Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Reverse engineering of regulatory networks in human B cells


Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Comparison of the performance of Bayesian networks and ARACNe algorithms in the analysis of a synthetic genetic network.
Figure 2: Schematic representation of the germinal center reaction and related normal and malignant B cell phenotypes.
Figure 3: Identification of key regulatory hubs in the human B cell network.
Figure 4: The MYC subnetwork.
Figure 5: Distribution of known MYC target genes among first and second neighbors in the MYC subnetwork.
Figure 6: Identification of new direct MYC targets by ChIP analysis.

Accession codes


Gene Expression Omnibus


  1. Han, J.D. et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88–93 (2004).

    CAS  Article  Google Scholar 

  2. Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N. & Barabasi, A.L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    CAS  Article  Google Scholar 

  3. Jordan, I.K., Marino-Ramirez, L., Wolf, Y.I. & Koonin, E.V. Conservation and coevolution in the scale-free human gene coexpression network. Mol. Biol. Evol. 21, 2058–2070 (2004).

    CAS  Article  Google Scholar 

  4. Lukashin, A.V., Lukashev, M.E. & Fuchs, R. Topology of gene expression networks as revealed by data mining and modeling. Bioinformatics 19, 1909–1916 (2003).

    CAS  Article  Google Scholar 

  5. Friedman, N., Linial, M., Nachman, I. & Pe'er, D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000).

    CAS  Article  Google Scholar 

  6. 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. 2001, 422–433 (2001).

    Google Scholar 

  7. Gat-Viks, I. & Shamir, R. Chain functions and scoring functions in genetic networks. Bioinformatics 19 Suppl 1, i108–i117 (2003).

    Article  Google Scholar 

  8. 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).

    CAS  Article  Google Scholar 

  9. Yeung, M.K., Tegner, J. & Collins, J.J. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA 99, 6163–6168 (2002).

    CAS  Article  Google Scholar 

  10. Ideker, T. et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929–934 (2001).

    CAS  Article  Google Scholar 

  11. Butte, A.J. & Kohane, I.S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 2000, 418–429 (2000).

    Google Scholar 

  12. Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R. & Kohane, I.S. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. USA 97, 12182–12186 (2000).

    CAS  Article  Google Scholar 

  13. Elkon, R., Linhart, C., Sharan, R., Shamir, R. & Shiloh, Y. Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. Genome Res. 13, 773–780 (2003).

    CAS  Article  Google Scholar 

  14. Hughes, T.R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).

    CAS  Article  Google Scholar 

  15. MacLennan, I.C. Germinal centers. Annu. Rev. Immunol. 12, 117–139 (1994).

    CAS  Article  Google Scholar 

  16. Barabasi, A.L. & Bonabeau, E. Scale-free networks. Sci. Am. 288, 60–69 (2003).

    Article  Google Scholar 

  17. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004).

    CAS  Article  Google Scholar 

  18. Smith, V.A., Jarvis, E.D. & Hartemink, A.J. Influence of network topology and data collection on network inference. Pac. Symp. Biocomput. 2003, 164–175 (2003).

    Google Scholar 

  19. Yu, J., Smith, A.V., Wang, P.P., Hartemink, A.J. & Jarvis, E.D. Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. in 3rd International Conference on Systems Biology (Karolinska Institute, Stockholm, Sweden, 2002).

    Google Scholar 

  20. Smith, V.A., Jarvis, E.D. & Hartemink, A.J. Evaluating functional network inference using simulations of complex biological systems. Bioinformatics 18, S216–S224 (2002).

    Article  Google Scholar 

  21. Jarvis, E.D. et al. A framework for integrating the songbird brain. J. Comp. Physiol. A. Neuroethol. Sens. Neural. Behav. Physiol. 188, 961–980 (2002).

    CAS  Article  Google Scholar 

  22. Zeeberg, B.R. et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 4, R28 (2003).

    Article  Google Scholar 

  23. Zeller, K.I., Jegga, A.G., Aronow, B.J., O'Donnell, K.A. & Dang, C.V. An integrated database of genes responsive to the Myc oncogenic transcription factor: identification of direct genomic targets. Genome Biol. 4, R69 (2003).

    Article  Google Scholar 

  24. Fernandez, P.C. et al. Genomic targets of the human c-Myc protein. Genes Dev. 17, 1115–1129 (2003).

    CAS  Article  Google Scholar 

  25. Dang, C.V. c-Myc target genes involved in cell growth, apoptosis, and metabolism. Mol. Cell. Biol. 19, 1–11 (1999).

    CAS  Article  Google Scholar 

  26. O'Connell, B.C. et al. A large scale genetic analysis of c-Myc-regulated gene expression patterns. J. Biol. Chem. 278, 12563–12573 (2003).

    CAS  Article  Google Scholar 

  27. D'Haeseleer, P., Wen, X., Fuhrman, S. & Somogyi, R. Linear modeling of mRNA expression levels during CNS development and injury. Pac. Symp. Biocomput. 1999, 41–52 (1999).

    Google Scholar 

  28. Stuart, J.M., Segal, E., Koller, D. & Kim, S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

    CAS  Article  Google Scholar 

  29. Barabasi, A.L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    CAS  Article  Google Scholar 

  30. Roos, J., Luz, J.M., Centoducati, S., Sternglanz, R. & Lennarz, W.J. ENP1, an essential gene encoding a nuclear protein that is highly conserved from yeast to humans. Gene 185, 137–146 (1997).

    CAS  Article  Google Scholar 

  31. Suzuki, N. et al. A cytoplasmic protein, bystin, interacts with trophinin, tastin, and cytokeratin and may be involved in trophinin-mediated cell adhesion between trophoblast and endometrial epithelial cells. Proc. Natl. Acad. Sci. USA 95, 5027–5032 (1998).

    CAS  Article  Google Scholar 

  32. Chen, W., Bucaria, J., Band, D.A., Sutton, A. & Sternglanz, R. Enp1, a yeast protein associated with U3 and U14 snoRNAs, is required for pre-rRNA processing and 40S subunit synthesis. Nucleic Acids Res. 31, 690–699 (2003).

    CAS  Article  Google Scholar 

  33. Stewart, M.J. & Nordquist, E.K. Drosophila Bys is nuclear and shows dynamic tissue-specific expression during development. Dev. Genes Evol. 215, 97–102 (2005).

    CAS  Article  Google Scholar 

  34. Joe, H. Multivariate Models and Dependence Concepts (Chapman & Hall, Boca Raton, Florida, 1997).

    Book  Google Scholar 

  35. Steuer, R., Kurths, J., Daub, C.O., Weise, J. & Selbig, J. The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics 18 Suppl 2: S231–S240 (2002).

    Article  Google Scholar 

  36. Cooper, G.F. & Herskovits, E. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347 (1992).

    Google Scholar 

  37. 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).

    CAS  Article  Google Scholar 

  38. Klein, U. et al. Transcriptional analysis of the B cell germinal center reaction. Proc. Natl. Acad. Sci. USA 100, 2639–2644 (2003).

    CAS  Article  Google Scholar 

  39. Klein, U. et al. Gene expression profile analysis of AIDS-related primary effusion lymphoma (PEL) suggests a plasmablastic derivation and identifies PEL-specific transcripts. Blood 101, 4115–4121 (2003).

    CAS  Article  Google Scholar 

  40. Basso, K. et al. Gene expression profiling of hairy cell leukemia reveals a phenotype related to memory B cells with altered expression of chemokine and adhesion receptors. J. Exp. Med. 199, 59–68 (2004).

    CAS  Article  Google Scholar 

  41. Kuppers, R. et al. Identification of Hodgkin and Reed-Sternberg cell-specific genes by gene expression profiling. J. Clin. Invest. 111, 529–537 (2003).

    CAS  Article  Google Scholar 

  42. Basso, K. et al. Tracking CD40 signaling during germinal center development. Blood 104, 4088–4096 (2004).

    CAS  Article  Google Scholar 

  43. Niu, H., Cattoretti, G. & Dalla-Favera, R. BCL6 controls the expression of the B7-1/CD80 costimulatory receptor in germinal center B cells. J. Exp. Med. 198, 211–221 (2003).

    CAS  Article  Google Scholar 

  44. Wu, K.J., Polack, A. & Dalla-Favera, R. Coordinated regulation of iron-controlling genes, H-ferritin and IRP2, by c-MYC. Science 283, 676–679 (1999).

    CAS  Article  Google Scholar 

  45. Kempkes, B. et al. B-cell proliferation and induction of early G1-regulating proteins by Epstein-Barr virus mutants conditional for EBNA2. EMBO J. 14, 88–96 (1995).

    CAS  Article  Google Scholar 

  46. Frank, S.R., Schroeder, M., Fernandez, P., Taubert, S. & Amati, B. Binding of c-Myc to chromatin mediates mitogen-induced acetylation of histone H4 and gene activation. Genes Dev. 15, 2069–2082 (2001).

    CAS  Article  Google Scholar 

Download references


We thank I. Nemenman for his expertise in Information Theory, M. Mattioli for contributing to the generation of the B-cell gene expression database and V. Miljkovic for help with the microarray hybridizations. K.B. is supported by a fellowship from the American-Italian Cancer Foundation, A.M. by the National Library of Medicine Medical Informatics Research Training Program at Columbia and U.K. by a fellowship from the Human Frontiers Science Program. This study was supported by a US National Institutes of Health grant to R.D.-F. and by the computational resources of the AMDeC Bioinformatics Core at Columbia University.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Andrea Califano.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

List of samples included in the gene expression profiles database. (PDF 14 kb)

Supplementary Table 2

Adjacency matrix for the B cell network inferred by ARACNe. (PDF 1318 kb)

Supplementary Table 3

List of 5% largest hubs in the B cell network and their distribution in the Gene Ontology categories. (PDF 44 kb)

Supplementary Table 4

c-MYC network genes identified by ARACNe. (PDF 115 kb)

Supplementary Table 5

Identification of E-boxes and ChIP validation in 34 c-MYC first neighbors. (PDF 10 kb)

Supplementary Table 6

Analysis of the Gene Ontology terms enrichment in the 10 largest c-MYC sub-hubs. (PDF 380 kb)

Supplementary Table 7

Oligonucleotide primer sequences and PCR conditions used for the ChIP assay. (PDF 12 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Basso, K., Margolin, A., Stolovitzky, G. et al. Reverse engineering of regulatory networks in human B cells. Nat Genet 37, 382–390 (2005).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing