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Abstract
When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation—known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.
Metabolically active organelles compete for cytosolic space and resources during metabolism rewiring. Here, the authors develop a computational model of yeast metabolism and resource allocation to predict condition- and compartment-specific proteome constraints that govern metabolic strategies.
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1 Technical University of Denmark, Novo Nordisk Foundation Center for Biosustainability, Lyngby, Denmark (GRID:grid.5170.3) (ISNI:0000 0001 2181 8870); Assiut University, Department of Information Systems, Faculty of Computers and Information, Assiut, Egypt (GRID:grid.252487.e) (ISNI:0000 0000 8632 679X)
2 Vrije Universiteit Amsterdam, Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Amsterdam, The Netherlands (GRID:grid.12380.38) (ISNI:0000 0004 1754 9227); Technical University Delft, Department of Industrial Microbiology, Delft, The Netherlands (GRID:grid.5292.c) (ISNI:0000 0001 2097 4740)
3 Vrije Universiteit Amsterdam, Systems Biology Lab, Amsterdam Institute of Molecular and Life Sciences, Amsterdam, The Netherlands (GRID:grid.12380.38) (ISNI:0000 0004 1754 9227)
4 University of Manchester, Division of Evolution & Genomic Sciences, Manchester, UK (GRID:grid.5379.8) (ISNI:0000000121662407)
5 University of Liverpool, Institute of Systems, Molecular and Integrative Biology, Liverpool, UK (GRID:grid.10025.36) (ISNI:0000 0004 1936 8470)
6 Technical University Delft, Department of Industrial Microbiology, Delft, The Netherlands (GRID:grid.5292.c) (ISNI:0000 0001 2097 4740)
7 Technical University of Denmark, Novo Nordisk Foundation Center for Biosustainability, Lyngby, Denmark (GRID:grid.5170.3) (ISNI:0000 0001 2181 8870)
8 Technical University of Denmark, Novo Nordisk Foundation Center for Biosustainability, Lyngby, Denmark (GRID:grid.5170.3) (ISNI:0000 0001 2181 8870); Chalmers University of Technology, Department of Biology and Biological Engineering, Gothenburg, Sweden (GRID:grid.5371.0) (ISNI:0000 0001 0775 6028)