TY - JOUR T1 - Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines JF - bioRxiv DO - 10.1101/174094 SP - 174094 AU - Fabian Fröhlich AU - Thomas Kessler AU - Daniel Weindl AU - Alexey Shadrin AU - Leonard Schmiester AU - Hendrik Hache AU - Artur Muradyan AU - Moritz Schütte AU - Ji-Hyun Lim AU - Matthias Heinig AU - Fabian J. Theis AU - Hans Lehrach AU - Christoph Wierling AU - Bodo Lange AU - Jan Hasenauer Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/08/09/174094.abstract N2 - The response of cancer cells to drugs is determined by various factors, including the cells’ mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and 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. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs. ER -