Show simple item record

dc.contributor.authorBusetto, AG
dc.contributor.authorHauser, A
dc.contributor.authorKrummenacher, G
dc.contributor.authorSunnåker, M
dc.contributor.authorDimopoulos, S
dc.contributor.authorOng, CS
dc.contributor.authorStelling, J
dc.contributor.authorBuhmann, JM
dc.date.accessioned2021-02-22T23:10:27Z
dc.date.available2021-02-22T23:10:27Z
dc.date.issued2013-10-15
dc.identifierpii: btt436
dc.identifier.citationBusetto, A. G., Hauser, A., Krummenacher, G., Sunnåker, M., Dimopoulos, S., Ong, C. S., Stelling, J. & Buhmann, J. M. (2013). Near-optimal experimental design for model selection in systems biology.. Bioinformatics, 29 (20), pp.2625-2632. https://doi.org/10.1093/bioinformatics/btt436.
dc.identifier.issn1367-4803
dc.identifier.urihttp://hdl.handle.net/11343/265150
dc.description.abstractMOTIVATION: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data. Motivated by biological applications, the method enables the design of crucial experiments: it determines a highly informative selection of measurement readouts and time points. RESULTS: We demonstrate formal guarantees of design efficiency on the basis of previous results. By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations. Moreover, the method exhibits the best polynomial-complexity constant approximation factor, unless P = NP. We measure the performance of the method in comparison with established alternatives, such as ensemble non-centrality, on example models of different complexity. Efficient design accelerates the loop between modeling and experimentation: it enables the inference of complex mechanisms, such as those controlling central metabolic operation. AVAILABILITY: Toolbox 'NearOED' available with source code under GPL on the Machine Learning Open Source Software Web site (mloss.org).
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleNear-optimal experimental design for model selection in systems biology.
dc.typeJournal Article
dc.identifier.doi10.1093/bioinformatics/btt436
melbourne.affiliation.departmentUniversity General
melbourne.affiliation.facultyCollected Works
melbourne.source.titleBioinformatics
melbourne.source.volume29
melbourne.source.issue20
melbourne.source.pages2625-2632
dc.rights.licenseCC BY
melbourne.elementsid591028
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789540
melbourne.contributor.authorOng, Cheng
dc.identifier.eissn1367-4811
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record