Inferring Nonstationary Gene Networks from Longitudinal Gene Expression Microarrays

  • Hsun-Hsien ChangEmail author
  • Marco F. Ramoni


Inferring gene networks from longitudinal gene expression microarrays is a crucial step towards the study of gene regulatory mechanisms. A decade ago, expensive microarray technology restricted the number of samples undergoing gene expression profiling in single studies, leading the inference algorithms that assume stationary gene networks to the best solution. Thanks to decreasing cost of modern microarray technologies, more gene expression profiles can be assessed in single studies. With more samples available, we can relax the stationarity assumption and develop a method to infer dynamic gene networks, which can reflect more realistic biology where genes adaptively orchestrate each other. This paper applied the framework of dynamic Bayesian networks to infer adaptive gene interactions by identifying individual transition networks between pairs of consecutive times. Due to high computational burden of inferring the interconnection patterns among all genes over time, we designed a parallelizable inference algorithm to make feasible the task. We validated our approach by two clinical studies: yellow fever vaccination and mechanical periodontal therapy. The inferred dynamic networks achieved more than 90% predictive accuracy, a significant improvement when compared to stationary models (p < 0.05). The adaptive models can help explain the induction of innate immunology in greater details after yellow fever vaccination and interpret the anti-inflammatory effect of mechanical periodontal therapy.


Nonstationary networks Bayesian networks Distributed computation Gene expression 



This research was supported by NIH/NIAID (5U19A1067854-05).


  1. 1.
    Ramoni, M. F., Sebastiani, P., & Kohane, I. S. (2002). Cluster analysis of gene expression dynamics. Proceedings of the National Academy of Sciences of the United States of America, 99(14), 9121–9126.MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Song, J. Z., Duan, K. M., Ware, T., & Surette, M. (2007). The wavelet-based cluster analysis for temporal gene expression data. EURASIP Journal on Bioinformatics and System Biology, 39382.Google Scholar
  3. 3.
    Schliep, A., Costa, I. G., Steinhoff, C., & Schonhuth, A. (2005). Analyzing gene expression time-courses. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2(3), 179–193.CrossRefGoogle Scholar
  4. 4.
    Androulakis, I. P., Yang, E., & Almon, R. R. (2007). Analysis of time-series gene expression data: methods, challenges, and opportunities. Annual Review of Biomedical Engineering, 9, 205–228.CrossRefGoogle Scholar
  5. 5.
    Querec, T. D., Akondy, R. S., Lee, E. K., Cao, W., Nakaya, H. I., Teuwen, D., et al. (2009). Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nature Immunology, 10(1), 116–125.CrossRefGoogle Scholar
  6. 6.
    Taylor, M. W., Tsukahara, T., McClintick, J. N., Edenberg, H. J., & Kwo, P. (2008). Cyclic changes in gene expression induced by Peg-interferon alfa-2b plus ribavirin in peripheral blood monocytes (PBMC) of hepatitis C patients during the first 10 weeks of treatment. Journal of Translational Medicine, 6, 66.CrossRefGoogle Scholar
  7. 7.
    Papapanou, P. N., Sedaghatfar, M. H., Demmer, R. T., Wolf, D. L., Yang, J., Roth, G. A., et al. (2007). Periodontal therapy alters gene expression of peripheral blood monocytes. Journal of Clinical Periodontology, 34(9), 736–747.CrossRefGoogle Scholar
  8. 8.
    Bar-Joseph, Z. (2004). Analyzing time series gene expression data. Bioinformatics, 20(16), 2493–2503.CrossRefGoogle Scholar
  9. 9.
    Bansal, M., Belcastro, V., Ambesi-Impiombato, A., & di Bernardo, D. (2007). How to infer gene networks from expression profiles. Molecular Systems Biology, 3, 78.Google Scholar
  10. 10.
    Finkenstadt, B., Heron, E. A., Komorowski, M., Edwards, K., Tang, S., Harper, C. V., et al. (2008). Reconstruction of transcriptional dynamics from gene reporter data using differential equations. Bioinformatics, 24(24), 2901–2907.CrossRefGoogle Scholar
  11. 11.
    Kim, H., Lee, J. K., & Park, T. (2007). Boolean networks using the chi-square test for inferring large-scale gene regulatory networks. BMC Bioinformatics, 8, 37.CrossRefGoogle Scholar
  12. 12.
    Kim, S., Kim, J., & Cho, K. H. (2007). Inferring gene regulatory networks from temporal expression profiles under time-delay and noise. Computational Biology and Chemistry, 31(4), 239–245.zbMATHCrossRefGoogle Scholar
  13. 13.
    Zou, M., & Conzen, S. D. (2005). A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21(1), 71–79.CrossRefGoogle Scholar
  14. 14.
    Ferrazzi, F., Sebastiani, P., Ramoni, M. F., & Bellazzi, R. (2007). Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks. BMC Bioinformatics, 8(Suppl 5), S2.CrossRefGoogle Scholar
  15. 15.
    Chang, H. H., & Ramoni, M. F. (2009). Robust cross-race gene expression analysis. Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, 505–508.Google Scholar
  16. 16.
    da Huang, W., Sherman, B. T., & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44–57.CrossRefGoogle Scholar
  17. 17.
    Febbraio, M., & Silverstein, R. L. (2007). CD36: implications in cardiovascular disease. The International Journal of Biochemistry & Cell Biology, 39(11), 2012–2030.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and TechnologyHarvard Medical SchoolBostonUSA
  2. 2.BostonUSA

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