A predictive dynamic yeast model based on component, energy, and electron carrier balances

Area of Science:

• Biochemistry
• Systems Biology
• Metabolic Engineering

Background:

• Yeast fermentation is crucial in biotechnology but predicting metabolic pathway flux remains challenging.
• Existing models often lack the predictive power to capture dynamic pathway shifts under varying conditions.

Purpose of the Study:

• To develop and validate a novel yeast model for predicting fermentation outcomes.
• To elucidate the influence of energy and electron carriers on metabolic pathway partitioning.

Main Methods:

• A new computational model integrating yeast metabolic pathways, energy (ATP/ADP), and electron carriers (NAD+/NADH).
• Model parameterization using stoichiometric coefficients from literature and adjustment against experimental data.
• Validation using experimental data from glucose fermentation in a non-aerated batch system.

Main Results:

• The model accurately predicts yeast fermentation on glucose in a non-aerated batch system.
• Demonstrates that energy (ATP) and electron carriers (NAD+) are key triggers for metabolic pathway activation.
• Highlights the role of mitochondrial respiration independent of the TCA cycle in yeast metabolism.

Conclusions:

• The developed model offers improved prediction of yeast fermentation dynamics.
• Provides insights into the regulatory roles of cellular energy and redox balance in metabolic pathway selection.
• Enhances understanding of yeast metabolic flexibility and mitochondrial function.