BMSS2

BMSS2 facilitates systematic model selection and identifiability analysis of kinetic models for synthetic biology applications.

Key Features:

• Implementation: Implemented as a unified Python package.
• Model Development and Simulation: Provides tools for developing kinetic models and simulating system dynamics.
• Bayesian Parameter Inference: Performs Bayesian parameter inference to estimate model parameters with quantified uncertainty.
• Global Sensitivity Analysis: Includes global sensitivity analysis to identify parameters that strongly influence model behavior.
• Model Selection: Supports systematic comparison and selection of competing models based on predefined criteria.
• Identifiability Analysis: Supports both a priori and a posteriori identifiability analyses to assess parameter determinability from data.
• Database-Driven Architecture: Stores and retrieves models in SBML format within a database-driven framework.
• MBase Integration: Integrates with the MBase repository for model deposition and access.

Scientific Applications:

• Kinetic modeling in synthetic biology: Development and refinement of kinetic models to capture system dynamics and predict behavior.
• Experimental design and parameter estimation: Guiding experimental design and data collection using identifiability and sensitivity analyses to improve parameter estimation.
• Model sharing and reuse: Deposition, retrieval, and reuse of SBML models via MBase to support reproducibility and collaboration.

Methodology:

Uses simulation, Bayesian parameter inference, global sensitivity analysis, systematic model selection, a priori and a posteriori identifiability analyses, and database storage/retrieval of SBML models with integration to MBase.