ArticleBioinformatics (Oxford, England)2023
Benchmarking tools for a priori identifiability analysis.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Article
- Leveraging mathematical models to predict and control T-cell activation.PLoS computational biology · 2026Article
- A virtual cohort framework with applications to adoptive cell therapy in bladder cancer.bioRxiv : the preprint server for biology · 2026Article
- Identification of dynamic models of microbial communities: A workflow addressing identifiability and modeling pitfalls.PLoS computational biology · 2025Article
- A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators.Journal of mathematical biology · 2025Article
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Employing Observability Rank Conditions for Taking into Account Experimental Information a priori.Bulletin of mathematical biology · 2025Article
- MxlPy-Python package for mechanistic learning and hybrid modelling in life science.Bioinformatics advances · 2025Article
- Global and local identifiability analysis of a nonlinear biphasic constitutive model in confined compression.Journal of the Royal Society, Interface · 2024Article
- Identifiability investigation of within-host models of acute virus infection.Mathematical biosciences and engineering : MBE · 2024Article
- Modeling the CD8＋ T cell immune response to influenza infection in adult and aged mice.Journal of theoretical biology · 2024Article
- Identifiability investigation of within-host models of acute virus infection.bioRxiv : the preprint server for biology · 2024Article
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.Frontiers in physiology · 2024Article
- Practical Identifiability of Plant Growth Models: A Unifying Framework and Its Specification for Three Local Indices.Plant phenomics (Washington, D.C.) · 2024Article
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- Structural identifiability of biomolecular controller motifs with and without flow measurements as model output.PLoS computational biology · 2023Article
- Endothelial cells signaling and patterning under hypoxia: a mechanistic integrative computational model including the Notch-Dll4 pathway.bioRxiv : the preprint server for biology · 2023Article
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2 authors.
Funding
Abstract
motivationThe theoretical possibility of determining the state and parameters of a dynamic model by measuring its outputs is given by its structural identifiability and its observability. These properties should be analysed before attempting to calibrate a model, but their a priori analysis can be challenging, requiring 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. These tools have vastly different features and capabilities, and a critical assessment of their performance is still lacking.
resultsHere, we present a comprehensive study of the computational resources available for analysing structural identifiability. We consider 13 software tools developed in 7 programming languages 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. AVAILABILITY AND IMPLEMENTATION: https://github.com/Xabo-RB/Benchmarking_files.
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