Dynamic optimization of metabolic networks coupled with gene expression

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Cellular growth relies on metabolic regulation via enzyme expression, especially in dynamic environments.
• Steady-state models fail to capture metabolic adaptations driven by gene expression changes and temporal biomass shifts.

Purpose of the Study:

• To develop a dynamic optimization framework integrating metabolic networks with biomass production and composition dynamics.
• To enable prediction of metabolic adaptations driven by enzyme expression changes.

Main Methods:

• Developed a dynamic optimization framework using timescale separation for metabolic networks.
• Integrated differential equations for substrate and biomass composition with quasi-steady state metabolic constraints.
• Employed dynamic optimization considering enzyme production costs and capacity, solved via linear programming.

Main Results:

• The framework predicts dynamic changes in metabolic fluxes and biomass composition.
• Applied to a minimal nutrient uptake network, reproducing Monod growth.
• Simulated core metabolic processes, predicting diauxic switches, waste product re-utilization, and nutrient depletion adaptation.

Conclusions:

• The dynamic optimization framework accurately predicts metabolic adaptations driven by enzyme expression.
• This approach offers a powerful tool for understanding and engineering cellular metabolism in changing conditions.