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A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations

  • Marco Vanoni*
  • , Pasquale Palumbo
  • , Federico Papa
  • , Stefano Busti
  • , Laura Gotti
  • , Meike Wortel
  • , Bas Teusink
  • , Ivan Orlandi
  • , Alex Pessina
  • , Cristina Airoldi
  • , Luca Brambilla
  • , Marina Vai
  • , Lilia Alberghina*
  • *Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

For unicellular organisms, the reproduction rate and growth are crucial fitness determinants and functional manifestations of the organism genotype. Using the budding yeast Saccharomyces cerevisiae as a model organism, we integrated metabolism, which provides energy and building blocks for growth, with cell mass growth and cell cycle progression into a low-granularity, multiscale (from cell to population) computational model. This model predicted that cells with constitutive respiration do not modulate cell size according to the growth conditions. We experimentally validated the model predictions using mutants with defects in the upper part of glycolysis or glucose transport. Plugging in molecular details of cellular subsystems allowed us to refine predictions from the cellular to the molecular level. Our hybrid multiscale modeling approach provides a framework for structuring molecular knowledge and predicting cell phenotypes under various genetic and environmental conditions.

Original languageEnglish
Article numbere1013296
Pages (from-to)1-33
Number of pages33
JournalPLoS Computational Biology
Volume21
Issue number7
Early online date21 Jul 2025
DOIs
Publication statusPublished - Jul 2025

Bibliographical note

Publisher Copyright:
© 2025 Vanoni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding

Funding: This work was supported by grants to the ISBE-SYSBIO infrastructure to LA and MV. The study also received financial support from the Italian Ministry of University and Research (MIUR) through the grant "Dipartimenti di Eccellenza - 2017" to the University of Milano-Bicocca, Department of Biotechnology and Biosciences, coordinated by MV, and from ”ELIXIRxNextGenerationIT” (Code IR0000010)-CUP B53C22001800006 to MV. F. P. and S. B. were partially supported by ISBE-SYSBIO. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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