MetaPlab

MetaPlab provides an integrated, modular framework for designing, managing, and analyzing biological models to understand and predict system behavior under external stimuli, environmental changes, or structural modifications.

Key Features:

• Modular architecture: A central module with an extensible plugin suite supports integration of analysis components and model extensions.
• Modeling framework: Uses metabolic P systems to represent complex biochemical processes.
• Mathematical representation: Implements finite difference recurrent equations to describe system dynamics.
• Evolutionary algorithm: Identifies flux regulation functions expressed as linear combinations of predefined primitive functions.
• Parameter estimation: Employs a reformulated least squares method tailored for estimating parameters across multiple reactions simultaneously.
• Flux regulator analysis: Supports identification and detailed analysis of flux regulators and regulatory mechanisms.
• Data integration: Facilitates data preparation and incorporation of prior knowledge for model construction and analysis.
• Model design and management: Provides integrated capabilities for designing, managing, and analyzing biological models.

Scientific Applications:

• System behavior prediction: Predicts biological system responses to external stimuli, environmental changes, and structural modifications.
• Regulatory mechanism discovery: Uncovers logic and regulatory mechanisms underlying observed biological dynamics.
• Dynamic parameter inference: Estimates reaction parameters across multiple reactions to capture intricate system dynamics.
• Flux regulation identification: Identifies flux regulation functions governing metabolic or biochemical fluxes.

Methodology:

Uses metabolic P systems modeled with finite difference recurrent equations; applies an evolutionary algorithm to identify flux regulation functions as linear combinations of predefined primitive functions and a reformulated least squares method for simultaneous multi-reaction parameter estimation.