Dataset Information

Liao2011 - Genome-scale metabolic reconstruction of Klebsiella pneumoniae (iYL1228)

ABSTRACT: Liao2011 - Genome-scale metabolic reconstruction of Klebsiella pneumoniae (iYL1228) This model is described in the article: An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228. Liao YC, Huang TW, Chen FC, Charusanti P, Hong JS, Chang HY, Tsai SF, Palsson BO, Hsiung CA. J. Bacteriol. 2011 Apr; 193(7): 1710-1717 Abstract: Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism. This model is hosted on BioModels Database and identified by: MODEL1507180054. 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: MODEL1507180054 | BioModels | 2015-07-30

REPOSITORIES: BioModels

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An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228.

Liao Yu-Chieh YC Huang Tzu-Wen TW Chen Feng-Chi FC Charusanti Pep P Hong Jay S J JS Chang Hwan-You HY Tsai Shih-Feng SF Palsson Bernhard O BO Hsiung Chao A CA

Journal of bacteriology 20110204 7

Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass compos ...[more]

PMID: 21296962

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Project description:Wodke2013 - Genome-scale constraint-based model of M.pneumoniae energy metabolism (iJW145) A new genome-scale metabolic reconstruction of M.pneumoniae is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome-reduce human pathogen. This model is described in the article: Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling. Wodke JA, Puchałka J, Lluch-Senar M, Marcos J, Yus E, Godinho M, Gutiérrez-Gallego R, dos Santos VA, Serrano L, Klipp E, Maier T. Mol Syst Biol. 2013;9:653. Abstract: Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms. This model is hosted on BioModels Database and identified by: MODEL1301290000 . 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:Becker2005 - Genome-scale metabolic network of Staphylococcus aureus (iSB619) This model is described in the article: Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. Becker SA, Palsson BØ. BMC Microbiol. 2005; 5: 8 Abstract: BACKGROUND: Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties. RESULTS: We reconstructed a genome-scale metabolic network of S. aureus strain N315. This reconstruction, termed iSB619, consists of 619 genes that catalyze 640 metabolic reactions. For 91% of the reactions, open reading frames are explicitly linked to proteins and to the reaction. All but three of the metabolic reactions are both charge and elementally balanced. The reaction list is the most complete to date for this pathogen. When the capabilities of the reconstructed network were analyzed in the context of maximal growth, we formed hypotheses regarding growth requirements, the efficiency of growth on different carbon sources, and potential drug targets. These hypotheses can be tested experimentally and the data gathered can be used to improve subsequent versions of the reconstruction. CONCLUSION: iSB619 represents comprehensive biochemically and genetically structured information about the metabolism of S. aureus to date. The reconstructed metabolic network can be used to predict cellular phenotypes and thus advance our understanding of a troublesome pathogen. This model is hosted on BioModels Database and identified by: MODEL1507180070. 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:Oh2007 - Genome-scale metabolic network of Bacillus subtilis (iYO844) This model is described in the article: Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. Oh YK, Palsson BO, Park SM, Schilling CH, Mahadevan R. J. Biol. Chem. 2007 Sep; 282(39): 28791-28799 Abstract: In this report, a genome-scale reconstruction of Bacillus subtilis metabolism and its iterative development based on the combination of genomic, biochemical, and physiological information and high-throughput phenotyping experiments is presented. The initial reconstruction was converted into an in silico model and expanded in a four-step iterative fashion. First, network gap analysis was used to identify 48 missing reactions that are needed for growth but were not found in the genome annotation. Second, the computed growth rates under aerobic conditions were compared with high-throughput phenotypic screen data, and the initial in silico model could predict the outcomes qualitatively in 140 of 271 cases considered. Detailed analysis of the incorrect predictions resulted in the addition of 75 reactions to the initial reconstruction, and 200 of 271 cases were correctly computed. Third, in silico computations of the growth phenotypes of knock-out strains were found to be consistent with experimental observations in 720 of 766 cases evaluated. Fourth, the integrated analysis of the large-scale substrate utilization and gene essentiality data with the genome-scale metabolic model revealed the requirement of 80 specific enzymes (transport, 53; intracellular reactions, 27) that were not in the genome annotation. Subsequent sequence analysis resulted in the identification of genes that could be putatively assigned to 13 intracellular enzymes. The final reconstruction accounted for 844 open reading frames and consisted of 1020 metabolic reactions and 988 metabolites. Hence, the in silico model can be used to obtain experimentally verifiable hypothesis on the metabolic functions of various genes. This model is hosted on BioModels Database and identified by: MODEL1507180013. 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:Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in growth performance and gas consumption/production of three wild-type strains and one genetically engineered strain in two independent quadruplicate chemostat bioreactor experiments: 1) with molecular hydrogen and carbon dioxide; and 2) with sodium formate. In the first experiment, we found the highest methane production rate for Methanothermobacter thermautotrophicus ΔH, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. We collected statistically reliable transcriptomics and proteomics data sets from these steady-state bioreactors, which we integrated within our genome-scale metabolic models. The implementation of an pan-model that contains combined reactions from all three microbes allowed us to perform an interspecies comparison of the complete omics data set. While the observed differences in the growth behavior cannot be fully explained, the comparison enabled us to identify crucial differences in growth-related pathways, such as formate anabolism. In the second experiment, we found stable growth with a M. thermautotrophicus ΔH plasmid-carrying strain on formate with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. The results of the two studies demonstrate the advantages of an integrative approach using fermentation and omics data with genome-scale modeling for the investigation of lesser studied metabolisms, and the biotechnological potential of Methanothermobacter spp. as production platform hosts.

Project description:Archer2011 - Genome-scale metabolic model of Escherichia coli (iCA1273) This model is described in the article: The genome sequence of E. coli W (ATCC 9637): comparative genome analysis and an improved genome-scale reconstruction of E. coli. Archer CT, Kim JF, Jeong H, Park JH, Vickers CE, Lee SY, Nielsen LK. BMC Genomics 2011; 12: 9 Abstract: BACKGROUND: Escherichia coli is a model prokaryote, an important pathogen, and a key organism for industrial biotechnology. E. coli W (ATCC 9637), one of four strains designated as safe for laboratory purposes, has not been sequenced. E. coli W is a fast-growing strain and is the only safe strain that can utilize sucrose as a carbon source. Lifecycle analysis has demonstrated that sucrose from sugarcane is a preferred carbon source for industrial bioprocesses. RESULTS: We have sequenced and annotated the genome of E. coli W. The chromosome is 4,900,968 bp and encodes 4,764 ORFs. Two plasmids, pRK1 (102,536 bp) and pRK2 (5,360 bp), are also present. W has unique features relative to other sequenced laboratory strains (K-12, B and Crooks): it has a larger genome and belongs to phylogroup B1 rather than A. W also grows on a much broader range of carbon sources than does K-12. A genome-scale reconstruction was developed and validated in order to interrogate metabolic properties. CONCLUSIONS: The genome of W is more similar to commensal and pathogenic B1 strains than phylogroup A strains, and therefore has greater utility for comparative analyses with these strains. W should therefore be the strain of choice, or 'type strain' for group B1 comparative analyses. The genome annotation and tools created here are expected to allow further utilization and development of E. coli W as an industrial organism for sucrose-based bioprocesses. Refinements in our E. coli metabolic reconstruction allow it to more accurately define E. coli metabolism relative to previous models. This model is hosted on BioModels Database and identified by: MODEL1507180010. 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:This model is from the article: A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BØ. Mol Syst Biol. 2011 Oct 11;7:535. doi: 10.1038/msb.2011.65. 21988831 , Abstract: The initial genome-scale reconstruction of the metabolic network of Escherichia coli K-12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. 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. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Dataset Information

Liao2011 - Genome-scale metabolic reconstruction of Klebsiella pneumoniae (iYL1228)

Publications

An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228.

Similar Datasets

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