Rebecka Jörnsten: Large-scale network modeling, data integration and experimental planning, with applications to cancer genomics

Aberration of a gene's copy number (CNA) is known to affect the mRNA level of that gene. CNAs are also known to be prevalent in cancer genomes. We construct CNA-mRNA network models to investigate the global effects of copy number changes on gene expression.  The network models allow us to identify CNA-hubs that appear to drive the gene expression.  Using regularized regression we uncover network modules that are predictive of clinical outcome (survival).
Time permitting, we will also discuss an experimental planning problem in cancer genomics. We present a novel computational-experimental framework for efficient identification of interacting target pairs (genes or drugs), applicable for screening of large systems (>1000 targets).

This framework exploits the fact that the response of a target pair in a given system can be partly predicted by computational means from (i) a small set of experimentally determined target pairs, and (ii) pre-existing data (e.g. drug-response databases, gene ontology, PPI) on the similarities between targets. We demonstrate the efficiency of the proposed method on several public, gold-standard data sets, and show that our protocol increases the rate of discovery of interactions by up to 7-fold compared with standard screening.