Inferring Protein Modulation from Gene Expression Data Using Conditional Mutual Information

• Published in: PloS one Vol. 9; no. 10; p. e109569
• Main Authors: Giorgi, Federico M., Lopez, Gonzalo, Woo, Jung H., Bisikirska, Brygida, Califano, Andrea, Bansal, Mukesh
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.10.2014; Public Library of Science (PLoS)
• Subjects: Adaptive algorithms; Algorithms; Bayes Theorem; Bioinformatics; Biology; Biology and Life Sciences; Cancer; Computer and Information Sciences; Computer applications; Epidermal growth factor; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Genomes; Humans; Inference; Information systems; Kinases; Lung cancer; Medical research; Models, Biological; Network topologies; Oligonucleotide Array Sequence Analysis; Phosphorylation; Post-translation; Protein interaction; Proteins; Proteins - genetics; Proteins - metabolism; RNA, Messenger - genetics; RNA, Messenger - metabolism; Sequence Analysis, RNA; Signal transduction; Signaling; Systems Biology; Transcription factors; Transduction; Translation; New York; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0109569

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.