PottersWheel

PottersWheel provides mechanistic mathematical modeling and data-driven parameter estimation for biochemical reaction networks and other dynamical systems.

Key Features:

• MATLAB toolbox: Implemented as a MATLAB toolbox for mechanistic mathematical modeling.
• Mechanistic modeling: Supports mechanistic mathematical models of biochemical reaction networks and other dynamical systems.
• Multi-experiment fitting: Implements multi-experiment fitting enabling simultaneous fitting of multiple experimental datasets obtained under varying conditions.
• Parameter estimation: Performs estimation of unknown model parameters from experimental data.
• Model validation and hypothesis discrimination: Enables quantitative validation of models and discrimination between competing mechanistic hypotheses.
• Text-based model specification: Accepts text-based model specifications.
• Compiled C code generation: Dynamically generates and compiles C code to accelerate simulations and parameter fitting.
• Data-driven modeling: Facilitates data-based modeling workflows for model calibration against experimental measurements.

Scientific Applications:

• Systems biology modeling: Modeling and analysis of biochemical reaction networks in systems biology.
• Dynamical systems analysis: Investigation of dynamical system behavior and temporal responses.
• Parameter inference across conditions: Inferring model parameters from multiple experiments conducted under varying laboratory conditions.
• Model selection and hypothesis testing: Discriminating between competing mechanistic hypotheses through quantitative model comparison.

Methodology:

Uses mechanistic mathematical models with multi-experiment simultaneous fitting and dynamically generated compiled C code to accelerate simulations and parameter fitting.