Bayesian genome scale modelling identifies thermal determinants of yeast metabolism
Artikel i vetenskaplig tidskrift, 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.
Författare
Gang Li
Chalmers, Biologi och bioteknik, Systembiologi
Yating Hu
Chalmers, Biologi och bioteknik, Systembiologi
Jan Zrimec
Chalmers, Biologi och bioteknik, Systembiologi
Hao Luo
Chalmers, Biologi och bioteknik, Systembiologi
Hao Wang
Chalmers, Biologi och bioteknik, Systembiologi, CSBI
Wallenberg Lab.
Aleksej Zelezniak
Science for Life Laboratory (SciLifeLab)
Chalmers, Biologi och bioteknik, Systembiologi
Boyang Ji
Chalmers, Biologi och bioteknik, Systembiologi
Danmarks Tekniske Universitet (DTU)
Jens B Nielsen
BioInnovation Institute
Danmarks Tekniske Universitet (DTU)
Chalmers, Biologi och bioteknik, Systembiologi
Nature Communications
2041-1723 (ISSN)
Vol. 12 1 190Ämneskategorier
Farmaceutisk vetenskap
Bioinformatik (beräkningsbiologi)
Bioinformatik och systembiologi
DOI
10.1038/s41467-020-20338-2
PubMed
33420025