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Kreutz, C.* ; Raue, A. ; Kaschek, D.* ; Timmer, J.*

Profile likelihood in systems biology.

FEBS J. 280, 2564-2571 (2013)
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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Inferring knowledge about biological processes by a mathematical description is a major characteristic of Systems Biology. To understand and predict system's behavior the available experimental information is translated into a mathematical model. Since the availability of experimental data is often limited and measurements contain noise, it is essential to appropriately translate experimental uncertainty to model parameters as well as to model predictions. This is especially important in Systems Biology because typically large and complex models are applied and therefore the limited experimental knowledge might yield weakly specified model components. Likelihood profiles have been recently suggested and applied in the Systems Biology for assessing parameter and prediction uncertainty. In this article, the profile likelihood concept is reviewed and the potential of the approach is demonstrated for a model of the erythropoietin (EPO) receptor.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Parameter Estimation ; Prediction ; Profile Likelihood ; Propagation Of Errors ; Uncertainty; Identifiability Analysis ; Predictions ; Reveals ; Models ; Range
Sprache englisch
Veröffentlichungsjahr 2013
HGF-Berichtsjahr 2013
ISSN (print) / ISBN 1742-464X
e-ISSN 1742-4658
Zeitschrift FEBS Journal, The
Quellenangaben Band: 280, Heft: 11, Seiten: 2564-2571 Artikelnummer: , Supplement: ,
Verlag Wiley
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)
POF Topic(s) 30505 - New Technologies for Biomedical Discoveries
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503700-004
PubMed ID 23581573
DOI 10.1111/febs.12276
WOS ID WOS:000319413400002
Scopus ID 84878233809
Erfassungsdatum 2013-06-29
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