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Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation.
Open accessAbstract
MOTIVATION: The simulation of biochemical kinetic systems is a powerful approach that can be used for: (i) checking the consistency of a postulated model with a set of experimental measurements, (ii) answering 'what if?' questions and (iii) exploring possible behaviours of a model. Here we describe a generic approach to combine numerical optimization methods with biochemical kinetic simulations, which is suitable for use in the rational design of improved metabolic pathways with industrial significance (metabolic engineering) and for solving the inverse problem of metabolic pathways, i.e. the estimation of parameters from measured variables. RESULTS: We discuss the suitability of various optimization methods, focusing especially on their ability or otherwise to find global optima. We recommend that a suite of diverse optimization methods should be available in simulation software as no single one performs best for all problems. We describe how we have implemented such a simulation-optimization strategy in the biochemical kinetics simulator Gepasi and present examples of its application. AVAILABILITY: The new version of Gepasi (3.20), incorporating the methodology described here, is available on the Internet at http://gepasi.dbs.aber.ac.uk/softw/Gepasi. html. CONTACT: prm@aber.ac.uk
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Cited by
Metabolomics—the link between genotypes and phenotypes
Fiehn, O. (2002) · Plant Molecular Biology · doi:10.1023/a:1013713905833 · Cited by 3,445
COPASI—a COmplex PAthway SImulator
Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., & Kummer, U. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl485 · Cited by 2,618
Self-citationToni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M. P. H. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0172 · Cited by 1,498
Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261
“On the other hand, collectively fitting parameters [11, 12] by optimizing the agreement between the model and available data often yields large parameter uncertainties [13, 14, 15].”
BackgroundParameter 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
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Metadata
- Authors
- Mendes, P., & Kell, D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 1998-01-01
- Volume
- 14
- Issue
- 10
- Pages
- 869-883
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W1982258483
- Cited by
- 676
- References
- 0
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Mendes, P., & Kell, D. (1998). Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics. https://doi.org/10.1093/bioinformatics/14.10.869
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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