STRIKE-GOLDD

STRIKE-GOLDD analyzes structural identifiability and observability of dynamic models represented by ordinary differential equations (ODEs), determining whether model parameters are uniquely identifiable and system states are observable from measurements with known or unknown inputs.


Key Features:

  • Model support: Analyzes linear and non-linear ODE models, including both rational and non-rational formulations.
  • Input handling: Handles scenarios with known inputs and with unknown inputs during identifiability and observability analysis.
  • Analyses performed: Determines structural identifiability of parameters and observability of system states from specified measurements.
  • Algorithms: Implements the ProbObsTest algorithm (tailored for rational models) and the FISPO algorithm for general models.
  • Computational performance: Includes ProbObsTest to provide substantial speed improvements over FISPO for computationally intensive rational models.
  • Implementation: Provided as a Matlab toolbox for execution of the described algorithms on ODE models.

Scientific Applications:

  • Systems biology: Assessment of parameter identifiability and state observability in mechanistic models of biological systems.
  • Pharmacokinetics: Evaluation of identifiability and observability for pharmacokinetic models represented by ODEs.

Methodology:

Performs structural identifiability and observability tests on ODE models using the ProbObsTest algorithm for rational models and the FISPO algorithm for general models, accommodating linear/non-linear and rational/non-rational formulations and known/unknown inputs.

Topics

Details

License:
GPL-3.0
Cost:
Free of charge
Tool Type:
library
Operating Systems:
Mac, Linux, Windows
Programming Languages:
MATLAB
Added:
2/10/2023
Last Updated:
11/24/2024

Operations

Publications

Díaz-Seoane S, Rey Barreiro X, Villaverde AF. STRIKE-GOLDD 4.0: user-friendly, efficient analysis of structural identifiability and observability. Bioinformatics. 2022;39(1). doi:10.1093/bioinformatics/btac748. PMID:36398887. PMCID:PMC9805590.

PMID: 36398887
PMCID: PMC9805590
Funding: - PREDYCTBIO: ED431F 2021/003, MCIN/AEI/10.13039/501100011033, PID2020-113992RA-I00, RYC-2019-027537-I