Dataset Information

Loira2012 - Metabolic Network of Y.lipolytica

ABSTRACT: This model is from the article: A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica Nicolas Loira, Thierry Dulermo, Jean-Marc Nicaud and David J Sherman BMC Systems Biology 2012, 6:35 doi: 10.1186/1752-0509-6-35 , Abstract: Background Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism. Results Combining in silico tools and expert manual curation, we have produced an accurate genome-scale metabolic model for Y. lipolytica. Using a scaffold derived from a functional metabolic model of the well-studied but phylogenetically distant yeast S. cerevisiae, we mapped conserved reactions, rewrote gene associations, added species-specific reactions and inflected specialized copies of scaffold reactions to account for species-specific expansion of protein families. We used physiological measures obtained under lab conditions to validate our predictions. Conclusions Y. lipolytica iNL895represents the first well-annotated metabolic model of an oleaginous yeast, providing a reference for future metabolic improvement, and a starting point for the metabolic reconstruction of other species in the Yarrowia clade and other oleaginous yeasts. Note: This SBML representation of the Yarrowia lipolytica metabolic network is made available under the Creative Commons Attribution-Share Alike 3.0 Unported Licence (see www.creativecommons.org). Nicolas Loira (loira@inria.fr, nloira@gmail.com); based on S.cerevisiae model yeast_4.36.xml (Herrgard, 2008)

SUBMITTER: Nicolas Loira

PROVIDER: MODEL1111190000 | BioModels | 2005-01-01

REPOSITORIES: BioModels

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A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica.

Loira Nicolas N Dulermo Thierry T Nicaud Jean-Marc JM Sherman David James DJ

BMC systems biology 20120504

<h4>Background</h4>Yarrowia lipolytica is an oleaginous yeast which has emerged as an important microorganism for several biotechnological processes, such as the production of organic acids, lipases and proteases. It is also considered a good candidate for single-cell oil production. Although some of its metabolic pathways are well studied, its metabolic engineering is hindered by the lack of a genome-scale model that integrates the current knowledge about its metabolism.<h4>Results</h4>Combinin ...[more]

PMID: 22558935

Publication: 1/34

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Project description:This is a reconstruction of the metabolic network of the yeast Saccharomyces cerevisiae as described in the article: The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. Nookaew I, Jewett MC, Meechai A, Thammarongtham C, Laoteng K, Cheevadhanarak S, Nielsen J, Bhumiratana S. BMC Syst Biol. 2008 Aug 7;2:71; PMID: 18687109 ,doi: 10.1186/1752-0509-2-71 Abstract: BACKGROUND: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism. RESULTS: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. CONCLUSION: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states. The SBML was downloaded from the BioMet Toolbox page( http://129.16.106.142/models.php?c=S.cerevisiae ) of Jens Nielsen's Lab for Systems Biology at Chalmers University ( http://www.sysbio.se/ ). The parameters LOWER_BOUND and UPPER_BOUND were added as defined in the BioOpt file available from the same page. One error was corrected R_PIxtO (Excretion of phosphate) LOWER_BOUND was changed from 1000 to 0. Parameters FLUX_VALUE were then calculated as defined in "Flux balance analysis: a geometric perspective.", Smallbone K and Simeonidis E, 2009 (J Theor Biol. 2009;258(2):311-5, pmid: 19490860 ). Technical notes: The compartments included here have no volume defined; there are no reliable estimates available for those volumes yet. There are no kinetic functions defined for the reactions because this model only represents the chemical structure of the network (stoichiometry). Reactions for uptake and excretion are defined for some of the metabolites. All uptake reactions are constrained to zero flux and all excretion reactions are unconstrained. All genes are assigned to the cytosol. This has no physiological meaning, but it is necessary for the structure of the model. Biomass equations are available for carbon-limited and nitrogen-limited growth. The nitrogen-limited biomass equation is constrained to zero flux. A few reactions are meant to be used to simulate the effect of for example increased ATP production. They are constrained to zero flux. This SBML representation of the yeast metabolic network is made available under the Creative Commons Attribution-Share Alike 3.0 Unported Licence (see www.creativecommons.org ). 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.

Project description:Kavšček2015 - Genome-scale metabolic model of Yarrowia lipolytica (iMK735) This model is described in the article: Optimization of lipid production with a genome-scale model of Yarrowia lipolytica. Kavšček M, Bhutada G, Madl T, Natter K. BMC Syst Biol 2015; 9: 72 Abstract: Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion.Using a genome-scale model (GSM) of baker's yeast as a scaffold, we reconstructed the metabolic network of Y. lipolytica and optimized it for use in flux balance analysis (FBA), with the aim to simulate growth and lipid production phases of this yeast. We validated our model and found the predictions of the growth behavior of Y. lipolytica in excellent agreement with experimental data. Based on these data, we successfully designed a fed-batch strategy to avoid citrate excretion during the lipid production phase. Further analysis of the network suggested that the oxygen demand of Y. lipolytica is reduced upon induction of lipid synthesis. According to this finding we hypothesized that a reduced aeration rate might induce lipid accumulation. This prediction was indeed confirmed experimentally. In a fermentation combining these two strategies lipid content of the biomass was increased by 80%, and lipid yield was improved more than four-fold, compared to standard conditions.Genome scale network reconstructions provide a powerful tool to predict the effects of genetic modifications and the metabolic response to environmental conditions. The high accuracy and the predictive value of a newly reconstructed GSM of Y. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineering Y. lipolytica towards higher efficiency in single cell oil production are discussed. This model is hosted on BioModels Database and identified by: MODEL1510060001. 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.

Dataset Information

Loira2012 - Metabolic Network of Y.lipolytica

Publications

A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica.

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