Kinetic sampling shows the effect of medium composition on metabolic control in Saccharomyces cerevisiae

TL;DR

This study shows how the environment affects yeast metabolism using a new computational method that is efficient and uses real data.

Contribution

A novel framework for metabolic control analysis that uses sampled parameters and omics data without requiring detailed model parameters.

Findings

Flux control patterns in yeast metabolism shift depending on nutrient limitations.

The framework successfully incorporates allosteric effectors and uses single steady-state omics data.

The method is computationally efficient, sampling 100 feasible models in under 20 minutes.

Abstract

Metabolic control analysis is used to understand regulation of metabolism and identify bottlenecks to be overcome in metabolic engineering for desired products. Its application has been hampered by the need for either parameterized models or carefully titrated experiments. In this study, we use thermodynamically feasible, sampled parameters to overcome this limitation. We use metabolic control analysis to explore central carbon metabolism of Saccharomyces cerevisiae growing in continuous culture under different nutrient limitations. Furthermore, we demonstrate shifts in flux control patterns in response to the different growth conditions and show how our results for specific reactions agree with the literature. Key advantages of the proposed framework include the incorporation of allosteric effectors, the use of omics data from a single steady-state time point and the computational efficiency; in all cases, 100 feasible models were sampled in less than 20 min on a laptop. The model and framework are freely available for researchers to use on their own data: https://github.com/biosustain/GRASP.git.

The online version contains supplementary material available at 10.1186/s12934-025-02884-w.

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