Inferring protein modulation from gene expression data using conditional mutual information

PLoS One. 2014 Oct 14;9(10):e109569. doi: 10.1371/journal.pone.0109569. eCollection 2014.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bayes Theorem
  • Gene Expression
  • Gene Expression Profiling
  • Gene Expression Regulation
  • Gene Regulatory Networks
  • Humans
  • Models, Biological*
  • Oligonucleotide Array Sequence Analysis
  • Proteins / genetics*
  • Proteins / metabolism
  • RNA, Messenger / genetics
  • RNA, Messenger / metabolism
  • Sequence Analysis, RNA
  • Systems Biology


  • Proteins
  • RNA, Messenger