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

Hongzhong Lu, Feiran Li, Benjamín J. Sánchez, Zhengming Zhu, Gang Li, Iván Domenzain, Simonas Marcišauskas, Petre Mihail Anton, Dimitra Lappa, Christian Lieven, Moritz Emanuel Beber, Nikolaus Sonnenschein, Eduard J. Kerkhoven and Jens Nielsen ()
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Hongzhong Lu: Chalmers University of Technology
Feiran Li: Chalmers University of Technology
Benjamín J. Sánchez: Chalmers University of Technology
Zhengming Zhu: Chalmers University of Technology
Gang Li: Chalmers University of Technology
Iván Domenzain: Chalmers University of Technology
Simonas Marcišauskas: Chalmers University of Technology
Petre Mihail Anton: Chalmers University of Technology
Dimitra Lappa: Chalmers University of Technology
Christian Lieven: Technical University of Denmark
Moritz Emanuel Beber: Technical University of Denmark
Nikolaus Sonnenschein: Technical University of Denmark
Eduard J. Kerkhoven: Chalmers University of Technology
Jens Nielsen: Chalmers University of Technology

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: Abstract 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 (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.

Date: 2019
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DOI: 10.1038/s41467-019-11581-3

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