SBbadger

SBbadger generates synthetic biochemical reaction and metabolic networks with user-defined structural and kinetic properties to produce benchmark models for statistical analysis of computational biology methods.

Key Features:

• Python-based implementation: Implemented in Python to provide the computational framework for network generation.
• Synthetic network generation: Produces synthetic biochemical reaction and metabolic network models.
• User-Defined Degree Distributions: Allows specification of degree distributions so generated networks can mimic the structural diversity observed in biological systems.
• Multiple Kinetic Formalisms: Supports multiple kinetic formalisms for modeling different biochemical processes.
• Customizable Network Properties: Exposes a range of definable network properties beyond degree distributions and kinetics.
• Benchmark model generation: Creates benchmark models that reflect properties of natural biochemical systems for method evaluation.

Scientific Applications:

• Benchmarking and method evaluation: Generates benchmark networks for evaluation and comparison of computational methods.
• Inference, optimization, and simulation: Supports development and testing of inference, optimization, and simulation approaches for biochemical reaction networks.
• Statistical analysis of network properties: Facilitates rigorous statistical analysis of synthetic networks that resemble biological systems.

Methodology:

Implements a computational and algorithmic workflow to produce diverse synthetic network models; the tool's performance has been demonstrated under various settings.