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.
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“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]
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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
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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
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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
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