Evolutionary Constraint in Artificial Gene Regulatory Networks

  • Alexander P. TurnerEmail author
  • George Lacey
  • Annika Schoene
  • Nina Dethlefs
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)


Evolutionary processes such as convergent evolution and rapid adaptation suggest that there are constraints on how organisms evolve. Without constraint, such processes would most likely not be possible in the time frame in which they are seen. This paper investigates how artificial gene regulatory networks (GRNs), a connectionist architecture designed for computational problem solving may too be constrained in its evolutionary pathway. To understand this further, GRNs are applied to two different computational tasks and the way their underlying genes evolve over time is observed. From this, rules about how often genes are evolved and how this correlates with their connectivity within the GRN are deduced. By generating and applying these rules, we can build an understanding of how GRNs are constrained in their evolutionary path, and build measures to exploit this to improve evolutionary performance and speed.


Artificial gene regulatory networks Evolutionary dynamics Computational optimisation 


  1. 1.
    Pigliucci, M.: Is evolvability evolvable? Nat. Rev. Genet. 9(1), 75–82 (2008)CrossRefGoogle Scholar
  2. 2.
    Li, J., Yuan, Z., Zhang, Z.: The cellular robustness by genetic redundancy in budding yeast. PLoS Genet. 6(11), e1001187 (2010)CrossRefGoogle Scholar
  3. 3.
    Tokuriki, N., Tawfik, D.S.: Protein dynamism and evolvability. Science 324(5924), 203–207 (2009)CrossRefGoogle Scholar
  4. 4.
    Pavlicev, M., Wagner, G.P.: A model of developmental evolution: selection, pleiotropy and compensation. Trends Ecol. Evol. 27(6), 316–322 (2012)Google Scholar
  5. 5.
    Graves, C.J., Ros, V.I., Stevenson, B., Sniegowski, P.D., Brisson, D.: Natural selection promotes antigenic evolvability. PLoS Pathog 9(11), e1003766 (2013)CrossRefGoogle Scholar
  6. 6.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)Google Scholar
  7. 7.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (2013)zbMATHGoogle Scholar
  8. 8.
    Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theor. Comput. Sci. 276(1–2), 51–81 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  10. 10.
    Hamann, H., Schmickl, T., Crailsheim, K.: Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 195–202. ACM (2011)Google Scholar
  11. 11.
    Dowsland, K.A., Thompson, J.M.: Simulated annealing. In: Handbook of Natural Computing, pp. 1623–1655. Springer (2012)Google Scholar
  12. 12.
    Castro, L.N.D., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer Science & Business Media (2002)Google Scholar
  13. 13.
    Schwab, J.D., Siegle, L., Kühlwein, S.D., Kühl, M., Kestler, H.A.: Stability of signaling pathways during aging-a boolean network approach. Biology 6(4), 46 (2017)Google Scholar
  14. 14.
    Turner, A.P., Caves, L.S., Stepney, S., Tyrrell, A.M., Lones, M.A.: Artificial epigenetic networks: Automatic decomposition of dynamical control tasks using topological self-modification (2016)Google Scholar
  15. 15.
    Ribba, B., Grimm, H.P., Agoram, B., Davies, M.R., Gadkar, K., Niederer, S., van Riel, N., Timmis, J., van der Graaf, P.H.: Methodologies for quantitative systems pharmacology (QSP) models: design and estimation. CPT Pharmacometrics Syst. Pharmacol. 6(8), 496–498 (2017)Google Scholar
  16. 16.
    Kirschner, M.: Beyond Darwin: evolvability and the generation of novelty. BMC Biol. 11(1), 1 (2013)CrossRefGoogle Scholar
  17. 17.
    Kitano, H.: Biological robustness. Nat. Rev. Genet. 5(11), 826–837 (2004)CrossRefGoogle Scholar
  18. 18.
    Palazzo, A.F., Gregory, T.R.: The case for junk DNA. PLoS Genet. 10(5), e1004351 (2014)CrossRefGoogle Scholar
  19. 19.
    Pennisi, E.: Encode project writes eulogy for junk DNA (2012)Google Scholar
  20. 20.
    Zotenko, E., Mestre, J., O’Leary, D.P., Przytycka, T.M.: Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality. PLoS Comput. Biol. 4(8), e1000140 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Silva, F., Duarte, M., Correia, L., Oliveira, S.M., Christensen, A.L.: Open issues in evolutionary robotics. Evol. Comput. 24(2), 205–236 (2016)CrossRefGoogle Scholar
  22. 22.
    Valero, K.W.: Aligning functional network constraint to evolutionary outcomes. bioRxiv (2018)Google Scholar
  23. 23.
    Pervouchine, D.D., Djebali, S., Breschi, A., Davis, C.A., Barja, P.P., Dobin, A., Tanzer, A., Lagarde, J., Zaleski, C., See, L.-H., et al.: Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression. Nat. Commun. 6, 5903 (2015)CrossRefGoogle Scholar
  24. 24.
    Foote, A.D., Liu, Y., Thomas, G.W.C., Vinař, T., Alföldi, J., Deng, J., Dugan, S., van Elk, C.E., Hunter, M.E., Joshi, V., et al.: Convergent evolution of the genomes of marine mammals. Nat. Genet. 47(3), 272 (2015)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hull UniversityHullUK

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