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dc.contributor.author
Mélykúti, Bence
dc.contributor.author
August, Elias
dc.contributor.author
Papachristodoulou, Antonis
dc.contributor.author
El-Samad, Hana
dc.date.accessioned
2019-04-24T12:55:05Z
dc.date.available
2017-06-09T09:22:25Z
dc.date.available
2019-04-24T12:55:05Z
dc.date.issued
2010
dc.identifier.issn
1752-0509
dc.identifier.other
10.1186/1752-0509-4-38
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/30166
dc.identifier.doi
10.3929/ethz-b-000030166
dc.description.abstract
Background The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures. Results In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy) distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input) profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs) to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amœbæ. Conclusions Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems biology approach to experiment design.
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.title
Discriminating between rival biochemical network models: three approaches to optimal experiment design
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
dc.date.published
2010-04-01
ethz.journal.title
BMC Systems Biology
ethz.journal.volume
4
en_US
ethz.journal.abbreviated
BMC syst. biol.
ethz.pages.start
38
en_US
ethz.size
16 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
005468370
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::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
ethz.date.deposited
2017-06-09T09:22:38Z
ethz.source
ECIT
ethz.identifier.importid
imp59364da4e042b10150
ethz.ecitpid
pub:49824
ethz.eth
no
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-08-01T15:12:25Z
ethz.rosetta.lastUpdated
2019-04-24T12:55:36Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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