Reverse engineering cellular networks (original) (raw)

Nature Protocols volume 1, pages 662–671 (2006)Cite this article

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 12 print issues and online access

$259.00 per year

only $21.58 per issue

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Additional access options:

Similar content being viewed by others

References

  1. Schena, M., Shalon, D., Davis, R.W. & Brown, P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).
    Article CAS Google Scholar
  2. Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005).
    Article CAS Google Scholar
  3. Perez, O.D. & Nolan, G.P. Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry. Nat. Biotechnol. 20, 155–162 (2002).
    Article CAS Google Scholar
  4. Lu, W., Kimball, E. & Rabinowitz, J.D. A high-performance liquid chromatography-tandem mass spectrometry method for quantitation of nitrogen-containing intracellular metabolites. J. Am. Soc. Mass Spectrom. 17, 37–50 (2006).
    Article CAS Google Scholar
  5. van Someren, E.P., Wessels, L.F., Backer, E. & Reinders, M.J. Genetic network modeling. Pharmacogenomics 3, 507–525 (2002).
    Article CAS Google Scholar
  6. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004).
    Article CAS Google Scholar
  7. Butte, A.J. & Kohane, I.S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 418–429 (2000).
  8. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D.A. & Nolan, G.P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005).
    Article CAS Google Scholar
  9. Margolin, A.A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7, S7 (2006).
    Article Google Scholar
  10. Tegner, J., Yeung, M.K., Hasty, J. & Collins, J.J. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc. Natl. Acad. Sci. USA 100, 5944–5949 (2003).
    Article CAS Google Scholar
  11. Gardner, T.S., di Bernardo, D., Lorenz, D. & Collins, J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102–105 (2003).
    Article CAS Google Scholar
  12. Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166–176 (2003).
    Article CAS Google Scholar
  13. Hartemink, A.J., Gifford, D.K., Jaakkola, T.S. & Young, R.A. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput. 422–433 (2001).
  14. Gat-Viks, I. & Shamir, R. Chain functions and scoring functions in genetic networks. Bioinformatics 19 (Suppl. 1): i108–i117 (2003).
    Article Google Scholar
  15. Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934 (2001).
    Article CAS Google Scholar
  16. Basso, K. et al. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382–390 (2005).
    Article CAS Google Scholar
  17. Hartemink, A.J. Reverse engineering gene regulatory networks. Nat. Biotechnol. 23, 554–555 (2005).
    Article CAS Google Scholar
  18. Ren, B. et al. Genome-wide location and function of DNA binding proteins. Science 290, 2306–2309 (2000).
    Article CAS Google Scholar
  19. Cover, T.M. & Thomas, J.A. Elements of Information Theory (John Wiley & Sons, New York, 1991).
    Book Google Scholar
  20. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
    Article CAS Google Scholar
  21. Kel, A.E. et al. MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 31, 3576–3579 (2003).
    Article CAS Google Scholar
  22. Klein, U. et al. Gene expression profiling of B cell chronic lymphocytic leukemia reveals a homogeneous phenotype related to memory B cells. J. Exp. Med. 194, 1625–1638 (2001).
    Article CAS Google Scholar
  23. Matys, V. et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34, D108–D110 (2006).
    Article CAS Google Scholar
  24. Vlieghe, D. et al. A new generation of JASPAR, the open-access repository for transcription factor binding site profiles. Nucleic Acids Res. 34, D95–D97 (2006).
    Article CAS Google Scholar
  25. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
    Article CAS Google Scholar

Download references

Acknowledgements

This work was supported by the National Cancer Institute, the National Institute of Allergy and Infectious Diseases, and the National Centers for Biomedical Computing NIH Roadmap Initiative. A.A.M. is supported by an IBM Ph.D. fellowship and by the National Library of Medicine Medical Informatics Research Training Program. I.N. is supported by the Department of Energy/National Nuclear Security Administration. We would like to thank R. Dalla-Favera for continued support and insight, K. Basso and U. Klein for contributions to the experimental validation of the original ARACNE algorithm, and K. Smith for help in reviewing the manuscript.

Author information

Author notes

  1. Adam A Margolin and Kai Wang: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biomedical Informatics, Columbia University, New York, 10032, New York, USA
    Adam A Margolin, Kai Wang & Andrea Califano
  2. Joint Centers for Systems Biology, Columbia University, New York, 10032, New York, USA
    Adam A Margolin, Kai Wang, Wei Keat Lim, Manjunath Kustagi & Andrea Califano
  3. Los Alamos National Laboratory, Los Alamos, 87545, New Mexico, USA
    Ilya Nemenman

Authors

  1. Adam A Margolin
    You can also search for this author inPubMed Google Scholar
  2. Kai Wang
    You can also search for this author inPubMed Google Scholar
  3. Wei Keat Lim
    You can also search for this author inPubMed Google Scholar
  4. Manjunath Kustagi
    You can also search for this author inPubMed Google Scholar
  5. Ilya Nemenman
    You can also search for this author inPubMed Google Scholar
  6. Andrea Califano
    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toAndrea Califano.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

About this article

Cite this article

Margolin, A., Wang, K., Lim, W. et al. Reverse engineering cellular networks.Nat Protoc 1, 662–671 (2006). https://doi.org/10.1038/nprot.2006.106

Download citation

This article is cited by