ArticlePLoS computational biology2016
Structural Identifiability of Dynamic Systems Biology Models.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed.
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- Raman-guided sample subset selection for cost-efficient offline calibration in bioprocesses.Bioprocess and biosystems engineering · 2026Article
- Review
- Identifiability of Bayesian models of perception.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Bayesian inference of gene regulatory networks at stochastic steady state.Journal of the Royal Society, Interface · 2026Article
- Cost-Effectiveness of Adult Hepatitis A Vaccination Strategies in Korea Under an Aging Susceptibility Profile.Applied health economics and health policy · 2026Article
- Current research landscape and future prospects of in silico modeling approaches for atopic dermatitis.JID innovations : skin science from molecules to population health · 2026Review
- Host-aware Identification of Intrinsic Gene Expression Biopart Parameters using Combinatorial Libraries.Nature communications · 2026Article
- Mathematical Modelling of the Mitochondrial Dicarboxylate Carrier (SLC25A10).Bulletin of mathematical biology · 2026Article
- Article
- Identifiability in Epidemic Models with Prior Immunity and Under-Reporting.Bulletin of mathematical biology · 2026Article
- Article
- Leveraging mathematical models to predict and control T-cell activation.PLoS computational biology · 2026Article
- A Practical Identifiability Criterion Leveraging Weak-Form Parameter Estimation.Bulletin of mathematical biology · 2026Article
- In Silico Comparison of Rifampicin and 25-desacetyl Rifampicin-Induced PXR-Mediated CYP450 Transcriptional Response in 3D Primary Human Hepatocytes.Bulletin of mathematical biology · 2026Article
- Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State.bioRxiv : the preprint server for biology · 2026Article
- Beyond static biomarkers: systems biology and AI for decoding cancer dynamics.Frontiers in systems biology · 2026Review
- Models of Cellular Mechanosensation.Results and problems in cell differentiation · 2026Review
- Investigation of a Two-Patch Within-Host Model of Hepatitis B Viral Infection.Bulletin of mathematical biology · 2025Article
- Systems modeling and uncertainty quantification of AMP-activated protein kinase signaling.NPJ systems biology and applications · 2025Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas.
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