Reverse engineering cellular networks

Adam A. Margolin, Kai Wang, Wei Keat Lim, Manjunath Kustagi, Ilya Nemenman, Andrea Califano

Research output: Contribution to journalArticlepeer-review

286 Scopus citations


We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing ∼10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.

Original languageEnglish (US)
Pages (from-to)662-671
Number of pages10
JournalNature protocols
Issue number2
StatePublished - Jul 2006
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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