A multiphase multiobjective dynamic genome-scale model shows different redox balancing among yeast species of the saccharomyces genus in fermentation

D. Henriques, R. Minebois, S.N. Mendoza, L.G. Macías, R. Pérez-Torrado, E. Barrio, B. Teusink, A. Querol, E. Balsa-Canto

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

© 2021 Henriques et al.Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts' metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories.
Original languageEnglish
Article numbere00260-21
JournalmSystems
Volume6
Issue number4
DOIs
Publication statusPublished - 1 Jul 2021

Funding

This project has received funding from MCIU/AEI/FEDER, UE (grant references RTI2018-093744-B-C31, RTI2018-093744-B-C32, RTI2018-093744-B-C33, and PID2019-104113RB-I00) and Xunta de Galicia (IN607B 2020/03). R.M. was supported by an FPI grant from the Ministerio de Economía y Competitividad, Spain (reference BES-2016-078202). S.N.M. acknowledges funding from CONICYT Becas Chile grant 72180373. S.N.M. and B.T. acknowledge support from YogurtDesign, EraCoBioTech grant 053.80.733.

FundersFunder number
Ministerio de Ciencia, Innovación y Universidades
Comisión Nacional de Investigación Científica y Tecnológica72180373, 053.80.733
Ministerio de Economía y CompetitividadBES-2016-078202
European Regional Development FundRTI2018-093744-B-C32, PID2019-104113RB-I00
Xunta de GaliciaIN607B 2020/03
Agencia Estatal de Investigación

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