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A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism

Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simula...

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Autores principales: Lu, Hongzhong, Li, Feiran, Sánchez, Benjamín J., Zhu, Zhengming, Li, Gang, Domenzain, Iván, Marcišauskas, Simonas, Anton, Petre Mihail, Lappa, Dimitra, Lieven, Christian, Beber, Moritz Emanuel, Sonnenschein, Nikolaus, Kerkhoven, Eduard J., Nielsen, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6687777/
https://www.ncbi.nlm.nih.gov/pubmed/31395883
http://dx.doi.org/10.1038/s41467-019-11581-3
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author Lu, Hongzhong
Li, Feiran
Sánchez, Benjamín J.
Zhu, Zhengming
Li, Gang
Domenzain, Iván
Marcišauskas, Simonas
Anton, Petre Mihail
Lappa, Dimitra
Lieven, Christian
Beber, Moritz Emanuel
Sonnenschein, Nikolaus
Kerkhoven, Eduard J.
Nielsen, Jens
author_facet Lu, Hongzhong
Li, Feiran
Sánchez, Benjamín J.
Zhu, Zhengming
Li, Gang
Domenzain, Iván
Marcišauskas, Simonas
Anton, Petre Mihail
Lappa, Dimitra
Lieven, Christian
Beber, Moritz Emanuel
Sonnenschein, Nikolaus
Kerkhoven, Eduard J.
Nielsen, Jens
author_sort Lu, Hongzhong
collection PubMed
description Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8(DB)), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.
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spelling pubmed-66877772019-08-12 A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism Lu, Hongzhong Li, Feiran Sánchez, Benjamín J. Zhu, Zhengming Li, Gang Domenzain, Iván Marcišauskas, Simonas Anton, Petre Mihail Lappa, Dimitra Lieven, Christian Beber, Moritz Emanuel Sonnenschein, Nikolaus Kerkhoven, Eduard J. Nielsen, Jens Nat Commun Article Genome-scale metabolic models (GEMs) represent extensive knowledgebases that provide a platform for model simulations and integrative analysis of omics data. This study introduces Yeast8 and an associated ecosystem of models that represent a comprehensive computational resource for performing simulations of the metabolism of Saccharomyces cerevisiae––an important model organism and widely used cell-factory. Yeast8 tracks community development with version control, setting a standard for how GEMs can be continuously updated in a simple and reproducible way. We use Yeast8 to develop the derived models panYeast8 and coreYeast8, which in turn enable the reconstruction of GEMs for 1,011 different yeast strains. Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8(DB)), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits. Nature Publishing Group UK 2019-08-08 /pmc/articles/PMC6687777/ /pubmed/31395883 http://dx.doi.org/10.1038/s41467-019-11581-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lu, Hongzhong
Li, Feiran
Sánchez, Benjamín J.
Zhu, Zhengming
Li, Gang
Domenzain, Iván
Marcišauskas, Simonas
Anton, Petre Mihail
Lappa, Dimitra
Lieven, Christian
Beber, Moritz Emanuel
Sonnenschein, Nikolaus
Kerkhoven, Eduard J.
Nielsen, Jens
A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
title A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
title_full A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
title_fullStr A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
title_full_unstemmed A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
title_short A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
title_sort consensus s. cerevisiae metabolic model yeast8 and its ecosystem for comprehensively probing cellular metabolism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6687777/
https://www.ncbi.nlm.nih.gov/pubmed/31395883
http://dx.doi.org/10.1038/s41467-019-11581-3
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