gcFront

gcFront identifies and optimizes gene knockout (KO) combinations to generate growth-coupled microbial cell factory designs that enforce obligatory chemical synthesis during maximum cell growth.

Key Features:

• Multi-Objective Optimization: Uses a genetic algorithm to search KO combinations that maximize cell growth rate, product synthesis, and coupling strength simultaneously.
• Pareto Front Exploration: Identifies a Pareto front of optimal designs providing alternative trade-offs between growth, synthesis, and coupling objectives.
• Coupling Strength Measure: Incorporates a quantitative coupling strength metric to prioritize designs where product synthesis is tightly linked to growth.
• Speed and Efficiency: Employs an algorithmic search strategy that can identify growth-coupled designs within minutes.

Scientific Applications:

• Synthetic Biology: Enables design of microbial strains with enforced product synthesis during maximal growth for engineered phenotypes.
• Metabolic Engineering: Guides selection of gene KOs to redirect metabolic fluxes toward target chemical production while maintaining growth.
• Industrial Bioproduction: Supports development of robust cell factories for production of chemicals, biofuels, and pharmaceuticals by identifying growth-coupled production strategies.

Methodology:

gcFront leverages genome-scale metabolic models to simulate potential KOs and assess their metabolic impact, applies an iterative genetic algorithm for multi-objective optimization, and outputs Pareto optimal designs.