Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing
Artikel i vetenskaplig tidskrift, 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.

Författare

Barbara Maria Schnitzer

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Linnea Österberg

Chalmers, Biologi och bioteknik, Systembiologi

Göteborgs universitet

Iro Skopa

Göteborgs universitet

Chalmers, Matematiska vetenskaper

Marija Cvijovic

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

PLoS Computational Biology

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

Vol. 18 7 e1010261

Ämneskategorier

Cellbiologi

Biofysik

Medicinsk bioteknologi (med inriktning mot cellbiologi (inklusive stamcellsbiologi), molekylärbiologi, mikrobiologi, biokemi eller biofarmaci)

DOI

10.1371/journal.pcbi.1010261

PubMed

35797415

Mer information

Senast uppdaterat

2022-08-01