SurreyFBA

SurreyFBA performs constraint-based modeling and simulation of genome-scale metabolic networks to analyze metabolism, predict gene essentiality, and support metabolic engineering.

Key Features:

• Constraint-Based Simulations: Implements constraint-based simulations of metabolic networks for gene essentiality prediction and metabolic pathway optimization.
• Network Map Visualization: Provides visualization of metabolic network maps and numerical results using the Petri net convention and via charts and plots.
• Minimal Substrate and Product Set Analysis: Implements protocols for analyzing minimal substrate and product sets to inform metabolic engineering and predict nutritional requirements in complex in vivo environments.
• GLPK Solver Integration and C++ Implementation: Integrates the GNU Linear Programming Kit (GLPK) as the linear-programming solver and is implemented in C++ to support efficient iterative LP solver calls.
• Integration with Scripting Languages: Supports integration with scripting languages commonly used in bioinformatics for incorporation into automated workflows.

Scientific Applications:

• Gene Essentiality Prediction: Predicts gene essentiality within genome-scale metabolic network models.
• Metabolic Engineering: Supports design and optimization of metabolic pathways for metabolic engineering applications.
• Predicting Nutritional Requirements: Predicts minimal nutritional requirements in complex in vivo environments via minimal substrate and product set analysis.
• Genome-Scale Metabolic Network Analysis: Enables analysis and simulation of genome-scale metabolic networks for systems biology research.

Methodology:

SurreyFBA employs constraint-based modeling by applying constraints to genome-scale metabolic models, uses the GNU Linear Programming Kit (GLPK) with iterative linear-programming calls, is implemented in C++, and includes protocols for minimal substrate and product set analysis.