Generating Synthetic Gene Regulatory Networks

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


Reconstructing GRN from microarray dataset is a very challenging problem as these datasets typically have large number of genes and less number of samples. Moreover, the reconstruction task becomes further complicated as there are no suitable synthetic datasets available for validation and evaluation of GRN reconstruction techniques. Synthetic datasets allow validating new techniques and approaches since the underlying mechanisms of the GRNs, generated from these datasets, are completely known. In this paper, we present an approach for synthetically generating gene networks using causal relationships. The synthetic networks can have varying topologies such as small world, random, scale free, or hierarchical topologies based on the well-defined GRN properties. These artificial but realistic GRN networks provide a simulation environment similar to a real-life laboratory microarray experiment. These networks also provide a mechanism for studying the robustness of reconstruction methods to individual and combination of parametric changes such as topology, noise (background and experimental noise) and time delays. Studies involving complicated interactions such as feedback loops, oscillations, bi-stability, dynamic behavior, vertex in-degree changes and number of samples can also be carried out by the proposed synthetic GRN networks.


Causal model synthetic gene regulatory networks microarrays 


  1. 1.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyse expression data. Journal on Computational Biology 7, 601–620 (2000)CrossRefGoogle Scholar
  2. 2.
    Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architecture. In: Pacific Symposium on Biocomputing, vol. 3, pp. 18–29 (1998)Google Scholar
  3. 3.
    Ando, S., Iba, H.: Inference of gene regulatory model by genetic algorithms. In: Proc. Conference on Evolutionary Computation, pp. 712–719 (2001)Google Scholar
  4. 4.
    Wahde, M., Hertz, J.: Modeling genetic regulatory dynamics in neural development. Journal on Computational Biology 8, 429–442 (2001)CrossRefGoogle Scholar
  5. 5.
    Mendes, P., Sha, W., Ye, K.: Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19, 122–129 (2003)CrossRefGoogle Scholar
  6. 6.
    Ram, R., Chetty, M., Dix, T.I.: Fuzzy Model for Gene Regulatory Networks. In: 2006 IEEE Congress on Evolutionary Computation (CEC) (2006)Google Scholar
  7. 7.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Ram, R., Chetty, M., Dix, T.I.: Causal Modeling of Gene Regulatory Network. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (2006)Google Scholar
  9. 9.
    Ram, R., Chetty, M., Dix, T.I.: Learning Structure of Gene Regulatory Networks. In: 6th IEEE International Conference on Computer and Information Science (ICIS) (2007)Google Scholar
  10. 10.
    Ram, R., Chetty, M.: A Guided genetic algorithm for Gene Regulatory Network. In: 2007 IEEE Congress on Evolutionary Computation (CEC) (2007)Google Scholar
  11. 11.
    Ram, R., Chetty, M.: Framework for path analysis for learning Gene regulatory network. In: Pattern Recognition in Bioinformatics. Springer – LNBI publication, Heidelberg (2007)Google Scholar
  12. 12.
    Ram, R., Chetty, M.: Modelling Gene regulatory networks. In: Applications of Computation Intelligence in biomedicine and Bioinformatics. Springer, Heidelberg (accepted, 2007)Google Scholar
  13. 13.
    Sprites, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search: Adaptive Computation and Machine Learning, 2nd edn. MIT Press, Cambridge (2000)Google Scholar
  14. 14.
    Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge (2000)Google Scholar
  15. 15.
    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
  16. 16.
    Erdös, P., Rényi, A.: On random graphs. Publ. Math. Debrecen 6, 290–297 (1959)Google Scholar
  17. 17.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefPubMedGoogle Scholar
  18. 18.
    Calvert, K.L., Doar, M.B., Zegura, E.W.: Modeling Internet topology. IEEE Communications Magazine 35, 160–163 (1997)CrossRefGoogle Scholar
  19. 19.
    Rahmel, J.: SplitNet: A Dynamic Hierarchical Network Model. In: AAAI/IAAI, vol. 2, p. 1404 (1996)Google Scholar
  20. 20.
    Featherstone, D.E., Broadie, K.: Wrestling with pleiotropy: genomic and topological analysis of the yeast gene expression network. Bioessays 24, 267–274 (2002)CrossRefPubMedGoogle Scholar
  21. 21.
    Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N., Barabási, A.-L.: The large-scale organization of metabolic networks. Nature 407, 651–654 (2000)CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ramesh Ram
    • 1
  • Madhu Chetty
    • 1
  1. 1.Gippsland School of ITMonash UniversityChurchillAustralia

Personalised recommendations