Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
Inferring signaling pathways using interventional data
Intelligent Data Analysis
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Motivation: Cellular signaling pathways, which are not modulated on a transcriptional level, cannot be directly deduced from expression profiling experiments. The situation changes, when external interventions such as RNA interference or gene knock-outs come into play. Even if the expression of the signaling genes is not changed, secondary effects in downstream genes shed light on the pathway, and allow partial reconstruction of its topology. Results: We introduce an algorithm to infer non-transcriptional pathway features based on differential gene expression in silencing assays. We demonstrate the power of our algorithm in the controlled setting of simulation studies, and explain its practical use in the context of an RNA interference dataset investigating the response to microbial challenge in Drosophila melanogaster. Contact: florian.markowetz@molgen.mpg.de