G-Network Modelling Based Abnormal Pathway Detection in Gene Regulatory Networks

Conference paper

Abstract

Gene expression centered gene regulatory networks studies can provide insight into the dynamics of pathway activities that depend on changes in their environmental conditions. Thus we propose a new pathway analysis approach to detect differentially behaving pathways in abnormal conditions based on G-network theory. Using this approach gene regulatory network model parameters are estimated from normal and abnormal samples using optimization techniques with corresponding constraints. We show that in a “p53 network” application, the proposed method effectively detects anomalous activated/inactivated pathways related with MDM2, ATM/ATR and RB1 genes, which could not be observed from previous analyses of gene regulatory network normal and abnormal behaviour.

Notes

Acknowledgments

We would like to thank to Omer Abdelrahman and Zerrin Isik for helpful discussions.

References

  1. 1.
    Opgen-Rhein, R., Strimmer, K.: Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process. BMC Bioinformatics 8(suppl 2), S3 (2007)CrossRefGoogle Scholar
  2. 2.
    Beissbarth, T., Speed, T.: GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20(9), 1464–1465 (2004)Google Scholar
  3. 3.
    Gelenbe, E.: G-networks with triggered customer movement. J. Appl. Probab. 30(3), 742–748 (1993)Google Scholar
  4. 4.
    Gelenbe, E.: Steady-state solution of probabilistic gene regulatory networks. J. Theor. Biol. Phys. Rev. E 76, 031903 (2007)CrossRefGoogle Scholar
  5. 5.
    Kim, H., Gelenbe, E.: Anomaly detection in gene expression via stochastic models of gene regulatory networks. BMC Genomics 10(suppl 3), S26 (2009)CrossRefGoogle Scholar
  6. 6.
    Thattai, M., van Oudenaarden, A.: Intrinsic noise in gene regulatory networks. In: Proceedings of the National Academy of Sciences 98(15), 8614–8619 (2001)Google Scholar
  7. 7.
    Wilkinson, D.J.: Stochastic modelling for quantitative description of heterogeneous biological systems. Nature Rev. Genetics 10(2), 122–133 (2009)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Schedlich, L., Graham, L.: Role of insulin-like growth factor binding protein-3 in breast cancer cell growth. Microsc. Res. Tech. 59(1), 12–22 (2002)CrossRefGoogle Scholar
  9. 9.
    Brown, C., Lain, S., Verma, C., Fersht, A., Lane, D.: Awakening guardian angels: drugging the p53 pathway. Nature Rev. Cancer 9(12), 862–873 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited  2011

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringImperial CollegeLondonUK
  2. 2.Department of Molecular Biology and GeneticsBilkent UniversityAnkaraTurkey

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