Dataset Information

Hong2004 - Genome-scale metabolic network of Mannheimia succiniciproducens (iSH335)

ABSTRACT: 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.

SUBMITTER: Nicolas Le Novère

PROVIDER: MODEL1507180025 | BioModels | 2015-07-30

REPOSITORIES: BioModels

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The genome sequence of the capnophilic rumen bacterium Mannheimia succiniciproducens.

Hong Soon Ho SH Kim Jin Sik JS Lee Sang Yup SY In Yong Ho YH Choi Sun Shim SS Rih Jeong-Keun JK Kim Chang Hoon CH Jeong Haeyoung H Hur Cheol Goo CG Kim Jae Jong JJ

Nature biotechnology 20040919 10

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 ge ...[more]

PMID: 15378067

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Project description: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.

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:Sohn2010 - Genome-scale metabolic network of Pseudomonas putida (PpuMBEL1071) This model is described in the article: In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival. Sohn SB, Kim TY, Park JM, Lee SY. Biotechnol J 2010 Jul; 5(7): 739-750 Abstract: Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli. This model is hosted on BioModels Database and identified by: MODEL1507180043. 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.