The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments
Open accessAbstract
Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.
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Citations
Cites
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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
Dynamic flux balance analysis of diauxic growth in Escherichia coli
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Background
Cited by
Gene regulatory network inference: Data integration in dynamic models—A review
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755
Zhang, X., Trame, M., Lesko, L., & Schmidt, S. (2015) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.6 · Cited by 467
“, genes or proteins and their known interactions), and work their way up to the patientphenotype level.(5,6) Both approaches have advantages and limitations, which have given rise to hybrid approaches such as pharmacokinetic/enhanced-pharmacodynamic (PK/ePD) models, that represent a synergy between the two approaches.”
BackgroundChen, W. W., Schoeberl, B., Jasper, P. J., Niepel, M., Nielsen, U. B., Lauffenburger, D. A., & Sorger, P. K. (2009) · Molecular Systems Biology · doi:10.1038/msb.2008.74 · Cited by 391
Systems biology: parameter estimation for biochemical models
Ashyraliyev, M., Fomekong‐Nanfack, Y., Kaandorp, J. A., & Blom, J. G. (2009) · The FEBS Journal · doi:10.1111/j.1742-4658.2008.06844.x · Cited by 323
An iterative identification procedure for dynamic modeling of biochemical networks
Balsa-Canto, E., Alonso, A. A., & Banga, J. R. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-11 · Cited by 202
Metadata
- Authors
- van Riel, N. A. W.
- Venue
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-09-26
- Volume
- 7
- Issue
- 4
- Pages
- 364-374
- ISSN
- 1467-5463 (ISSN-L)
- 1477-4054
- OpenAlex
- W2103838923
- Cited by
- 254
- References
- 16
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
