Format

Send to

Choose Destination
Cell Syst. 2018 Dec 26;7(6):567-579.e6. doi: 10.1016/j.cels.2018.10.013. Epub 2018 Nov 28.

Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany; Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany.
2
Alacris Theranostics GmbH, Berlin 12489, Germany; Max Planck Institute for Molecular Genetics, Berlin 14195, Germany.
3
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.
4
KG Jebsen Centre for Psychosis Research, NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo 0450, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo 0450, Norway.
5
Alacris Theranostics GmbH, Berlin 12489, Germany.
6
Max Planck Institute for Molecular Genetics, Berlin 14195, Germany; Dahlem Centre for Genome Research and Medical Systems Biology, Berlin 12489, Germany.
7
Alacris Theranostics GmbH, Berlin 12489, Germany; Max Planck Institute for Molecular Genetics, Berlin 14195, Germany. Electronic address: b.lange@alacris.de.
8
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany; Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching 85748, Germany. Electronic address: jan.hasenauer@helmholtz-muenchen.de.

Abstract

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 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.

KEYWORDS:

biomarker; cancer signaling; drug response; drug synergy; mechanistic modeling; parameter estimation; sequencing data; systems biology

PMID:
30503647
DOI:
10.1016/j.cels.2018.10.013
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center