Academic Commons

Articles

Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information

Giorgi, Federico M.; Lopez, Gonzalo; Woo, Jung H.; Bisikirska, Brygida; Califano, Andrea; Bansal, Mukesh

Systematic, high-throughput dissection of causal post-translational regulatory dependencies, on a genome wide basis, is still one of the great challenges of biology. Due to its complexity, however, only a handful of computational algorithms have been developed for this task. Here we present CINDy (Conditional Inference of Network Dynamics), a novel algorithm for the genome-wide, context specific inference of regulatory dependencies between signaling protein and transcription factor activity, from gene expression data. The algorithm uses a novel adaptive partitioning methodology to accurately estimate the full Condition Mutual Information (CMI) between a transcription factor and its targets, given the expression of a signaling protein. We show that CMI analysis is optimally suited to dissecting post-translational dependencies. Indeed, when tested against a gold standard dataset of experimentally validated protein-protein interactions in signal transduction networks, CINDy significantly outperforms previous methods, both in terms of sensitivity and precision.

Files

Also Published In

More About This Work

Academic Units
Biomedical Informatics
Systems Biology
Physiology and Cellular Biophysics
Publisher
Public Library of Science
Published Here
June 3, 2016