  Background: Elucidating gene regulatory networks is crucial for understanding
normal cell physiology and complex pathologic phenotypes. Existing
computational methods for the genome-wide ``reverse engineering'' of such
networks have been successful only for lower eukaryotes with simple genomes.
Here we present ARACNE, a novel algorithm, using microarray expression
profiles, specifically designed to scale up to the complexity of regulatory
networks in mammalian cells, yet general enough to address a wider range of
network deconvolution problems. This method uses an information theoretic
approach to eliminate the majority of indirect interactions inferred by
co-expression methods.
  Results: We prove that ARACNE reconstructs the network exactly
(asymptotically) if the effect of loops in the network topology is negligible,
and we show that the algorithm works well in practice, even in the presence of
numerous loops and complex topologies. We assess ARACNE's ability to
reconstruct transcriptional regulatory networks using both a realistic
synthetic dataset and a microarray dataset from human B cells. On synthetic
datasets ARACNE achieves very low error rates and outperforms established
methods, such as Relevance Networks and Bayesian Networks. Application to the
deconvolution of genetic networks in human B cells demonstrates ARACNE's
ability to infer validated transcriptional targets of the c MYC proto-oncogene.
We also study the effects of mis estimation of mutual information on network
reconstruction, and show that algorithms based on mutual information ranking
are more resilient to estimation errors.
