Big data in yeast systems biology
Artikel i vetenskaplig tidskrift, 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.

machine learning

multi-omics profiling

genome-scale metabolic models

integrative data analysis

big data

Författare

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

Ämneskategorier

Annan data- och informationsvetenskap

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1093/femsyr/foz070

PubMed

31603503

Mer information

Senast uppdaterat

2019-11-20