SIAN

SIAN analyzes structural identifiability of ordinary differential equation (ODE) models to determine whether model parameters can be uniquely identified from ideal continuous noise-free data.

Key Features:

• Structural Identifiability Analysis: Performs structural identifiability analysis on ordinary differential equation (ODE) models to assess whether parameters can be uniquely determined.
• Advanced Problem Solving: Addresses complex identifiability issues that were previously unsolvable by other software approaches.
• Implementation: Implemented in Maple, a high-level language and environment for mathematical computation.

Scientific Applications:

• Systems Biology: Evaluates identifiability of ODE models used in systems biology to ensure parameters can be inferred uniquely.
• Pharmacokinetics: Applies to pharmacokinetic ODE models to assess whether model parameters are identifiable for parameter estimation.
• Parameter Estimation for Dynamic Processes: Supports domains requiring precise parameter estimation from dynamic biological processes by identifying non-unique solutions prior to experiments.

Methodology:

Performs a priori analysis of model structure to determine whether unique parameter values can be identified given perfect continuous noise-free data.