Modeling the metabolic dynamics at the genome-scale by optimized yield analysis.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• The hybrid cybernetic model (HCM) approach integrates enzyme synthesis and regulation for dynamic modeling in bioreaction engineering.
• Current HCM application on genome-scale metabolic models (GEMs) is computationally intensive due to elementary flux mode (EFM) calculations.

Purpose of the Study:

• To present a novel HCM strategy for genome-scale metabolic dynamics simulation.
• To overcome the computational burden of EFM calculations in HCMs.

Main Methods:

• Developed an optimized yield analysis algorithm (opt-yield-FBA) based on flux balance analysis (FBA).
• Applied the opt-yield-FBA to calculate optimal yield solutions and yield spaces for GEMs.
• Integrated the opt-yield-FBA into the HCM strategy to avoid EFM computations.

Main Results:

• The opt-yield-FBA enables HCM strategy application on genome-scale metabolic networks without EFM calculation.
• Demonstrated the feasibility of simulating metabolic dynamics at the genome-scale.
• Successfully illustrated the strategy by simulating microbial community dynamics.

Conclusions:

• The proposed HCM strategy with opt-yield-FBA provides an efficient method for dynamic modeling of genome-scale metabolic networks.
• This approach facilitates the development of dynamic models for complex microbial systems.
• Offers a computationally feasible alternative to EFM-based methods for metabolic modeling.