PolyRound

PolyRound performs rounding transformations that rescale convex polytopes representing constraint sets in metabolic models to achieve uniform width and thereby improve the efficiency and accuracy of random flux sampling.

Key Features:

• Rounding transformation: Rescales convex polytopes to produce approximately uniform widths across dimensions.
• Redundant-constraint removal: Dynamically identifies and removes redundant inequality constraints during the rounding procedure.
• Preprocessing for numerical stability: Simplifies numerical computations involved in constraint-based analysis by conditioning the polytope geometry.
• Improved sampling efficiency and accuracy: Facilitates more efficient and more numerically stable random flux sampling in metabolic networks.
• Model applicability: Applies to convex polytopes that represent constraint sets in genome-scale metabolic models.
• Benchmark performance: Successfully processed all 108 models in the BiGG database without parameter tuning, compared to prior methods that handled about 50% of those models.

Scientific Applications:

• Random flux sampling: Supports uniform and more stable sampling of steady-state flux spaces in metabolic network models.
• Constraint-based analysis: Enables improved numerical preprocessing for flux balance analysis and related constraint-based investigations.
• Model benchmarking and validation: Serves as a preprocessing and evaluation step for genome-scale models, including models from the BiGG database.

Methodology:

Applies a rounding transformation that rescales convex polytopes to achieve uniform width across dimensions and dynamically removes redundant inequality constraints during the rounding procedure to simplify downstream numerical computations.