The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Comparative study of parameter sensitivity analyses of the TCR-activated Erk-MAPK signalling pathway
Abstract
Parameter estimation is a major challenge for mathematical modelling of biological systems. Given the uncertainties associated with model parameters, it is important to understand how sensitive the model output is to variations in parameter values. A local sensitivity analysis determines the model sensitivity to parameter variations over a localised region around the nominal parameter values, whereas a global sensitivity analysis (GSA) investigates the sensitivity over the entire parameter space. Using a T-cell receptor-activated Erk-MAPK signalling pathway model as an example, the authors present a comparative study of a variety of different sensitivity analysis techniques. These techniques include: local sensitivity analysis, existing GSA methods of partial rank correlation coefficient, Sobol's, extended Fourier amplitude sensitivity test, as well as a weighted average of local sensitivities and a new GSA method to extract global parameter sensitivities from a parameter identification routine. Results of this study revealed critical reactions in the signalling pathway and their impact on the signalling dynamics and provided insights into embedded regulatory mechanisms such as feedback loops in the pathway. From this study, a recommendation emerges for a general sensitivity analysis strategy to efficiently and reliably infer quantitative, dynamic as well as topological properties from systems biology models.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Despite being intuitive, results from such an analysis should be considered with care as different parameter sets can lead to different sensitivities [55,56].”
From Parameter uncertainty in biochemical models described by ordinary differential equations (2013) · cites this paper as [55]
Citations
Cites
(2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,923
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 8,270
Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates
Sobol′, I. M. (2001) · Mathematics and Computers in Simulation · doi:10.1016/s0378-4754(00)00270-6 · Cited by 6,389
A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output
Saltelli, A., Tarantola, S., & Chan, K. P.-S. (1999) · Technometrics · doi:10.1080/00401706.1999.10485594 · Cited by 2,275
A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output
Saltelli, A., Tarantola, S., & Chan, K. (1999) · Technometrics · doi:10.2307/1270993 · Cited by 1,435
Blower, S. M., & Dowlatabadi, H. (1994) · International Statistical Review / Revue Internationale de Statistique · doi:10.2307/1403510 · Cited by 1,358
The Regulation of Cellular Systems
Heinrich, R., & Schuster, S. (1996) · Springer eBooks · doi:10.1007/978-1-4613-1161-4 · Cited by 1,193
Identification and Review of Sensitivity Analysis Methods
Christopher Frey, H., & Patil, S. R. (2002) · Risk Analysis · doi:10.1111/0272-4332.00039 · Cited by 1,036
Receptors; models for binding, trafficking and signaling
(1997) · General Pharmacology The Vascular System · doi:10.1016/s0306-3623(97)90087-2 · Cited by 865
Sensitivity Analysis in Chemical Kinetics
RABITZ, H. A., Kramer, M. A., & Dacol, D. K. (1983) · Annual Review of Physical Chemistry · doi:10.1146/annurev.pc.34.100183.002223 · Cited by 538
TCR ligand discrimination is enforced by competing ERK positive and SHP-1 negative feedback pathways
Štefanová, I., Hemmer, B., Vergelli, M., Martin, R., Biddison, W. E., & Germain, R. N. (2003) · Nature Immunology · doi:10.1038/ni895 · Cited by 465
Receptors: Models for Binding, Trafficking, and Signaling
Lauffenburger, D. A., & Linderman, J. J. (1993) · doi:10.1093/oso/9780195064667.001.0001 · Cited by 429
Staging and resetting T cell activation in SMACs
Freiberg, B. A., Kupfer, H., Maslanik, W., Delli, J., Kappler, J., Zaller, D. M., & Kupfer, A. (2002) · Nature Immunology · doi:10.1038/ni836 · Cited by 349
Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis
Bentele, M., Lavrik, I., Ulrich, M., Stößer, S., Heermann, D. W., Kalthoff, H., Krammer, P. H., & Eils, R. (2004) · The Journal of Cell Biology · doi:10.1083/jcb.200404158 · Cited by 319
Brightman, F. A., & Fell, D. A. (2000) · FEBS Letters · doi:10.1016/s0014-5793(00)02037-8 · Cited by 247
Cacuci, D. G., & Ionescu‐Bujor, M. (2004) · Nuclear Science and Engineering · doi:10.13182/04-54cr · Cited by 215
Cho, K.-H., Shin, S.-Y., Kolch, W., & Wolkenhauer, O. (2003) · SIMULATION · doi:10.1177/0037549703040943 · Cited by 172
Protein tyrosine phosphatases in T cell physiology
Mustelin, T., Alonso, A., Bottini, N., Huynh, H., Rahmouni, S., Nika, K., Louis-Dit-Sully, C., Tautz, L., Togo, S. H., Bruckner, S. R., et al. (2004) · Molecular Immunology · doi:10.1016/j.molimm.2004.04.015 · Cited by 89
Zheng, Y., Balakrishnan, V., Buzzard, G. T., Geahlen, R. L., Harrison, M. L., & Rundell, A. E. (2005) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2004.08.015 · Cited by 7
Self-citation
Research works printed but not linked: 13
- Rabitz, 1983, 34, 419
- Kinzer-Ursem, 2005, Proc. Conf. Biocomplexity
- Zi, 2005, 579, 1101
- Bentele, 2004, 166, 839
- Zhang, Y.: ‘A systems biology study to deliniate the T cell receptor-activated Erk-MAPK signalling pathway’, (Purdue University, West Lafayette, IN, USA 2005),Ph.D Thesis
- Brightman, 2000, 482, 169
- Blower, 1994, 62, 229
- Mckay, 1979, 21, 239
- Sobol, 2001, 55, 271
- Mustelin, 2004, 41, 687
- Veillette, 2002, 20, 669
- Stefanova, 2003, 4, 248
- Freiberg, 2002, 3, 911
Cited by
Sensitivity analysis approaches applied to systems biology models
Zi, Z. (2011) · IET Systems Biology · doi:10.1049/iet-syb.2011.0015 · Cited by 406
van Riel, N. A. W. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbl040 · Cited by 254
Computational methods for discovering gene networks from expression data
Lee, W.-P., & Tzou, W. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp028 · Cited by 229
Parameter uncertainty in biochemical models described by ordinary differential equations
Vanlier, J., Tiemann, C. A., Hilbers, P. A. J., & van Riel, N. A. W. (2013) · Mathematical Biosciences · doi:10.1016/j.mbs.2013.03.006 · Cited by 116
Sensitivity analysis: from model parameters to system behaviour
Ingalls, B. (2008) · Essays in Biochemistry · doi:10.1042/bse0450177 · Cited by 105
Metadata
- Authors
- Zheng, Y., & Rundell, A.
- Affiliations
- Purdue University West Lafayette (US)
- Venue
- IEE Proceedings - Systems Biology
- Published
- 2006
- Volume
- 153
- Issue
- 4
- Pages
- 201
- ISSN
- 1741-2471 (ISSN-L)
- OpenAlex
- W2124630954
- Cited by
- 121
- References
- 19
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 5 · 2022: 6 · 2021: 4 · 2020: 4 · 2019: 3 · 2018: 5 · 2017: 7 · 2016: 6 · 2015: 6 · 2014: 9 · 2013: 10 · 2012: 13
- FWCI
- 2.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Systems Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Purdue Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Physical Sciences › Engineering › Computational Mechanics › Probabilistic and Robust Engineering Design
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- parameter sensitivity analysis, global sensitivity analysis, local sensitivity analysis, T cell receptor signaling, partial rank correlation coefficient, Sobol indices, parameter estimation, feedback loops, systems biology modeling
- MeSH terms
- Algorithms, Computer Simulation, Models, Biological, Receptors, Antigen, T-Cell, Sensitivity and Specificity, T-Lymphocytes, MAP Kinase Signaling System, Extracellular Signal-Regulated MAP Kinases
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
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.
