Predictive Models of Gene Regulation

Application of Regression Methods to Microarray Data
  • Debopriya Das
  • Michael Q. Zhang
Part of the Methods in Molecular Biology™ book series (MIMB, volume 377)


Eukaryotic transcription is a complex process. A myriad of biochemical signals cause activators and repressors to bind specific cis-elements on the promoter DNA, which help to recruit the basal transcription machinery that ultimately initiates transcription. In this chapter, we discuss how regression techniques can be effectively used to infer the functional cis-regulatory elements and their cooperativity from microarray data. Examples from yeast cell cycle are drawn to demonstrate the power of these techniques. Periodic regulation of the cell cycle, connection with underlying energetics, and the inference of combinatorial logic are also discussed. An implementation based on regression splines is discussed in detail.

Key Words

Transcription regulation regression splines cooperativity correlation yeast cell cycle cis-regulatory element MARS 


  1. 1.
    Spellman, P. T., Sherlock, G., Zhang, M. Q., et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9, 3273–3297.PubMedGoogle Scholar
  2. 2.
    Bussemaker, H. J., Li, H., and Siggia, E. D. (2001) Regulatory element detection using correlation with expression. Nat. Genet. 27, 167–171.PubMedCrossRefGoogle Scholar
  3. 3.
    Das, D., Banerjee, N., and Zhang, M. Q. (2004) Interacting models of cooperative gene regulation. Proc. Natl. Acad. Sci. USA 101, 16,234–16,239.PubMedCrossRefGoogle Scholar
  4. 4.
    Conlon, E. M., Liu, X. S., Lieb, J.D., and Liu, J. S. (2003) Integrating regulatory motif discovery and genome-wide expression analysis. Proc. Natl. Acad. Sci. USA 100, 3339–3344.PubMedCrossRefGoogle Scholar
  5. 5.
    Djordjevic, M., Sengupta, A.M., and Shraiman, B. I. (2003) A biophysical approach to transcription factor binding site discovery. Genome Res. 13, 2381–2390.PubMedCrossRefGoogle Scholar
  6. 6.
    Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R.J., and Church, G. M. (1999) Systematic determination of genetic network architecture. Nat. Genet. 22, 281–285.PubMedCrossRefGoogle Scholar
  7. 7.
    Liu, X. S., Brutlag, D.L., and Liu, J. S. (2002) An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat. Biotechnol. 20, 835–839.PubMedGoogle Scholar
  8. 8.
    Carey, M. (1998) The enhanceosome and transcriptional synergy. Cell 92, 5–8.PubMedCrossRefGoogle Scholar
  9. 9.
    Ptashne, M. and Gann, A.(1997) Transcriptional activation by recruitment. Nature 386, 569–577.PubMedCrossRefGoogle Scholar
  10. 10.
    Pilpel, Y., Sudarsanam, P., and Church, G. M. (2001) Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet. 29, 153–159.PubMedCrossRefGoogle Scholar
  11. 11.
    Banerjee, N. and Zhang, M. Q. (2003) Identifying cooperativity among transcription factors controlling the cell cycle in yeast. Nucleic Acids Res. 31, 7024–7031.PubMedCrossRefGoogle Scholar
  12. 12.
    Kato, M., Hata, N., Banerjee, N., Futcher, B., and Zhang, M. Q. (2004) Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biol. 5, R56.PubMedCrossRefGoogle Scholar
  13. 13.
    Keles, S., vonder Laan, M., and Eisen, M. B. (2002) Identification of regulatory elements using a feature selection method. Bioinformatics 18, 1167–1175.PubMedCrossRefGoogle Scholar
  14. 14.
    Chiang, D. Y., Moses, A. M., Kellis, M., Lander, E.S., and Eisen, M. B. (2003) Phylogenetically and spatially conserved word pairs associated with gene-expression changes in yeasts. Genome Biol. 4, R43.PubMedCrossRefGoogle Scholar
  15. 15.
    Friedman, J.H. (1991) Multivariate Adaptive Regression Splines. Annals of Statistics 19, 1–67.CrossRefGoogle Scholar
  16. 16.
    Hastie, T., Tibshirani, R., and Friedman, J. H. (2001) The Elements of Statistical Learning, Springer Verlag, New York, NY.Google Scholar
  17. 17.
    Cho, R. J., Campbell, M. J., Winzeler, E. A., et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell. 2, 65–73.PubMedCrossRefGoogle Scholar
  18. 18.
    Kellis, M., Patterson, N., Endrizzi, M., Birren, B., and Lander, E. S. (2003) Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423, 241–254.PubMedCrossRefGoogle Scholar
  19. 19.
    Beer, M.A. and Tavazoie, S. (2004) Predicting gene expression from sequence. Cell 117, 185–198.PubMedCrossRefGoogle Scholar
  20. 20.
    Pennacchio, L.A. and Rubin, E. M. (2001) Genomic strategies to identify mammalian regulatory sequences. Nat. Rev. Genet. 2, 100–109.PubMedCrossRefGoogle Scholar
  21. 21.
    Keles, S., vander Laan, M. J., and Vulpe, C. (2004) Regulatory motif finding by logic regression. Bioinformatics 20, 2799–2811.PubMedCrossRefGoogle Scholar
  22. 22.
    Phuong, T. M., Lee, D., and Lee, K. H. (2004) Regression trees for regulatory element identification. Bioinformatics 20, 750–757.PubMedCrossRefGoogle Scholar
  23. 23.
    Orian, A., van Steensel, B., Delrow, J., et al. (2003) Genomic binding by the Drosophila Myc, Max, Mad/Mnt transcription factor network. Genes Dev. 17, 1101–1114.PubMedCrossRefGoogle Scholar
  24. 24.
    Das, D., Nahlé, Z., and Zhang, M. Q. (2006) Adaptively inferring human transcriptional subnetworks. Mol. Syst. Biol. 2, 2006. 0029.Google Scholar
  25. 25.
    Press, W. H., Flannery, B. P., Teukolsky, S.A., and Vetterling, W. T. (1992) Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, Cambridge, UK.Google Scholar
  26. 26.
    Steinberg, D. and Colla, P. (1999) MARS: An Introduction. Salford Systems, San Diego, CA.Google Scholar

Copyright information

© Humana Press Inc., Totowa, NJ 2007

Authors and Affiliations

  • Debopriya Das
    • 1
  • Michael Q. Zhang
    • 2
  1. 1.Lawrence Berkeley National LaboratoryBerkeley
  2. 2.Cold Spring Harbor LaboratoryCold Spring Harbor

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