Stability of Inferring Gene Regulatory Structure with Dynamic Bayesian Networks

  • Jagath C. Rajapakse
  • Iti Chaturvedi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)

Abstract

Though a plethora of techniques have been used to build gene regulatory networks (GRN) from time-series gene expression data, stabilities of such techniques have not been studied. This paper investigates the stability of GRN built using dynamic Bayesian networks (DBN) by synthetically generating gene expression time-series. Assuming scale-free topologies, sample datasets are drawn from DBN to evaluate the stability of estimating the structure of GRN. Our experiments indicate although high accuracy can be achieved with equal number of time points to the number of genes in the network, the presence of large numbers of false positives and false negatives deteriorate the stability of building GRN. The stability could be improved by gathering gene expression at more time points. Interestingly, large networks required less number of time points (normalized to the size of the network) than small networks to achieve the same level stability.

Keywords

Dynamic Bayesian networks gene regulatory networks Markov chain Monte Carlo simulation scale-free networks stability 

References

  1. 1.
    Li, P., Zhang, C., Perkins, E.J., Gong, P., Deng, Y.: Comparison of probabilistic boolean network and dynamic bayesian network approaches for inferring gene regulatorynetworks. BMC Bioinformatics 8, S13–S20 (2007)CrossRefGoogle Scholar
  2. 2.
    Akutsu, T., Miyano, S., Kuhara, S.: Algorithms for identifying boolean networks and related biological networks based on matrix multiplication and fingerprint function. Journal of Computational Biology 7(3-4), 331–343 (2000)CrossRefGoogle Scholar
  3. 3.
    Liu, B., Thiagarajan, P., Hsu, D.: Probabilistic approximations of signaling pathway dynamics. In: Computational Methods in Systems Biology, pp. 251–265 (2009)Google Scholar
  4. 4.
    Gebert, J., Motameny, S., Faigle, U., Forst, C.V., Schrader, R.: Identifying genes of gene regulatory networks using formal concept analysis. Journal of Computational Biology 15(2), 185–194 (2008)CrossRefGoogle Scholar
  5. 5.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian networks to analyze expression data. Journal of Computational Biology 7(3-4), 601–620 (2000)CrossRefGoogle Scholar
  6. 6.
    Imoto, S., Kim, S., Goto, T., Miyano, S., Aburatani, S., Tashiro, K., Kuhara, S.: Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network. J. Bioinform. Comput. Biol. 1(2), 231–252 (2003)CrossRefGoogle Scholar
  7. 7.
    Nariai, N., Kim, S., Imoto, S., Miyano, S.: Using protein-protein interactions for refining gene networks estimated from microarray data by bayesian networks. In: Pac. Symp. Biocomput., pp. 336–347 (2004)Google Scholar
  8. 8.
    Ota, K., Yamada, T., Yamanishi, Y., Goto, S., Kanehisa, M.: Comprehensive analysis of delay in transcriptional regulation using expression profiles. Genome Informatics 14, 302–303 (2003)Google Scholar
  9. 9.
    Perrin, E.D., Liva, R., Mazurie, A., Bottani, S., Mallet, J., dAlche-Buc, F.: Gene network inference using dynamic bayesian networks. Bioinformatics 12(2), 138–148 (2003)Google Scholar
  10. 10.
    Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19(17), 2271–2282 (2003)CrossRefGoogle Scholar
  11. 11.
    Friedman, N., Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 139–140 (1998)Google Scholar
  12. 12.
    Albert-Laszlo, B., Reka, A.: Emergence of scaling in random networks. Science 286(5439), 509 (1999)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jagath C. Rajapakse
    • 1
    • 2
    • 3
  • Iti Chaturvedi
    • 1
  1. 1.Bioinformatics Research Center, School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Singapore-MIT AllianceSingapore

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