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.


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