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Big data in yeast systems biology.

Authors
  • Yu, Rosemary1, 2
  • Nielsen, Jens1, 2, 3, 4
  • 1 Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. , (Sweden)
  • 2 Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden. , (Sweden)
  • 3 Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark. , (Denmark)
  • 4 BioInnovation Institute, Ole Maaløes Vej 3, DK-2200 Copenhagen N, Denmark. , (Denmark)
Type
Published Article
Journal
FEMS Yeast Research
Publisher
Oxford University Press
Publication Date
Nov 01, 2019
Volume
19
Issue
7
Identifiers
DOI: 10.1093/femsyr/foz070
PMID: 31603503
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology. © FEMS 2019.

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