PuSH - Publication Server of Helmholtz Zentrum München

Fröhlich, F. ; Kessler, T.* ; Weindl, D. ; Shadrin, A.* ; Schmiester, L. ; Hache, H.* ; Muradyan, A.* ; Schütte, M.* ; Lim, J.H.* ; Heinig, M. ; Theis, F.J. ; Lehrach, H.* ; Wierling, C.* ; Lange, B.* ; Hasenauer, J.

Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model.

Cell Syst. 7, 567-579 (2018)
Publ. Version/Full Text Research data DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Mechanistic 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 10 4 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.
Additional Metrics?
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Keywords Biomarker ; Cancer Signaling ; Drug Response ; Drug Synergy ; Mechanistic Modeling ; Parameter Estimation ; Sequencing Data ; Systems Biology; Systems Biology; Sensitivity; Discovery; Resource; Metrics
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Journal Cell Systems
Quellenangaben Volume: 7, Issue: 6, Pages: 567-579 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Maryland Heights, MO