Bayesian genome scale modelling identifies thermal determinants of yeast metabolism
Journal article, 2021
The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.
Author
Gang Li
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Yating Hu
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Jan Zrimec
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Hao Luo
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Hao Wang
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology, CSBI
Wallenberg Lab.
Aleksej Zelezniak
Science for Life Laboratory (SciLifeLab)
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Boyang Ji
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Technical University of Denmark (DTU)
Jens B Nielsen
BioInnovation Institute
Technical University of Denmark (DTU)
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Nature Communications
2041-1723 (ISSN)
Vol. 12 1 190Subject Categories
Pharmaceutical Sciences
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1038/s41467-020-20338-2
PubMed
33420025