Study on the Use of Evolutionary Techniques for Inference in Gene Regulatory Networks

  • Leon Palafox
  • Nasimul Noman
  • Hitoshi Iba
Part of the Proceedings in Information and Communications Technology book series (PICT, volume 6)


Inference in Gene Regulatory Networks remains an important problem in Molecular Biology. Many models have been proposed to model the relationships within genes in a DNA chain. Many of these models use Evolutionary Techniques to find the best parameters of specific DNA motifs.

In this work, we compare the popular S-System using a powerful evolutionary technique, DPSO, and the novel Recursive Neural Network model, using clustered (PBIL). We will use the SOS network for E.coli to do the comparison to finally show how they fare against other techniques in the area of (GRN) inference.


  1. 1.
    Noman, N., Iba, H.: Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(4), 634–647 (2007)CrossRefGoogle Scholar
  2. 2.
    Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K., Tomita, M.: Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 19(5), 643 (2003)CrossRefGoogle Scholar
  3. 3.
    Xu, R., Donald Wunsch, I.I., Frank, R.: Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 681–692 (2007)Google Scholar
  4. 4.
    Xie, X.F., Zhang, W.J., Yang, Z.L.: Dissipative particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 2, pp. 1456–1461. IEEE (2002)Google Scholar
  5. 5.
    Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pacific Symposium on Biocomputing, vol. 4, pp. 17–28. Citeseer (1999)Google Scholar
  6. 6.
    Murphy, K., Mian, S.: Modelling gene expression data using dynamic Bayesian networks. Graphical Models, 12 (1999)Google Scholar
  7. 7.
    Noman, N., Iba, H.: Inference of gene regulatory networks using s-system and differential evolution. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, Washington, DC, p. 439. Citeseer (2005)Google Scholar
  8. 8.
    Kimura, S., Ide, K., Kashihara, A., Kano, M., Hatakeyama, M., Masui, R., Nakagawa, N., Yokoyama, S., Kuramitsu, S., Konagaya, A.: Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 21(7), 1154 (2005)CrossRefGoogle Scholar
  9. 9.
    Vohradský, J.: Neural network model of gene expression. The FASEB Journal: official publication of the Federation of American Societies for Experimental Biology 15(3), 846–854 (2001)CrossRefGoogle Scholar
  10. 10.
    Pettersson, F., Biswas, A., Sen, P.K., Saxén, H., Chakraborti, N.: Analyzing Leaching Data for Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms. Materials and Manufacturing Processes 24(3), 320–330 (2009)CrossRefGoogle Scholar
  11. 11.
    Zamparelli, M.: Genetically Trained Cellular Neural Networks. Neural Networks: The Official Journal of the International Neural Network Society 10(6), 1143–1151 (1997)CrossRefGoogle Scholar
  12. 12.
    Savageau, M.A.: Biochemical systems analysis+*:: I. Some mathematical properties of the rate law for the component enzymatic reactions. Journal of Theoretical Biology 25(3), 365–369 (1969)CrossRefGoogle Scholar
  13. 13.
    Tominaga, D., Koga, N., Okamoto, M.: Efficient numerical optimization algorithm based on genetic algorithm for inverse problem. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 251, p. 258 (2000)Google Scholar
  14. 14.
    Palafox, L., Hashimoto, H.: 4W1H and Particle Swarm Optimization for Human Activity Recognition. Journal of Advanced Computational Intelligence and Intelligent Informatics 15(7), 793–799 (2011)CrossRefGoogle Scholar
  15. 15.
    AlRashidi, M., El-Hawary, M.: A survey of particle swarm optimization applications in electric power systems. IEEE Transactions on Evolutionary Computation 13(4), 913–918 (2009)CrossRefGoogle Scholar
  16. 16.
    Liao, C., Luarn, P.: A discrete version of particle swarm optimization for flowshop scheduling problems. Computers & Operations Research 34(10), 3099–3111 (2007)CrossRefGoogle Scholar
  17. 17.
    Sebag, M., Ducoulombier, A.: Extending population-based incremental learning to continuous search spaces. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 418–427. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  18. 18.
    Emmendorfer, L., Pozo, A.: Effective Linkage Learning Using Low-Order Statistics and Clustering. IEEE Transactions on Evolutionary Computation 13(6), 1233–1246 (2009)CrossRefGoogle Scholar
  19. 19.
    Ronen, M., Rosenberg, R., Shraiman, B.I., Alon, U.: Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proceedings of the National Academy of Sciences 99, 10555 (2002)CrossRefGoogle Scholar
  20. 20.
    Perrin, B.E., Ralaivola, L., Mazurie, A., Bottani, S., Mallet, J., D’Alche-Buc, F.: Gene networks inference using dynamic Bayesian networks. Bioinformatics 19(suppl. 2), ii138–ii148 (2003)CrossRefGoogle Scholar
  21. 21.
    Cho, D.Y., Cho, K.H., Zhang, B.T.: Identification of biochemical networks by S-tree based genetic programming. Bioinformatics 22, 1631–1640 (2006)CrossRefGoogle Scholar
  22. 22.
    Kabir, M., Noman, N., Iba, H.: Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinformatics 11(suppl. 1), S56 (2010)CrossRefGoogle Scholar

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Authors and Affiliations

  • Leon Palafox
    • 1
  • Nasimul Noman
    • 2
  • Hitoshi Iba
    • 2
  1. 1.Department of Electric EngineeringUniversity of TokyoJapan
  2. 2.Department of Information, Science and TechnologyUniversity of TokyoJapan

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