Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing
Journal article, 2022

The accumulation of protein damage is one of the major drivers of replicative ageing, describing a cell’s reduced ability to reproduce over time even under optimal conditions. Reactive oxygen and nitrogen species are precursors of protein damage and therefore tightly linked to ageing. At the same time, they are an inevitable by-product of the cell’s metabolism. Cells are able to sense high levels of reactive oxygen and nitrogen species and can subsequently adapt their metabolism through gene regulation to slow down damage accumulation. However, the older or damaged a cell is the less flexibility it has to allocate enzymes across the metabolic network, forcing further adaptions in the metabolism. To investigate changes in the metabolism during replicative ageing, we developed an multi-scale mathematical model using budding yeast as a model organism. The model consists of three interconnected modules: a Boolean model of the signalling network, an enzyme-constrained flux balance model of the central carbon metabolism and a dynamic model of growth and protein damage accumulation with discrete cell divisions. The model can explain known features of replicative ageing, like average lifespan and increase in generation time during successive division, in yeast wildtype cells by a decreasing pool of functional enzymes and an increasing energy demand for maintenance. We further used the model to identify three consecutive metabolic phases, that a cell can undergo during its life, and their influence on the replicative potential, and proposed an intervention span for lifespan control.


Barbara Maria Schnitzer

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

Linnea Österberg

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

University of Gothenburg

Iro Skopa

University of Gothenburg

Chalmers, Mathematical Sciences

Marija Cvijovic

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 18 7 e1010261

Subject Categories

Cell Biology


Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)





More information

Latest update

8/1/2022 1