Väremo2015 - Human myocyte metabolic model
Ontology highlight
ABSTRACT:
SUBMITTER: 
Leif Väremo
PROVIDER: MODEL1503240000 | BioModels | 2016-02-10
REPOSITORIES: BioModels
Publications
Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes.
Väremo Leif L Scheele Camilla C Broholm Christa C Mardinoglu Adil A Kampf Caroline C Asplund Anna A Nookaew Intawat I Uhlén Mathias M Pedersen Bente Klarlund BK Nielsen Jens J
Cell reports 20150430 6
Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. Thes ...[more]
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Project description:This model is from the article:
Building a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae.
Smallbone K, Malys N, Messiha HL, Wishart JA, Simeonidis E. Methods Enzymol.
2011;500:355-70. 21943906
,
Abstract:
In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by utilizing the example of trehalose metabolism in yeast. The biology of the trehalose cycle is briefly reviewed and discussed.
This SBML model 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) 2005-2012 The BioModels.net Team.
For more information see the terms of use
.
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.
2010-07-12 | BIOMD0000000380 | BioModels
Project description: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)
2005-01-01 | MODEL1111190000 | BioModels
Project description:Bekaert2012 - Reconstruction of D.rerio Metabolic Network
Danio rerio
metabolic model accounting for subcellular compartmentalisation (ZebraGEM)
This SBML representation of the D. rerio
(zebrafish) metabolic network is made available under the Creative Commons Attribution-Share Alike 3.0 Unported Licence (see www.creativecommons.org
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This model is described in the article:
Reconstruction of Danio rerio Metabolic Model Accounting for Subcellular Compartmentalisation.
Bekaert M.
PLoS One. 2012;7(11):e49903.
Abstract:
Plant and microbial metabolic engineering is commonly used in the production of functional foods and quality trait improvement. Computational model-based approaches have been used in this important endeavour. However, to date, fish metabolic models have only been scarcely and partially developed, in marked contrast to their prominent success in metabolic engineering. In this study we present the reconstruction of fully compartmentalised models of the Danio rerio (zebrafish) on a global scale. This reconstruction involves extraction of known biochemical reactions in D. rerio for both primary and secondary metabolism and the implementation of methods for determining subcellular localisation and assignment of enzymes. The reconstructed model (ZebraGEM) is amenable for constraint-based modelling analysis, and accounts for 4,988 genes coding for 2,406 gene-associated reactions and only 418 non-gene-associated reactions. A set of computational validations (i.e., simulations of known metabolic functionalities and experimental data) strongly testifies to the predictive ability of the model. Overall, the reconstructed model is expected to lay down the foundations for computational-based rational design of fish metabolic engineering in aquaculture.
This model is hosted on BioModels Database
and identified by: MODEL1204120000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024
.
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>http://creativecommons.org/publicdomain/zero/1.0/] for more information.
2005-01-01 | MODEL1204120000 | BioModels
Project description:This is the model described in the article:
Towards a genome-scale kinetic model of cellular metabolism
Smallbone K, Simeonidis E, Swainston N, Mendes P. BMC Syst Biol.
2010 Jan 28;4(1):6. PMID: 20109182
, doi: 10.1186/1752-0509-4-6
Abstract:
CKGROUND: Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.
RESULTS: We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system.
CONCLUSIONS: Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model (see additional file 1) is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/
.
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.
2005-01-01 | MODEL1001200000 | BioModels
Project description:Heavner2012 - Metabolic Network of S.cerevisiae
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 is described in the article:
Yeast 5 - an expanded reconstruction of the Saccharomyces cerevisiae metabolic network.
Heavner BD, Smallbone K, Barker B, Mendes P, Walker LP.
BMC Syst Biol. 2012 Jun 4;6:55.
Abstract:
BACKGROUND:
Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature.
RESULTS:
Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model's predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3.
CONCLUSIONS:
Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.
This model is hosted on BioModels Database
and identified by: MODEL1209060000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024
.
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>http://creativecommons.org/publicdomain/zero/1.0/] for more information.
2005-01-01 | MODEL1209060000 | BioModels
Project description:This is a reconstruction of the biochemical network of the yeast Saccharomyces cerevisiae
carried out at a jamboree organized
in April 2007 in the Manchester Centre for Integrative Systems
Biology. It is the result of a consensus merger of two previous
reconstructions.
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).
All reactions are marked reversible because all chemical
reactions are reversible.
The model is described in the article and available as supplementary material:
A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.
Herrgård MJ, Swainston N et al.
, Nat Biotechnol.
2008 Oct;26(10):1155-60. PMID: 18846089
, doi: 10.1038/nbt1492
Abstract:
Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms. Up to date versions can be found under http://www.comp-sys-bio.org/yeastnet/
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
). 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.
2005-01-01 | MODEL0072364382 | BioModels