Dataset Information

Duarte2004 - Genome-scale metabolic network of Saccharomyces cerevisiae (iND750)

ABSTRACT: Duarte2004 - Genome-scale metabolic network of Saccharomyces cerevisiae (iND750) This model is described in the article: Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Duarte NC, Herrgård MJ, Palsson BØ. Genome Res. 2004 Jul; 14(7): 1298-1309 Abstract: A fully compartmentalized genome-scale metabolic model of Saccharomyces cerevisiae that accounts for 750 genes and their associated transcripts, proteins, and reactions has been reconstructed and validated. All of the 1149 reactions included in this in silico model are both elementally and charge balanced and have been assigned to one of eight cellular locations (extracellular space, cytosol, mitochondrion, peroxisome, nucleus, endoplasmic reticulum, Golgi apparatus, or vacuole). When in silico predictions of 4154 growth phenotypes were compared to two published large-scale gene deletion studies, an 83% agreement was found between iND750's predictions and the experimental studies. Analysis of the failure modes showed that false predictions were primarily caused by iND750's limited inclusion of cellular processes outside of metabolism. This study systematically identified inconsistencies in our knowledge of yeast metabolism that require specific further experimental investigation. This model is hosted on BioModels Database and identified by: MODEL1507180019. 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: MODEL1507180019 | BioModels | 2015-07-30

REPOSITORIES: BioModels

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Publications

Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model.

Duarte Natalie C NC Herrgård Markus J MJ Palsson Bernhard Ø BØ

Genome research 20040614 7

A fully compartmentalized genome-scale metabolic model of Saccharomyces cerevisiae that accounts for 750 genes and their associated transcripts, proteins, and reactions has been reconstructed and validated. All of the 1149 reactions included in this in silico model are both elementally and charge balanced and have been assigned to one of eight cellular locations (extracellular space, cytosol, mitochondrion, peroxisome, nucleus, endoplasmic reticulum, Golgi apparatus, or vacuole). When in silico ...[more]

PMID: 15197165

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Project description:Förster2008 - Genome-scale metabolic network of Saccharamyces cerevisiae (iFF708) This model is described in the article: Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Förster J, Famili I, Fu P, Palsson BØ, Nielsen J. Genome Res. 2003 Feb; 13(2): 244-253 Abstract: The metabolic network in the yeast Saccharomyces cerevisiae was reconstructed using currently available genomic, biochemical, and physiological information. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, and transport steps between the compartments and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network containing 1175 metabolic reactions and 584 metabolites. The number of gene functions included in the reconstructed network corresponds to approximately 16% of all characterized ORFs in S. cerevisiae. Using the reconstructed network, the metabolic capabilities of S. cerevisiae were calculated and compared with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions. This model is hosted on BioModels Database and identified by: MODEL1507180012. 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:Kuepfer2005 - Genome-scale metabolic network of Saccharomyces cerevisiae (iLL672) This model is described in the article: Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Kuepfer L, Sauer U, Blank LM. Genome Res. 2005 Oct; 15(10): 1421-1430 Abstract: The roles of duplicate genes and their contribution to the phenomenon of enzyme dispensability are a central issue in molecular and genome evolution. A comprehensive classification of the mechanisms that may have led to their preservation, however, is currently lacking. In a systems biology approach, we classify here back-up, regulatory, and gene dosage functions for the 105 duplicate gene families of Saccharomyces cerevisiae metabolism. The key tool was the reconciled genome-scale metabolic model iLL672, which was based on the older iFF708. Computational predictions of all metabolic gene knockouts were validated with the experimentally determined phenotypes of the entire singleton yeast library of 4658 mutants under five environmental conditions. iLL672 correctly identified 96%-98% and 73%-80% of the viable and lethal singleton phenotypes, respectively. Functional roles for each duplicate family were identified by integrating the iLL672-predicted in silico duplicate knockout phenotypes, genome-scale carbon-flux distributions, singleton mutant phenotypes, and network topology analysis. The results provide no evidence for a particular dominant function that maintains duplicate genes in the genome. In particular, the back-up function is not favored by evolutionary selection because duplicates do not occur more frequently in essential reactions than singleton genes. Instead of a prevailing role, multigene-encoded enzymes cover different functions. Thus, at least for metabolism, persistence of the paralog fraction in the genome can be better explained with an array of different, often overlapping functional roles. This model is hosted on BioModels Database and identified by: MODEL1507180066. 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:Genome-scale datasets have been used extensively in model organisms to screen for specific candidates or to predict functions for uncharacterized genes. However, despite the availability of extensive knowledge in model organisms, the planning of genome-scale experiments in poorly studied species is still based on the intuition of experts or heuristic trials. We propose that computational and systematic approaches can be applied to drive the experiment planning process in poorly studied species based on available data and knowledge in closely related model organisms. In this paper, we suggest a computational strategy for recommending genome-scale experiments based on their capability to interrogate diverse biological processes to enable protein function assignment. To this end, we use the data-rich functional genomics compendium of the model organism to quantify the accuracy of each dataset in predicting each specific biological process and the overlap in such coverage between different datasets. Our approach uses an optimized combination of these quantifications to recommend an ordered list of experiments for accurately annotating most proteins in the poorly studied related organisms to most biological processes, as well as a set of experiments that target each specific biological process. The effectiveness of this experiment- planning system is demonstrated for two related yeast species: the model organism Saccharomyces cerevisiae and the comparatively poorly studied Saccharomyces bayanus. Our system recommended a set of S. bayanus experiments based on an S. cerevisiae microarray data compendium. In silico evaluations estimate that less than 10% of the experiments could achieve similar functional coverage to the whole microarray compendium. This estimation was confirmed by performing the recommended experiments in S. bayanus, therefore significantly reducing the labor devoted to characterize the poorly studied genome. This experiment-planning framework could readily be adapted to the design of other types of large-scale experiments as well as other groups of organisms.

Project description:Background: Cell line specific, genome-scale metabolic models enable rigorous and systematic in silico investigation of cellular metabolism. Such models have recently become available for Chinese hamster ovary (CHO) too. However, a key ingredient, namely an experimentally validated biomass function that summarizes the cellular composition, was so far missing in all genome-scale metabolic models of CHO cell lines. Here, we close this gap by providing extensive experimental data on the biomass composition of 13 parental and producer CHO cell lines under various conditions.Results: We report protein, lipid, DNA, RNA and carbohydrate content, cell dry mass, and protein and lipid composition. Furthermore, we present meticulous data on exchange rates between cells and environment and provide detailed experimental protocols on how to determine all of the above. The biomass composition is converted into cell line and condition specific biomass functions for usage in cell line specific, genome-scale metabolic models of CHO. Finally, flux-balance analysis (FBA) is used to demonstrate consistency between in silico predictions and experimental analysis.Conclusions: Our study reveals a strong variability of the total protein content and cell dry mass across cell lines. Yet, the relative amino acid composition is independent of the cell lines and conditions and thus needs not be explicitly measured for each new cell line. In contrast, the lipid composition is strongly influenced by the growth media and needs to be determined for each case. These cell line specific variations in the biomass composition have a small impact on growth rate predictions with FBA, as inaccuracies in the predictions are typically dominated by inaccuracies in the exchange rate spectra. Cell specific biomass variations become only important if the experimental errors in the exchange rate spectra drop below twenty percent. Links:All data and code for data processing are available at <a href='http://dx.doi.org/10.17632/g3wz7spyc9.1' rel='noopener noreferrer' target='_blank'>Data Mendeley</a>.Cell line specific metabolic models were deposited in <a href='https://www.ebi.ac.uk/biomodels/(Chelliah et al, 2014)' rel='noopener noreferrer' target='_blank'>BioModels</a>, identifiers are listed in the study sample table.

Dataset Information

Duarte2004 - Genome-scale metabolic network of Saccharomyces cerevisiae (iND750)

Publications

Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model.

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