Big data in yeast systems biology
Reviewartikel, 2019

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.

genome-scale metabolic models

machine learning

multi-omics profiling

big data

integrative data analysis


Tao Yu

Chalmers, Biologi och bioteknik, Systembiologi

Jens B Nielsen

Danmarks Tekniske Universitet (DTU)

Chalmers, Biologi och bioteknik, Systembiologi

FEMS Yeast Research

1567-1356 (ISSN) 1567-1364 (eISSN)

Vol. 19 7 foz070


Annan data- och informationsvetenskap

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi





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