Evolutionary Approach to Gene Regulatory Networks

  • Hitoshi IbaEmail author


Gene regulatory networks (GRNs) described in this chapter are recently attracting attention as a model that can learn in a way similar to neural networks. Gene regulatory networks express the interactions between genes in an organism. We first give several inference methods to GRN. Then, we explain the real-world application of GRN to robot motion learning. We show how GRNs have generated effective motions to specific humanoid tasks. Thereafter, we explain ERNe (Evolving Reaction Network), which produces a type of genetic network suitable for biochemical systems. ERNe’s effectiveness is shown by several in silico and in vitro experiments, such as oscillator syntheses, XOR problem solving, and inverted pendulum task.


DREAM (Dialogue for Reverse Engineering Assessments and INTERNe (IEC-based GRN inference with ERNe) MONGERN (MOtioN generation by GEne regulatory networks) ERNe (Evolving Reaction Network) DNA PEN Toolbox Speciation 


  1. 1.
    Aldana, M., Balleza, E., Kauffman, S., Resendiz, O.: Robustness and evolvability in genetic regulatory networks. J. Theor. Biol. 245(3), 433–448 (2006)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Alipanahi, B., Delong, A., Weirauch, W.T., Frey, B.J.: Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015)CrossRefGoogle Scholar
  3. 3.
    Aubert, N., Dinh, Q.H., Hagiya, M., Iba, H., Fujii, T., Bredeche, N., Rondelez, Y.: Evolution of cheating DNA-based agents playing the game of rock-paper-scissors. In: Advances in Artificial Life, ECAL, vol. 12, pp. 1143–1150 (2013)Google Scholar
  4. 4.
    Chen, Y., Li, Y., Narayan, R., Subramanian, A., Xie, X.: Gene expression inference with deep learnings. Bioinformatics 32(12), 1832–1839 (2016)CrossRefGoogle Scholar
  5. 5.
    Cliff, D., Harvey, I., Husbands, P.: Explorations in evolutionary robotics. Adapt. Behavior 2, 72–110 (2000)Google Scholar
  6. 6.
    Dinh, Q.H., Aubert, N., Noman, H., Fujii, T., Rondelez, Y., Iba, H.: An effective method for evolving reaction networks in synthetic biochemical systems. IEEE Trans. Evol. Comput. 18 (2015)Google Scholar
  7. 7.
    Iba, H., Noman, N. (eds.): Evolutionary Computation in Gene Regulatory Network Research. Wiley Series in Bioinformatics. Wiley, Hoboken (2016)zbMATHGoogle Scholar
  8. 8.
    Inamura, T., Nakamura, Y.: An integrated model of imitation learning and symbol development based on Mimesis theory. Brain Neural Netw. 12(1), 74–80 (2005)CrossRefGoogle Scholar
  9. 9.
    Jin, Y., Guo, H., Meng, Y.: A hierarchical gene regulatory network for adaptive multi-robot pattern formation. IEEE Trans. Syst. Man Cybern. B Cybern. 42(3), 805–816 (2012)CrossRefGoogle Scholar
  10. 10.
    Kauffman, S.A.: The Origins of Order, Self-organization and Selection in Evolution. Oxford University Press, New York (1993)Google Scholar
  11. 11.
    Marbach, D., Costello, J.C., Kuffner, R., Vega, N., Prill, R.J., Camacho, D.M., Allison, K.R., The DREAM5 Consortium, Kellis, M., Collins, J.J., Stolovitzky, G.: Wisdom of crowds for robust gene network inference. Nat. Methods 9(8), 796–804 (2012)Google Scholar
  12. 12.
    Mendes, P., Sha, W., Ye, K.: Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19(Suppl 2), 122–129 (2003)CrossRefGoogle Scholar
  13. 13.
    Montagne, K., Plasson, R., Sakai, Y., Fujii, T., Rondelez, Y.: Programming an in vitro DNA oscillator using a molecular networking strategy. Mol. Syst. Biol. 7, 466 (2011)CrossRefGoogle Scholar
  14. 14.
    Nolfi, S., Floreano, D.: Evolutionary Robotics. MIT Press, Cambridge (2000)Google Scholar
  15. 15.
    Park, Y., Kellis, M.: Deep learning for regulatory genomics. Nat. Biotechnol. 33(8), 825–826 (2015)CrossRefGoogle Scholar
  16. 16.
    Padirac, A., Fujii, T., Rondelez, Y.: Bottom-up construction of in vitro switchable memories. Proc. Natl. Acad. Sci. (PNAS) 109(47), E3212–E3220 (2012)CrossRefGoogle Scholar
  17. 17.
    Palafox, L., Noman, N., Iba, H.: On the use of population based incremental learning to do reverse engineering on gene regulatory networks. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2012)Google Scholar
  18. 18.
    Palafox, L., Noman, N., Iba, H.: Gene regulatory network reverse engineering using population based incremental learning and K-means. In: GECCO (Companion), pp. 1423–1424 (2012)Google Scholar
  19. 19.
    Palafox, L., Noman, N., Iba, H.: Reverse engineering of gene regulatory networks using dissipative particle swarm optimization. IEEE Trans. Evol. Comput. 17(4), 577–587 (2013)CrossRefGoogle Scholar
  20. 20.
    Palafox, L., Noman, N., Iba, H.: Extending population based incremental learning using Dirichlet processes. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1686–1693 (2016)Google Scholar
  21. 21.
    Qian, L., Winfree, E.: Scaling up digital circuit computation with DNA strand displacement cascades. Science 332(6034), 1196–1201 (2011)CrossRefGoogle Scholar
  22. 22.
    Zaier, R.: Motion generation of humanoid robot based on polynomials generated by recurrent neural network. In: Proceedings of the First Asia International Symposium on Mechatronics (2004)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.The University of TokyoTokyoJapan

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