GenSSI

GenSSI performs structural identifiability analysis of ordinary differential equation (ODE) models to determine whether model parameters can be uniquely estimated from experimental data in systems biology.

Key Features:

• SBML import: Supports Systems Biology Markup Language (SBML) import to load ODE models encoded in SBML.
• State/Parameter transformations: Allows transformations of states and parameters to facilitate identifiability analysis.
• Multi-experiment identifiability: Enables structural identifiability assessment across multiple experimental datasets simultaneously.
• MATLAB toolbox implementation: Implemented as a MATLAB toolbox with compatibility across multiple MATLAB versions.
• Improved computational efficiency: Provides increased computational performance to handle more complex models.
• Generating-series approach: Employs generating series methods to test parameter identifiability in ODE models.

Scientific Applications:

• Identifiability analysis of ODE models: Assess structural identifiability of ODE-based mathematical models used in systems biology.
• Parameter uniqueness assessment: Determine whether model parameters can be uniquely estimated from experimental data to support model validation.
• Multi-dataset model analysis: Perform identifiability assessments across multiple datasets to improve robustness when analyzing complex biological systems.

Methodology:

GenSSI generates series expansions to test the structural identifiability of parameters in ODE models.