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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Benchmarking tools for a priori identifiability analysis.

Xabier Rey Barreiro¹, Alejandro F Villaverde^1,2

¹Department of Systems and Control Engineering, Universidade de Vigo, 36310 Vigo, Galicia, Spain.

Bioinformatics (Oxford, England)

|February 1, 2023

Summary

This summary is machine-generated.

This study benchmarks 13 software tools for analyzing structural identifiability and observability in dynamic models. It reveals tool strengths and weaknesses, guiding users in selecting appropriate computational resources for model calibration.

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Area of Science:

Systems Biology
Computational Modeling
Control Theory

Background:

Structural identifiability and observability are crucial for dynamic model analysis and calibration.
A priori analysis of these properties can be computationally intensive, necessitating efficient software tools.
Existing software tools vary significantly in features and capabilities, lacking comprehensive performance assessments.

Purpose of the Study:

To conduct a comprehensive evaluation of computational resources for analyzing structural identifiability.
To compare the performance of various software tools across diverse modeling scenarios.
To provide guidance for selecting appropriate tools and identify areas for future development.

Main Methods:

Evaluation of 13 software tools developed in 7 programming languages.
Utilized a benchmark suite comprising 25 case studies derived from 21 distinct models.
Performance assessment focused on computational resources and analytical capabilities.

Main Results:

Identified distinct strengths and weaknesses among the evaluated software tools.
Demonstrated significant performance variations based on model complexity and tool implementation.
Provided empirical data to support tool selection for structural identifiability analysis.

Conclusions:

The study offers valuable insights into the practical performance of identifiability analysis software.
Guidelines are provided to aid researchers in choosing the most suitable tool for their specific modeling tasks.
Highlights opportunities for enhancing existing tools and developing new solutions for more efficient model analysis.