# Benchmarking tools for a priori identifiability analysis

arXiv: https://arxiv.org/abs/2207.09745 · HTML: https://mlsanity.com/paper/2207.09745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Structure is information: structural identifiability mappings for machine learning with partially observed dynamical systems](https://mlsanity.com/paper/2502.04131/) (2025)

## Abstract (arXiv)

The structural identifiability and the observability of a model determine the possibility of inferring its parameters and states by observing its outputs. These properties should be analysed before attempting to calibrate a model. Unfortunately, such textita priori analysis can be challenging, since it requires symbolic calculations that often have a high computational cost. In recent years a number of software tools have been developed for this task, mostly in the systems biology community but also in other disciplines. These tools have vastly different features and capabilities, and a critical assessment of their performance is still lacking. Here we present a comprehensive study of the computational resources available for analysing structural identifiability. We consider 12 software tools developed in 7 programming languages (Matlab, Maple, Mathematica, Julia, Python, Reduce, and Maxima), and evaluate their performance using a set of 25 case studies created from 21 models. Our results reveal their strengths and weaknesses, provide guidelines for choosing the most appropriate tool for a given problem, and highlight opportunities for future developments.