ETFBA

ETFBA extends flux balance analysis by incorporating enzyme protein allocation and thermodynamics constraints to enable growth simulation, metabolic flux estimation, and variability analysis under stress conditions.

Key Features:

• Enzyme protein allocation constraints: Integrates enzyme protein costs into genome-scale metabolic modeling.
• Thermodynamics constraints: Incorporates reaction Gibbs free energy changes into flux analysis.
• pH-dependent enzyme kinetics: Dynamically adjusts enzyme kinetics as a function of intracellular pH to simulate metabolic responses to acidification.
• Growth simulation: Simulates organism growth under varying environmental conditions including different external pH levels.
• Metabolic flux estimation: Calculates metabolic flux distributions under specified constraints.
• Variability analysis: Performs analysis of metabolic flux variability under stress conditions.
• Gene knockout simulation: Enables in silico gene knockout analysis for essential gene identification.

Scientific Applications:

• Acid stress response modeling: Simulates Escherichia coli metabolic adaptation to intracellular acidification across varying external pH levels.
• Fermentation product prediction: Replicates metabolic shifts such as lactate production as the primary fermentation product at low pH under anaerobic conditions.
• Protein efficiency analysis: Identifies reduced protein efficiency of metabolic branches under acidic stress, such as the acetyl-CoA branch compared to lactate dehydrogenase.
• Strain optimization target identification: Identifies strategies for enhancing cell growth under acidic anaerobic conditions by improving enzyme activities such as lactate dehydrogenase and pyruvate formate lyase.
• Metabolic bottleneck detection: Pinpoints key metabolic bottlenecks in environmental stress responses.

Methodology:

ETFBA implements a pH-, Enzyme protein allocation-, and Thermodynamics-constrained Flux Balance Analysis framework as a Python package, integrating enzyme protein costs, reaction Gibbs free energy changes, and pH-dependent enzyme kinetics into genome-scale metabolic models.