Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling

Area of Science:

• Computational biology
• Systems biology
• Metabolic engineering

Background:

• Integrated modeling of metabolism and gene regulation remains a significant challenge.
• Existing constraint-based methods like FBA, rFBA, dFBA, RBA, and deFBA have limitations in simultaneously accounting for macromolecule production costs and regulatory events.
• There is a need for a unified framework to predict metabolic dynamics under both resource constraints and regulatory control.

Purpose of the Study:

• To introduce a novel constraint-based modeling framework, regulatory dynamic enzyme-cost flux balance analysis (r-deFBA).
• To unify dynamic metabolic modeling, cellular resource allocation, and transcriptional regulation within a hybrid discrete-continuous setting.
• To enable prediction of metabolic dynamics considering both macromolecule costs and regulatory events.

Main Methods:

• Development of the r-deFBA framework, integrating discrete regulatory states with continuous metabolic dynamics.
• Formulation of the underlying dynamic optimization problem as a mixed-integer linear optimization problem.
• Utilizing efficient solvers for mixed-integer linear optimization to analyze the model.

Main Results:

• r-deFBA successfully unifies dynamic metabolism, cellular resource allocation, and transcriptional regulation.
• The framework predicts discrete regulatory states alongside continuous dynamics of fluxes, substrates, enzymes, and regulatory proteins.
• Demonstrates the capability to predict cellular behavior towards objectives like biomass maximization over time.

Conclusions:

• r-deFBA represents a significant advancement in constraint-based modeling for systems biology.
• The unified approach allows for more realistic and comprehensive predictions of cellular metabolism and regulation.
• The method provides a powerful tool for understanding and engineering biological systems.