabolic network. Selvarasu S, Karimi IA, Ghim GH, Lee DY. Mol Biosyst 2010 Jan; 6(1): 152-161 Abstract: Genome-scale metabolic modeling has been successfully applied to a multitude of microbial systems, thus improving our understanding of their cellular metabolisms. Nevertheless, only a handful of works have been done for describing mammalian cells, particularly mouse, which is one of the important model organisms, providing various opportunities for both biomedical research and biotechnological applications. Presented herein is a genome-scale mouse metabolic model that was systematically reconstructed by improving and expanding the previous generic model based on integrated biochemical and genomic data of Mus musculus. The key features of the updated model include additional information on gene-protein-reaction association, and improved network connectivity through lipid, amino acid, carbohydrate and nucleotide biosynthetic pathways. After examining the model predictability both quantitatively and qualitatively using constraints-based flux analysis, the structural and functional characteristics of the mouse metabolism were investigated by evaluating network statistics/centrality, gene/metabolite essentiality and their correlation. The results revealed that overall mouse metabolic network is topologically dominated by highly connected and bridging metabolites, and functionally by lipid metabolism that most of essential genes and metabolites are from. The current in silico mouse model can be exploited for understanding and characterizing the cellular physiology, identifying potential cell engineering targets for the enhanced production of recombinant proteins and developing diseased state models for drug targeting. This model is hosted on BioModels Database and identified by: MODEL1507180042. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Oliveira2005 - Genome-scale metabolic network of Lactococcus lactis (iAO358) This model is described in the article: Modeling Lactococcus lactis using a genome-scale flux model. Oliveira AP, Nielsen J, Förster J. BMC Microbiol. 2005; 5: 39 Abstract: BACKGROUND: Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks. RESULTS: The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. CONCLUSION: The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function. This model is hosted on BioModels Database and identified by: MODEL1507180014. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Widiastuti2010 - Genome-scale metabolic network Zymomonas mobilis (iZM363) This model is described in the article: Genome-scale modeling and in silico analysis of ethanologenic bacteria Zymomonas mobilis. Widiastuti H, Kim JY, Selvarasu S, Karimi IA, Kim H, Seo JS, Lee DY. Biotechnol. Bioeng. 2011 Mar; 108(3): 655-665 Abstract: Bioethanol has been recognized as a potential alternative energy source. Among various ethanol-producing microbes, Zymomonas mobilis has acquired special attention due to its higher ethanol yield and tolerance. However, cellular metabolism in Z. mobilis remains unclear, hindering its practical application for bioethanol production. To elucidate such physiological characteristics, we reconstructed and validated a genome-scale metabolic network (iZM363) of Z. mobilis ATCC31821 (ZM4) based on its annotated genome and biochemical information. The phenotypic behaviors and metabolic states predicted by our genome-scale model were highly consistent with the experimental observations of Z. mobilis ZM4 strain growing on glucose as well as NMR-measured intracellular fluxes of an engineered strain utilizing glucose, fructose, and xylose. Subsequent comparative analysis with Escherichia coli and Saccharomyces cerevisiae as well as gene essentiality and flux coupling analyses have also confirmed the functional role of pdc and adh genes in the ethanologenic activity of Z. mobilis, thus leading to better understanding of this natural ethanol producer. In future, the current model could be employed to identify potential cell engineering targets, thereby enhancing the productivity of ethanol in Z. mobilis. This model is hosted on BioModels Database and identified by: MODEL1507180057. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Kim2007 - Genome-scale metabolic network of Mannheimia succiniciproducens (iTY425)

Publications

Genome-scale analysis of Mannheimia succiniciproducens metabolism.

Similar Datasets

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