A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism
Artikel i vetenskaplig tidskrift, 2019
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.
Författare
Hongzhong Lu
Chalmers, Biologi och bioteknik, Systembiologi
Feiran Li
Chalmers, Biologi och bioteknik, Systembiologi
Benjamín José Sánchez
Chalmers, Biologi och bioteknik, Systembiologi
Zhengming Zhu
Chalmers, Biologi och bioteknik, Systembiologi
Jiangnan University
Gang Li
Chalmers, Biologi och bioteknik, Systembiologi
Iván Domenzain Del Castillo Cerecer
Chalmers, Biologi och bioteknik, Systembiologi
Simonas Marcisauskas
Chalmers, Biologi och bioteknik, Systembiologi
Petre Mihail Anton
CSBI
Dimitra Lappa
Chalmers, Biologi och bioteknik, Systembiologi
Christian Lieven
Danmarks Tekniske Universitet (DTU)
Moritz Emanuel Beber
Danmarks Tekniske Universitet (DTU)
N. Sonnenschein
Danmarks Tekniske Universitet (DTU)
Eduard Kerkhoven
Chalmers, Biologi och bioteknik, Systembiologi
Jens B Nielsen
Danmarks Tekniske Universitet (DTU)
Chalmers, Biologi och bioteknik, Systembiologi
BioInnovation Institute
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 10 1 3586Ämneskategorier
Bioinformatik (beräkningsbiologi)
Reglerteknik
Datavetenskap (datalogi)
DOI
10.1038/s41467-019-11581-3