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
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
Kitano, H. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1471 · Cited by 2,278
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
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874 · cited 1×
“quantification of insulin sensitivity [75]).”
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
Mahadevan, R., Edwards, J. S., & Doyle, F. J. (2002) · Biophysical Journal · doi:10.1016/s0006-3495(02)73903-9 · Cited by 945
Parameter estimation in biochemical pathways: a comparison of global optimization methods
Moles, C. G., Mendes, P., & Banga, J. R. (2003) · Genome Research · doi:10.1101/gr.1262503 · Cited by 858
Modeling the Heart--from Genes to Cells to the Whole Organ
Noble, D. (2002) · Science · doi:10.1126/science.1069881 · Cited by 622
Kell, D. B., & Oliver, S. G. (2004) · BioEssays · doi:10.1002/bies.10385 · Cited by 535
Interlinked Fast and Slow Positive Feedback Loops Drive Reliable Cell Decisions
Brandman, O., Ferrell, J. E., Li, R., & Meyer, T. (2005) · Science · doi:10.1126/science.1113834 · Cited by 466 · cited 1×
“Many efforts pursue realistic large-scale complex models, but simplified models show to be valuable in terms of understanding the essential and/or qualitative behaviour of biological systems [10, 34, 40].”
Background
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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
Parameter estimation and optimal experimental design
Banga, J. R., & Balsa-Canto, E. (2008) · Essays in Biochemistry · doi:10.1042/bse0450195 · Cited by 141
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
Friedrich, C. (2016) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12056 · Cited by 87
Wearable sweat sensing for prolonged, semicontinuous, and nonobtrusive health monitoring
Moonen, E. J. M., Haakma, J. R., Peri, E., Pelssers, E., Mischi, M., & den Toonder, J. M. J. (2020) · VIEW · doi:10.1002/viw.20200077 · Cited by 86
Stimulus Design for Model Selection and Validation in Cell Signaling
Apgar, J. F., Toettcher, J. E., Endy, D., White, F. M., & Tidor, B. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0040030 · Cited by 79
Parameter identifiability of power-law biochemical system models
Srinath, S., & Gunawan, R. (2010) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2010.02.019 · Cited by 68
A cooperative strategy for parameter estimation in large scale systems biology models
Villaverde, A. F., Egea, J. A., & Banga, J. R. (2012) · BMC Systems Biology · doi:10.1186/1752-0509-6-75 · Cited by 56
A new strategy for assessing sensitivities in biochemical models
Sahle, S., Mendes, P., Hoops, S., & Kummer, U. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2008.0108 · Cited by 44
He, F., Brown, M., & Yue, H. (2010) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.1558 · Cited by 24
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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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.
