Skip to main content

Predictive Models of Gene Regulation

Application of Regression Methods to Microarray Data

  • Protocol

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 377))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  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.

    PubMed  CAS  Google Scholar 

  2. Bussemaker, H. J., Li, H., and Siggia, E. D. (2001) Regulatory element detection using correlation with expression. Nat. Genet. 27, 167–171.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    PubMed  CAS  Google Scholar 

  8. Carey, M. (1998) The enhanceosome and transcriptional synergy. Cell 92, 5–8.

    Article  PubMed  CAS  Google Scholar 

  9. Ptashne, M. and Gann, A.(1997) Transcriptional activation by recruitment. Nature 386, 569–577.

    Article  PubMed  CAS  Google Scholar 

  10. Pilpel, Y., Sudarsanam, P., and Church, G. M. (2001) Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet. 29, 153–159.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  13. Keles, S., vonder Laan, M., and Eisen, M. B. (2002) Identification of regulatory elements using a feature selection method. Bioinformatics 18, 1167–1175.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  15. Friedman, J.H. (1991) Multivariate Adaptive Regression Splines. Annals of Statistics 19, 1–67.

    Article  Google Scholar 

  16. Hastie, T., Tibshirani, R., and Friedman, J. H. (2001) The Elements of Statistical Learning, Springer Verlag, New York, NY.

    Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  19. Beer, M.A. and Tavazoie, S. (2004) Predicting gene expression from sequence. Cell 117, 185–198.

    Article  PubMed  CAS  Google Scholar 

  20. Pennacchio, L.A. and Rubin, E. M. (2001) Genomic strategies to identify mammalian regulatory sequences. Nat. Rev. Genet. 2, 100–109.

    Article  PubMed  CAS  Google Scholar 

  21. Keles, S., vander Laan, M. J., and Vulpe, C. (2004) Regulatory motif finding by logic regression. Bioinformatics 20, 2799–2811.

    Article  PubMed  CAS  Google Scholar 

  22. Phuong, T. M., Lee, D., and Lee, K. H. (2004) Regression trees for regulatory element identification. Bioinformatics 20, 750–757.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  24. Das, D., Nahlé, Z., and Zhang, M. Q. (2006) Adaptively inferring human transcriptional subnetworks. Mol. Syst. Biol. 2, 2006. 0029.

    Google Scholar 

  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. Steinberg, D. and Colla, P. (1999) MARS: An Introduction. Salford Systems, San Diego, CA.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Humana Press Inc., Totowa, NJ

About this protocol

Cite this protocol

Das, D., Zhang, M.Q. (2007). Predictive Models of Gene Regulation. In: Korenberg, M.J. (eds) Microarray Data Analysis. Methods in Molecular Biology™, vol 377. Humana Press. https://doi.org/10.1007/978-1-59745-390-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-59745-390-5_5

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-540-8

  • Online ISBN: 978-1-59745-390-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics