Skip to main content
SpringerLink
Log in

Computational Methods for Analyzing Dynamic Regulatory Networks

  • Protocol
  • First Online:
Book cover Computational Biology of Transcription Factor Binding

Part of the book series: Methods in Molecular Biology ((MIMB,volume 674))

Abstract

Regulatory and other networks in the cell change in a highly dynamic way over time and in response to internal and external stimuli. While several different types of high-throughput experimental procedures are available to study systems in the cell, most only measure static properties of such networks. Information derived from sequence data is inherently static, and most interaction data sets are measured in a static way as well. In this chapter we discuss one of the few abundant sources for temporal information, time series expression data. We provide an overview of the methods suggested for clustering this type of data to identify functionally related genes. We also discuss methods for inferring causality and interactions using lagged correlations and regression analysis. Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks. We point to software tools implementing the methods discussed in this chapter. As more temporal measurements become available, the importance of analyzing such data and of combining it with other types of data will greatly increase.

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

Access this chapter

Log in via an institution
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 169.99
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

Institutional subscriptions

References

  1. Gasch, A.P., Spellman, P.T., and Kao, C.M. et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 11, 4241–4257.

    PubMed  CAS  Google Scholar 

  2. Nau, G.J., Richmond, J.F.L., Schlesinger, A. et al. (2002) Human macrophage activation programs induced by bacterial pathogens. Proc Natl Acad Sci USA 99, 1503–1508.

    Article  PubMed  CAS  Google Scholar 

  3. Bar-Joseph, Z., Siegfried, Z., Brandeis, M. et al. (2008) Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells. Proc Natl Acad Sci USA 105, 955–960.

    Article  PubMed  CAS  Google Scholar 

  4. Xie, X., Lu, J., Kulbokas, E.J. et al. (2005) Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals, Nature 434, 338–345.

    Article  PubMed  CAS  Google Scholar 

  5. Harbison, C.T., Gordon, D.B., Lee, T.I. et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104.

    Article  PubMed  CAS  Google Scholar 

  6. Krogan, N.J., Cagney, G., Yu, H. et al. (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643.

    Article  PubMed  CAS  Google Scholar 

  7. Gavin, A., Aloy, P., Grandi, P. et al. (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636.

    Article  PubMed  CAS  Google Scholar 

  8. Tan, L.P., Seinen, E., Duns, G. et al. (2009) A high throughput experimental approach to identify miRNA targets in human cells. Nucleic Acids Res 2009, gkp715.

    Google Scholar 

  9. Bar-Joseph, Z. (2004) Analyzing time series gene expression data. Bioinformatics 20, 2493–2503.

    Article  PubMed  CAS  Google Scholar 

  10. Eisen, M.B., Spellman, P.T., Brown, P.O. et al. (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95, 14863–14868.

    Article  PubMed  CAS  Google Scholar 

  11. Ernst, J., Nau, G.J., and Bar-Joseph, Z. (2005) Clustering short time series gene expression data. Bioinformatics 21, i159–i168.

    Article  PubMed  CAS  Google Scholar 

  12. Tavazoie, S., Hughes, J.D., Campbell, M.J. et al. (1999) Systematic determination of genetic network architecture. Nat Genet 22, 281–285.

    Article  PubMed  CAS  Google Scholar 

  13. Alter, O., Brown, P.O., and Botstein, D. (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 97, 10101–10106.

    Article  PubMed  CAS  Google Scholar 

  14. Holter, N.S., Mitra, M., Maritan, A. et al. (2000) Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci USA 97, 8409–8414.

    Article  PubMed  CAS  Google Scholar 

  15. Lee, S., and Batzoglou, S. (2003) Application of independent component analysis to microarrays. Genome Biol 4, R76.

    Article  PubMed  Google Scholar 

  16. Frigyesi, A., Veerla, S., Lindgren, D. et al. (2006) Independent component analysis reveals new and biologically significant structures in micro array data. BMC Bioinformatics 7, 290.

    Article  PubMed  Google Scholar 

  17. Bar-Joseph, Z., Gerber, G., Simon, I. et al. (2003) Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes. Proc Natl Acad Sci USA 100, 10146–10151.

    Article  PubMed  CAS  Google Scholar 

  18. Magni, P., Ferrazzi, F., Sacchi, L. et al. (2008) TimeClust: a clustering tool for gene expression time series. Bioinformatics 24, 430–432.

    Article  PubMed  CAS  Google Scholar 

  19. Liu, H., Tarima, S., Borders, A. et al. (2005) Quadratic regression analysis for gene discovery and pattern recognition for non-cyclic short time-course microarray experiments. BMC Bioinformatics 6, 106.

    Article  PubMed  Google Scholar 

  20. Wang, L., Ramoni, M., and Sebastiani, P. (2006) Clustering short gene expression profiles. In: Research in Computational Molecular Biology. pp. 60–68.

    Google Scholar 

  21. Kim, J., and Kim, J.H. (2007) Difference-based clustering of short time-course microarray data with replicates. BMC Bioinformatics 8, 253.

    Article  PubMed  Google Scholar 

  22. Déjean, S., Martin, P.G.P., Baccini, A. et al. (2007) Clustering time-series gene expression data using smoothing spline derivatives. EURASIP J Bioinform Syst Biol 2007, 70561.

    Article  Google Scholar 

  23. Li, C., Yuan, Y., and Wilson, R. (2008) An unsupervised conditional random fields approach for clustering gene expression time series. Bioinformatics 24, 2467–2473.

    Article  PubMed  CAS  Google Scholar 

  24. Schliep, A., Schonhuth, A., and Steinhoff, C. (2003) Using hidden Markov models to analyze gene expression time course data. Bioinformatics 19, i255–i263.

    Article  PubMed  Google Scholar 

  25. Schliep, A., Steinhoff, C., and Schonhuth, A. (2004) Robust inference of groups in gene expression time-courses using mixtures of HMMs. Bioinformatics 20, i283–i289.

    Article  PubMed  CAS  Google Scholar 

  26. Ramoni, M.F., Sebastiani, P., and Kohane, I.S. (2002) Cluster analysis of gene expression dynamics. Proc Natl Acad Sci USA 99, 9121–9126.

    Article  PubMed  CAS  Google Scholar 

  27. Zhou, C., and Wakefield, J. (2006) A Bayesian mixture model for partitioning gene expression data. Biometrics 62, 515–525.

    Article  PubMed  Google Scholar 

  28. Ernst, J., and Bar-Joseph, Z. (2006) STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7, 191.

    Article  PubMed  Google Scholar 

  29. Anand, A., Suganthan, P., and Deb, K. (2007) A novel fuzzy and multiobjective evolutionary algorithm based gene assignment for clustering short time series expression data. In: IEEE Congress on Evolutionary Computation 2007. pp. 297–304.

    Google Scholar 

  30. Workman, C.T., Mak, H.C., McCuine, S. et al. (2006) A systems approach to mapping DNA damage response pathways. Science 312, 1054–1059.

    Article  PubMed  CAS  Google Scholar 

  31. Yeang, C., Mak, H.C., McCuine, S. et al. (2005) Validation and refinement of gene-regulatory pathways on a network of physical interactions. Genome Biol 6, R62.

    Article  PubMed  Google Scholar 

  32. Qian, J., Dolled-Filhart, M., Lin, J. et al. (2001) Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions. J Mol Biol 314, 1053–1066.

    Article  PubMed  CAS  Google Scholar 

  33. Schmitt, W.A., Raab, R.M., and Stephanopoulos, G. (2004) Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data. Genome Res 14, 1654–1663.

    Article  PubMed  CAS  Google Scholar 

  34. Balasubramaniyan, R., Hullermeier, E., Weskamp, N. et al. (2005) Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics 21, 1069–1077.

    Article  PubMed  CAS  Google Scholar 

  35. Pe’er, D., Regev, A., Elidan, G. et al. (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics 17, S215–S224.

    Article  PubMed  Google Scholar 

  36. Hartemink, A.J. (2005) Reverse engineering gene regulatory networks. Nat Biotechnol 23, 554–555.

    Article  PubMed  CAS  Google Scholar 

  37. Ong, I.M., Glasner, J.D., and Page, D. (2002) Modelling regulatory pathways in E. coli from time series expression profiles. Bioinformatics 18, S241–S248.

    Article  PubMed  Google Scholar 

  38. Perrin, B., Ralaivola, L., Mazurie, A. et al. (2003) Gene networks inference using dynamic Bayesian networks. Bioinformatics 19, ii138–ii148.

    Article  PubMed  Google Scholar 

  39. Kim, S., Imoto, S., and Miyano, S. (2004) Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 75, 57–65.

    Article  PubMed  CAS  Google Scholar 

  40. de Hoon, M., Imoto, S., and Miyano, S. (2009) Inferring gene regulatory networks from time-ordered gene expression data using differential equations. In: Discovery Science. pp. 283–288. Springer, Berlin/Heidelberg.

    Google Scholar 

  41. Shermin, A., and Orgun, M.A. (2009) Using dynamic Bayesian networks to infer gene regulatory networks from expression profiles. In: Proceedings of the 2009 ACM Symposium on Applied Computing. pp. 799–803. ACM, Honolulu, Hawaii.

    Google Scholar 

  42. Zou, M., and Conzen, S.D. (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21, 71–79.

    Article  PubMed  CAS  Google Scholar 

  43. Ahmed, A., and Xing, E.P. (2009) Recovering time-varying networks of dependencies in social and biological studies. Proc Natl Acad Sci USA 106, 11878–11883.

    Article  PubMed  CAS  Google Scholar 

  44. 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 

  45. Lee, H.K., Hsu, A.K., Sajdak, J. et al. (2004) Coexpression analysis of human genes across many microarray data sets. Genome Res 14, 1085–1094.

    Article  PubMed  CAS  Google Scholar 

  46. Aach, J., and Church, G.M. (2001) Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495–508.

    Article  PubMed  CAS  Google Scholar 

  47. Bar-Joseph, Z., Gerber, G.K., Gifford, D.K. et al. (2003) Continuous representations of time-series gene expression data. J Comput Biol 10, 341–356.

    Article  PubMed  CAS  Google Scholar 

  48. Smith, A.A., Vollrath, A., Bradfield, C.A. et al. (2008) Similarity queries for temporal toxicogenomic expression profiles. PLoS Comput Biol 4, e1000116.

    Article  PubMed  Google Scholar 

  49. Lin, T., Kaminski, N., and Bar-Joseph, Z. (2008) Alignment and classification of time series gene expression in clinical studies. Bioinformatics 24, i147–i155.

    Article  PubMed  CAS  Google Scholar 

  50. Wilczynski, B., and Tiuryn, J. (2007) Reconstruction of mammalian cell cycle regulatory network from microarray data using stochastic logical networks. In: Computational Methods in Systems Biology. pp. 121–135.

    Google Scholar 

  51. Shi, Y., Mitchell, T., and Bar-Joseph, Z. (2007) Inferring pairwise regulatory relationships from multiple time series datasets. Bioinformatics 23, 755–763.

    Article  PubMed  CAS  Google Scholar 

  52. The ENCODE Project Consortium. (2007) Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816.

    Article  Google Scholar 

  53. Kundaje, A., Middendorf, M., Gao, F. et al. (2005) Combining sequence and time series expression data to learn transcriptional modules. IEEE ACM Trans Comput Biol Bioinform 2, 194–202.

    Article  CAS  Google Scholar 

  54. Ramsey, S.A., Klemm, S.L., Zak, D.E. et al. (2008) Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics. PLoS Comput Biol 4, e1000021.

    Article  PubMed  Google Scholar 

  55. Bonneau, R., Reiss, D., Shannon, P. et al. (2006) The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 7, R36.

    Article  PubMed  Google Scholar 

  56. Madar, A., Greenfield, A., Oster, H. et al. (2009) The Inferelator 2.0: a scalable framework for reconstruction of dynamic regulatory network models. In: Proceedings of the 31st Annual International Conference of the IEEE EMBS. Minneapolis, MN.

    Google Scholar 

  57. Luscombe, N.M., Madan Babu, M., Yu, H. et al. (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312.

    Article  PubMed  CAS  Google Scholar 

  58. Chawade, A., Brautigam, M., Lindlof, A. et al. (2007) Putative cold acclimation pathways in Arabidopsis thaliana identified by a combined analysis of mRNA co-expression patterns, promoter motifs and transcription factors. BMC Genomics 8, 304.

    Article  PubMed  Google Scholar 

  59. Wu, W., and Li, W. (2008) Systematic identification of yeast cell cycle transcription factors using multiple data sources. BMC Bioinformatics 9, 522.

    Article  PubMed  Google Scholar 

  60. Lin, L., Lee, H., Li, W. et al. (2005) Dynamic modeling of cis-regulatory circuits and gene expression prediction via cross-gene identification. BMC Bioinformatics 6, 258.

    Article  PubMed  Google Scholar 

  61. Liao, J.C., Boscolo, R., Yang, Y. et al. (2003) Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci USA 100, 15522–15527.

    Article  PubMed  CAS  Google Scholar 

  62. Tran, L.M., Brynildsen, M.P., Kao, K.C. et al. (2005) gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation. Metab Eng 7, 128–141.

    Article  PubMed  CAS  Google Scholar 

  63. Galbraith, S.J., Tran, L.M., and Liao, J.C. (2006) Transcriptome network component analysis with limited microarray data. Bioinformatics 22, 1886–1894.

    Article  PubMed  CAS  Google Scholar 

  64. Cokus, S., Rose, S., Haynor, D. et al. (2006) Modelling the network of cell cycle transcription factors in the yeast Saccharomyces cerevisiae. BMC Bioinformatics 7, 381.

    Article  PubMed  Google Scholar 

  65. Xiao, Y., and Segal, M.R. (2009) Identification of yeast transcriptional regulation networks using multivariate random forests. PLoS Comput Biol 5, e1000414.

    Article  PubMed  Google Scholar 

  66. Bernard, A., and Hartemink, A.J. (2005) Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data. Pac Symp Biocomput 2005, 459–470.

    Google Scholar 

  67. Sanguinetti, G., Lawrence, N.D., and Rattray, M. (2006) Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinformatics 22, 2775–2781.

    Article  PubMed  CAS  Google Scholar 

  68. Shi, Y., Klutstein, M., Simon, I. et al. (2009) A combined expression-interaction model for inferring the temporal activity of transcription factors. J Comput Biol 16, 1035–1049.

    Article  PubMed  CAS  Google Scholar 

  69. Ernst, J., Vainas, O., Harbison, C.T. et al. (2007) Reconstructing dynamic regulatory maps. Mol Syst Biol 3, 74.

    Article  PubMed  Google Scholar 

  70. Ernst, J., Beg, Q.K., Kay, K.A. et al. (2008) A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli. PLoS Comput Biol 4, e1000044.

    Article  PubMed  Google Scholar 

  71. Vu, T.T., and Vohradsky, J. (2009) Inference of active transcriptional networks by integration of gene expression kinetics modeling and multisource data. Genomics 93, 426–433.

    Article  PubMed  CAS  Google Scholar 

  72. Reeder, C.C. (2008) A novel computational method for inferring dynamic genetic regulatory trajectories. Thesis, Massachusetts Institute of Technology.

    Google Scholar 

  73. Frey, B.J., and Dueck, D. (2007) Clustering by passing messages between data points. Science 315, 972–976.

    Article  PubMed  CAS  Google Scholar 

  74. Amit, I., Garber, M., Chevrier, N. et al. (2009) Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263.

    Article  PubMed  CAS  Google Scholar 

  75. Lu, R., Markowetz, F., Unwin, R.D. et al. (2009) Systems-level dynamic analyses of fate change in murine embryonic stem cells. Nature 462, 358–362.

    Article  PubMed  CAS  Google Scholar 

  76. Philippar, U., Schratt, G., Dieterich, C. et al. (2004) The SRF target gene Fhl2 antagonizes RhoA/MAL-dependent activation of SRF. Mol Cell 16, 867–880.

    Article  PubMed  CAS  Google Scholar 

  77. Segal, E., Shapira, M., Regev, A. et al. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34, 166–176.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Anthony Gitter & Ziv Bar-Joseph

  2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

    Yong Lu

Authors
  1. Anthony Gitter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziv Bar-Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ziv Bar-Joseph .

Editor information

Editors and Affiliations

  1. , Department of Statistics, University of Nebraska-Lincoln, Vine Street 1901, Lincoln, 68588-0665, Nebraska, USA

    Istvan Ladunga

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Gitter, A., Lu, Y., Bar-Joseph, Z. (2010). Computational Methods for Analyzing Dynamic Regulatory Networks. In: Ladunga, I. (eds) Computational Biology of Transcription Factor Binding. Methods in Molecular Biology, vol 674. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-854-6_24

Download citation

  • DOI: https://doi.org/10.1007/978-1-60761-854-6_24

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-853-9

  • Online ISBN: 978-1-60761-854-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation