The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
On structural and practical identifiability
Open accessAbstract
We discuss issues of structural and practical identifiability of partially observed differential equations, which are often applied in systems biology. The development of mathematical methods to investigate structural nonidentifiability has a long tradition. Computationally efficient methods to detect and cure it have been developed recently. Practical nonidentifiability, on the other hand, has not been investigated at the same conceptually clear level. We argue that practical identifiability is more challenging than structural identifiability when it comes to modeling experimental data. We discuss that the classical approach based on the Fisher information matrix has severe shortcomings. As an alternative, we propose using the profile likelihood, which is a powerful approach to detect and resolve practical nonidentifiability.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Robustness in the Strategy of Scientific Model Building
Box, G. E. P. (1979) · Robustness in Statistics · doi:10.1016/b978-0-12-438150-6.50018-2 · Cited by 1,562
Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U., & Timmer, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp358 · Cited by 1,442
Self-citationOn the general theory of control systems
Kalman, R. E. (1960) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)70094-8 · Cited by 1,222
Identification of parametric models : from experimental data
Walter, É., & Pronzato, L. (1997) · Cited by 1,197
Sachs, L. (1984) · Springer Series in Statistics · doi:10.1007/978-1-4612-5246-7 · Cited by 973
Bellman, R., & Åström, K. J. (1970) · Mathematical Biosciences · doi:10.1016/0025-5564(70)90132-x · Cited by 871
Fundamental properties of unperturbed haematopoiesis from stem cells in vivo
Busch, K., Klapproth, K., Barile, M., Flossdorf, M., Holland-Letz, T., Schlenner, S. M., Reth, M., Höfer, T., & Rodewald, H.-R. (2015) · Nature · doi:10.1038/nature14242 · Cited by 769
Murphy, S. A., & Van Der Vaart, A. W. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474219 · Cited by 712
On global identifiability for arbitrary model parametrizations
Ljung, L., & Glad, T. (1994) · Automatica · doi:10.1016/0005-1098(94)90029-9 · Cited by 643
On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics
Miao, H., Xia, X., Perelson, A. S., & Wu, H. (2011) · SIAM Review · doi:10.1137/090757009 · Cited by 610
Cited by
A practical guide for the generation of model-based virtual clinical trials
Craig, M., Gevertz, J. L., Kareva, I., & Wilkie, K. P. (2023) · Frontiers in Systems Biology · doi:10.3389/fsysb.2023.1174647 · Cited by 52
Colebank, M. J., & Chesler, N. C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010017 · Cited by 24
Preston, S. P., Wilkinson, R. D., Clayton, R. H., Chappell, M. J., & Mirams, G. R. (2025) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2025.100563 · Cited by 11
“Profile (log)likelihood functions can be used to assess both structural and practical identifiability (Kreutz et al., 2013; Wieland et al., 2021) of an individual parameter θ i .”
(2025) · doi:10.1093/bioinformatics/btaf581 · Cited by 6
Ramírez Mazo, C., Gómez-Echavarría, L., Lema-Pérez, L., & Röblitz, S. (2026) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2026.112518
Metadata
- Authors
- Wieland, F.-G., Hauber, A. L., Rosenblatt, M., Tönsing, C., & Timmer, J.
- Venue
- Current Opinion in Systems Biology
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 25
- Pages
- 60-69
- ISSN
- 2452-3100 (ISSN-L)
- OpenAlex
- W3126293391
- Cited by
- 325
- References
- 76
- FWCI
- 7.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: On structural and practical identifiability. https://app.cochat.ai/papers/10.1016/j.coisb.2021.03.005
- The paper
- Wieland, F.-G., Hauber, A. L., Rosenblatt, M., Tönsing, C., & Timmer, J. (2021). On structural and practical identifiability. Current Opinion in Systems Biology. https://doi.org/10.1016/j.coisb.2021.03.005
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
