Predicting Metabolic Adaptation Under Dynamic Substrate Conditions Using a Resource-Dependent Kinetic Model: A Case...

Area of Science:

• Systems Biology
• Metabolic Engineering
• Microbial Physiology

Background:

• Microorganisms adapt phenotypes, including metabolism, to survive environmental changes.
• Resource allocation models predict proteome under steady-state, but lack enzyme kinetics for dynamic environments.

Purpose of the Study:

• Develop a resource-dependent kinetic model for microbial adaptation.
• Investigate proteome allocation in *Saccharomyces cerevisiae* under dynamic conditions.
• Understand the Crabtree effect and cellular robustness.

Main Methods:

• Combined published kinetic models and calibrated parameters to proteomics/fluxomics data.
• Applied the model to *Saccharomyces cerevisiae* under chemostat and dynamic substrate conditions.
• Evaluated model predictions against experimental proteome data.

Main Results:

• Predicted specific proteomes at different dilution rates.
• The Crabtree effect is linked to mitochondrial constraints, not total proteome limits.
• Dynamic conditions shift proteome allocation, increasing space for growth and the trehalose cycle for robustness.
• Model accurately predicts major proteome adaptation trends.

Conclusions:

• The developed model accurately predicts microbial proteome adaptation to dynamic environments.
• Cellular "overcapacity" enhances robustness to environmental perturbations.
• The approach can be extended to optimize production organisms and industrial processes.