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dc.date.accessioned2020-02-04T20:23:33Z
dc.date.available2020-02-04T20:23:33Z
dc.date.created2019-03-05T11:56:04Z
dc.date.issued2018
dc.identifier.citationFröhlich, Fabian Kessler, Thomas Weindl, Daniel Shadrin, Alexey A. Schmiester, Leonard Hache, Hendrik Muradyan, Artur Schutte, Moritz Lim, Ji-Hyun Heinig, Matthias Theis, Fabian J. Lehrach, Hans Wierling, Christoph Lange, Bodo Hasenauer, Jan . Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model. Cell Systems. 2018, 7(6), 567-579.e6
dc.identifier.urihttp://hdl.handle.net/10852/72748
dc.description.abstractMechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 104 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways (>1,200 species and >2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.en_US
dc.languageEN
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleEfficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway modelen_US
dc.typeJournal articleen_US
dc.creator.authorFröhlich, Fabian
dc.creator.authorKessler, Thomas
dc.creator.authorWeindl, Daniel
dc.creator.authorShadrin, Alexey A.
dc.creator.authorSchmiester, Leonard
dc.creator.authorHache, Hendrik
dc.creator.authorMuradyan, Artur
dc.creator.authorSchutte, Moritz
dc.creator.authorLim, Ji-Hyun
dc.creator.authorHeinig, Matthias
dc.creator.authorTheis, Fabian J.
dc.creator.authorLehrach, Hans
dc.creator.authorWierling, Christoph
dc.creator.authorLange, Bodo
dc.creator.authorHasenauer, Jan
cristin.unitcode185,53,10,70
cristin.unitnameNORMENT part UiO
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin1682344
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Cell Systems&rft.volume=7&rft.spage=567&rft.date=2018
dc.identifier.jtitleCell Systems
dc.identifier.volume7
dc.identifier.issue6
dc.identifier.startpage567
dc.identifier.endpage579.e6
dc.identifier.doihttps://doi.org/10.1016/j.cels.2018.10.013
dc.identifier.urnURN:NBN:no-75829
dc.type.documentTidsskriftartikkelen_US
dc.type.peerreviewedPeer reviewed
dc.source.issn2405-4712
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/72748/2/Efficient_Parameter_Estimation_Cell_Systems.pdf
dc.type.versionPublishedVersion


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