Advertisement

Constraint Minimization for Efficient Modeling of Gene Regulatory Network

  • Ramesh Ram
  • Madhu Chetty
  • Dieter Bulach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)

Abstract

Due to various complexities, as well as noise and high dimensionality, reconstructing a gene regulatory network (GRN) from a high-throughput microarray data becomes computationally intensive.In our earlier work on causal model approach for GRN reconstruction, we had shown the superiority of Markov blanket (MB) algorithm compared to the algorithm using the existing Y and V causal models. In this paper, we show the MB algorithm can be enhanced further by application of the proposed constraint logic minimization (CLM) technique. We describe a framework for minimizing the constraint logic involved (condition independent tests) by exploiting the Markov blanket learning methods developed for a Bayesian network (BN). The constraint relationships are represented in the form of logic using K-map and with the aid of CLM increase the algorithm efficiency and the accuracy. We show improved results by investigations on both the synthetic as well as the real life yeast cell cycle data sets.

Keywords

Causal model Markov blanket Constraint minimization Gene regulatory network 

References

  1. 1.
    Sprites, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search: Adaptive Computation and Machine Learning, 2nd edn. MIT Press, Cambridge (2000)Google Scholar
  2. 2.
    Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge (2000)Google Scholar
  3. 3.
    Verma., T.S., Pearl., J.: ‘A theory of inferred causation’, Principles of Knowledge Representation and Reasoning, pp. 441–452 (1991)Google Scholar
  4. 4.
    Cheng, J., Bell, D.A., Liu, W.: An algorithm for Bayesian belief network construction from data. In: Book An algorithm for Bayesian belief network construction from data, pp. 83–90 (1997)Google Scholar
  5. 5.
    Ram, R., Chetty, M., Dix, T.I.: Causal Modeling of Gene Regulatory Network. In: Book Causal Modeling of Gene Regulatory Network, pp. 1–8 (2006)Google Scholar
  6. 6.
    Ram, R., Chetty, M.: Learning Structure of Gene Regulatory Networks. In: Book Learning Structure of Gene Regulatory Networks, pp. 525–531 (2007)Google Scholar
  7. 7.
    Mano., M.M.: Digital Design, 3rd edn. Prentice Hall, Inc., Englewood Cliffs (2002)Google Scholar
  8. 8.
    Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9, 3273–3297 (1998)CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Aliferis, C.F., Tsamardinos, I.: Algorithms for Large-Scale Local Causal Discovery in the Presence of Small Sample or Large Causal Neighborhoods. In: Book Algorithms for Large-Scale Local Causal Discovery in the Presence of Small Sample or Large Causal Neighborhoods, Vanderbilt University (2002)Google Scholar
  10. 10.
    Aliferis, C.F., Statnikov, I.T.A.: HITON, A Novel Markov Blanket Algorithm for Optimal Variable Selection. In: Book HITON, A Novel Markov Blanket Algorithm for Optimal Variable Selection, Vanderbilt University (2003)Google Scholar
  11. 11.
    Chickering, D.M.: Learning Equivalence Classes of Bayesian-Network Structures. Journal of Machine Learning Research 2, 445–498 (2002)Google Scholar
  12. 12.
    Ram, R., Chetty, M.: Framework for path analysis for learning Gene regulatory network. In: Book Framework for path analysis for learning Gene regulatory network, pp. 264–273. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Chen, K.C., Calzone, L., Csikasz-Nagy, A., Cross, F.R., Novak, B., Tyson, J.J.: Integrative analysis of cell cycle control in budding yeast. Mol. Biol. Cell. 15, 3841–3862 (2004)CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Futcher, B.: Transcriptional Regulatory Networks and the Yeast Cell Cycle. Current Opinion in Cell Biology 14, 676–683 (2002)CrossRefPubMedGoogle Scholar
  15. 15.
    Gardner, T.S., et al.: Inferring genetic networks and indentifying compoud mode of action via expression profiling. Science 301, 102–105 (2003)CrossRefPubMedGoogle Scholar
  16. 16.
    Güldener, U., Münsterkötter, M., Kastenmüller, G., et al.: CYGD: the Comprehensive Yeast Genome Database. Nucleic Acids Research 33, D364–D368 (2005)CrossRefGoogle Scholar
  17. 17.
    Shinohara, A., Iida, K., Takeda, M., Maruyama, O., Miyano, S., Kuhara, S.: Finding Sparse Gene Networks. Genome Inf. 11, 249–250 (2000)Google Scholar
  18. 18.
    Stucki, J.W.: Stability analysis of biochemical systems. A practical guide. Progress Biophys. Mol. Biol. 33, 99–187 (1978)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ramesh Ram
    • 1
  • Madhu Chetty
    • 1
  • Dieter Bulach
    • 2
  1. 1.Gippsland School of ITMonash UniversityChurchillAustralia
  2. 2.CSIRO Livestock IndustriesAustralian Animal Health LaboratoryGeelongAustralia

Personalised recommendations