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dc.contributor.author
Jia, Gengjie
dc.contributor.author
Stephanopoulos, Gregory
dc.contributor.author
Gunawan, Rudiyanto
dc.date.accessioned
2019-03-13T13:11:17Z
dc.date.available
2017-06-10T12:31:48Z
dc.date.available
2019-03-13T13:11:17Z
dc.date.issued
2012
dc.identifier.issn
1752-0509
dc.identifier.other
10.1186/1752-0509-6-142
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/61473
dc.identifier.doi
10.3929/ethz-b-000061473
dc.description.abstract
Background An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE). Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified). Results In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates) exceeds that of metabolites (chemical species). Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA) models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented. Conclusions The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Incremental parameter estimation
en_US
dc.subject
Parameter subspace
en_US
dc.subject
Error minimizations of concentration and slope
en_US
dc.subject
GMA model
en_US
dc.title
Incremental parameter estimation of kinetic metabolic network models
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
dc.date.published
2012-11-21
ethz.journal.title
BMC Systems Biology
ethz.journal.volume
6
en_US
ethz.journal.abbreviated
BMC syst. biol.
ethz.pages.start
142
en_US
ethz.size
25 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03898 - Gunawan, Rudiyanto (ehemalig)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03898 - Gunawan, Rudiyanto (ehemalig)
ethz.date.deposited
2017-06-10T12:33:44Z
ethz.source
ECIT
ethz.identifier.importid
imp593650351f29e60978
ethz.ecitpid
pub:97929
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-19T01:38:30Z
ethz.rosetta.lastUpdated
2019-03-13T13:11:22Z
ethz.rosetta.exportRequired
false
ethz.rosetta.versionExported
true
ethz.COinS
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