ArticlePloS one2018
Determining minimal output sets that ensure structural identifiability.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective.PloS one · 2025Article
- Structural identifiability of biomolecular controller motifs with and without flow measurements as model output.PLoS computational biology · 2023Article
- Article
- Mathematical Modelling in Plant Synthetic Biology.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Assessing the role of initial conditions in the local structural identifiability of large dynamic models.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: "Which experimental outputs should be measured to ensure that unique model parameters can be calculated?". Stated formally, we examine the topic of minimal output sets that guarantee a model's structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times.
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