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 1 190

Subject Categories

Pharmaceutical Sciences

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

DOI

10.1038/s41467-020-20338-2

More information

Latest update

1/20/2021