Skip to main content

Advertisement

SpringerLink
Log in

The Regulatory Network Computational Device

  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

Evolutionary Algorithms (EA) approach the genotype–phenotype relationship differently than does nature, and this discrepancy is a recurrent issue among researchers. Moreover, in spite of some performance improvements, it is a fact that biological knowledge has advanced faster than our ability to incorporate novel biological ideas into EAs. Recently, some researchers have started exploring computationally new comprehension of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Gene Regulatory Network (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN were tested. In this paper, we describe one of those, the Regulatory Network Computational Device, demonstrating experimentally its capabilities. The efficacy and efficiency of this alternative is tested experimentally using typical benchmark problems for Genetic Programming (GP) systems. We devise a modified factorial problem to investigate the use of feedback connections and the scalability of the approach. In order to gain a better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. W. Banzhaf, Artificial regulatory networks and genetic programming. in Genetic programming theory and practice, chap. 4, ed by R.L. Riolo, B. Worzel (Kluwer, Dordrecht, 2003) pp. 43–62.

    Google Scholar 

  2. W. Banzhaf, G. Beslon, S. Christensen, J. Foster, F. Képès, V. Lefort, J. Miller, M. Radman, J. Ramsden, From artificial evolution to computational evolution: a research agenda. Nat. Rev. Genet. 7(9), 729–735 (2006)

    Article  Google Scholar 

  3. H. Bolouri, Computational modeling of gene regulatory networks: a primer. (Imperial College Press, London, 2008)

    Google Scholar 

  4. J. Bongard, Evolving modular genetic regulatory networks. in IEEE 2002 Congress on Evolutionary Computation (CEC2002) (IEEE Press, New York, 2002), pp. 1872–1877

  5. F. Crick, Central dogma of molecular biology. Nature 227, 561–563 (1970)

    Article  Google Scholar 

  6. E.H. Davidson, The regulatory genome: gene regulatory networks in development and evolutione. (Academic Press, London, 2006)

    Google Scholar 

  7. P. Dwight Kuo, W. Banzhaf, A. Leier, Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. Bio Syst. 85(3), 177–200 (2006)

    Google Scholar 

  8. P. Eggenberger, Evolving morphologies of simulated 3D organisms based on differential gene expression. in Fourth European Conference of Artificial Life, ed by P. Husbands, I. Harvey (MIT Press, Cambridge, MA, 1997) pp. 205–213.

    Google Scholar 

  9. A.E.B. Eiben, J.E. Smith, Introduction to Evolutionary Computing. (Springer, Berlin, 2003)

    MATH  Google Scholar 

  10. C. Ferreira, Genetic representation and genetic neutrality in gene expression programming. Adv. Complex Syst. 5(4), 389–408 (2002)

    Article  MATH  Google Scholar 

  11. C. Ferreira, Gene Expression Programming , 2nd edn. (Springer, Berlin, 2006)

    Google Scholar 

  12. A.P. Field, G. Hole, How to design and report experiments. (Sage, Beverley Hills, CA, 2003)

    Google Scholar 

  13. E. Galván-López, R. Poli, A. Kattan, M. ONeill, A. Brabazon, Neutrality in evolutionary algorithms… what do we know?. Evol. Syst. 2(3), 145–163 (2011)

    Article  Google Scholar 

  14. Â. Gonçalves, E. Costa. A computational model of gene regulatory networks and its topological properties, in Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, 2008, ed. by S. Bullock, J. Noble, R. Watson, M.A. Bedau, pp. 204–211

  15. Â. Gonçalves, E. Costa, A model for an heterogeneous gene regulatory network. in Handbook of Research on Computational Methodologies in Gene Regulatory Networks, chap. Chap. 12, ed by S. Das, D. Caragea, S.M. Welch, W.H. Hsu (IGI Global, Hershey, PA, 2009)

    Google Scholar 

  16. S. Harding, J. Miller, W. Banzhaf, Self modifying cartesian genetic programming: Fibonacci, squares, regression and summing. in Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, LNCS, vol. 5481, ed by L. Vanneschi, S. Gustafson, A. Moraglio, I. Falco, M. Ebner (Springer, Tuebingen, 2009) pp. 133–144.

    Google Scholar 

  17. S. Harding, J.F. Miller, W. Banzhaf, Developments in cartesian genetic programming: self-modifying cgp. Genet. Program Evolvable Mach. 11(3–4), 397–439 (2010)

    Article  Google Scholar 

  18. M. Hecker, S. Lambeck, S. Toefler, van E. Soemren, R. Guthke, Gene regulatory network inference: data integration in dynamic models—a review. BioSystems 96(1), 86–103 (2009)

    Article  Google Scholar 

  19. L. Huelsbergen, Finding general solutions to the parity problem by evolving machine-language representations. in Genetic Programming 1998: Proceedings of the Third Annual Conference, ed by J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, R. Riolo (Morgan Kaufmann, University of Wisconsin, Madison, WI, 1998) pp. 158–166.

    Google Scholar 

  20. F. Jacob, J. Monod, Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3(3), 318–356 (1961)

    Article  Google Scholar 

  21. M. Joachimczak, B. Wrobel, Evo-devo in silico: a model of a gene network regulating multicellular development in 3d space with artificial physics. in Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, ed by S. Bullock, J. Noble, R. Watson, M.A. Bedau (MIT Press, Cambridge, MA, 2008) pp. 297–304.

    Google Scholar 

  22. H. de Jong, Modeling and simulation of genetic regulatory systems: a litterature review. J. Comput. Biol. 9(1), 67–103 (2002)

    Article  Google Scholar 

  23. H. de Jong, J.L. Gouzé, C. Hernandez, M. Page, T. Sari, J. Geiselmann. Hybrid modeling and simulation of genetic regulatory networks. in Proceedings of the 6th International Conference on Hybrid Systems: Computation and Control, HSCC’03. (Springer, Berlin, 2003), pp. 267–282

  24. S. Kauffman, The origins of order: self-organization and selection of evolution. (Oxford University Press, Oxford, 1993)

    Google Scholar 

  25. P. Kennedy, T. Osborn, A model of gene expression and regulation in an artificial cellular organism. Compl. Syst. 13(1), 33–59 (2001)

    MathSciNet  MATH  Google Scholar 

  26. M. Kessler, Analysis of the dynamics of a GRN-based evo-devo system. Ph.D. thesis, University of Zurich (2009)

  27. J. Knabe, M. Schilstra, C. Nehaviv, Evolution and morphogenesis of differential multicellular organisms: autonomously generated diffusion gradients for positional information. in Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, ed by S. Bullock, J. Noble, R. Watson, M.A. Bedau (MIT Press, Cambridge, MA, 2008) pp. 321–328.

    Google Scholar 

  28. J. Koza, M. Keane, Genetic breeding of non-linear optimal control strategies for broom balancing. Anal. Optim. Syst. 144, 47–56 (1990)

    Article  Google Scholar 

  29. J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. (MIT Press, Cambridge, MA, 1992)

    MATH  Google Scholar 

  30. J.R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs (Complex Adaptive Systems). (MIT Press, Cambridge, MA, 1994)

    Google Scholar 

  31. P. Kuo, A. Leier, W. Banzhaf, Evolving dynamics in an artificial regulatory network model. in Proceedings of the Parallel Problem Solving from Nature Conference (PPSN-04), ed by X. Yao, E. Burke, J.A. Lozano, J. Smith, J.J. Merelo-Guervós, J.A. Bullinaria, J. Rowe, P. Tino, A. Kabán, H.P. Schwefel (Springer, Berlin, 2004) pp. 571–580.

    Google Scholar 

  32. T. Kuyucu, M.A. Trefzer, J.F. Miller, A.M. Tyrrell, A scalable solution to n-bit parity via artificial development. in 5th International Conference on PhD Research in Microelectronics and Electronics (PRIME), 2009, pp. 144–147

  33. W.B. Langdon, R. Poli, Why ants are hard. in Genetic Programming 1998: Proceedings of the Third Annual Conference, CSRP-98-4, ed by J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, R. Riolo (University of Birmingham, School of Computer Science, Morgan Kaufmann, University of Wisconsin, Madison, WI, USA, 1998) pp. 193–201.

    Google Scholar 

  34. M.A. Lones, A.M. Tyrrell, Biomimetic representation with genetic programming enzyme. Genet. Program Evolvable Mach. 3(1), 193–217 (2002)

    Article  MATH  Google Scholar 

  35. R.L. Lopes, E. Costa, Rencode: a regulatory network computational device. In: S. Silva, J.A. Foster, M. Nicolau, M. Giacobini, P. Machado (eds) EuroGP ’11: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, LNCS, vol. 6621, (Springer, Turin, Italy, 2011) pp. 143–154.

    Google Scholar 

  36. R.L. Lopes, E. Costa, The squares problem and a neutrality analysis with rencode. in Proceedings of the 15th Portugese Conference on Progress in Artificial Intelligence, EPIA’11 (Springer, Berlin, 2011), pp. 182–195

  37. R.L. Lopes, E. Costa, Using feedback in a regulatory network computational device. in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO ’11 (ACM, New York, NY, 2011), pp. 1499–1506

  38. D. Marbach, C. Mattiussi, D. Floreano, Bio-mimetic evolutionary reverse engineering of gene regulatory networks. In: E. Marchiori, J.H. Moore, J.C. Rajapakse (eds) 5th European Conference on Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics, EvoBIO2007, LNCS, vol. 4447, (Springer, Berlin, 2007) pp. 155–165.

    Chapter  Google Scholar 

  39. J.F. Miller, Cartesian Genetic Programming. (Springer, Berlin, 2011)

    Book  MATH  Google Scholar 

  40. M. Nicolau, M. Schoenauer et al., Evolving specific network statistical properties using a gene regulatory network model. in GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ed by G. Raidl (ACM, Montreal, 2009) pp. 723–730.

    Chapter  Google Scholar 

  41. M. Nicolau, M. Schoenauer, W. Banzhaf, Evolving genes to balance a pole. in Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010, LNCS, vol. 6021, ed by A.I. Esparcia-Alcazar, A. Ekart, S. Silva, S. Dignum, A.S. Uyar (Springer, Istanbul, 2010) pp. 196–207.

    Google Scholar 

  42. N. Noman, H. Iba, Inferring gene regulatory networks using differential evolution with local search heuristics. Computational Biology and Bioinformatics. IEEE/ACM Transactions on 4(4), 634–647 (2007)

    Google Scholar 

  43. M. O’Neill, C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language. (Kluwer Academic Publishers, Dordrecht, 2003)

    MATH  Google Scholar 

  44. M. O’Neill, L. Vanneschi, S. Gustafson, W. Banzhaf, Open issues in genetic programming. Genet. Program Evolvable Mach. 11, 339–363 (2010)

    Article  Google Scholar 

  45. T. Quick, C. Nehaviv, K. Dautenhahn, G. Roberts, Evolving embodied genetic regulatory network-driven control systems. in Proceedings of the European Conference on Artificial Life (ECAL 2003), Lecture Notes in Artificial Intelligence, vol. 2801, ed by W. Banzhaf, 2003, pp. 266–277

  46. T. Reil, Dynamics of gene expression in an artifical genome—implications for biological and artificial ontogeny. in Proceedings of the 5th European conference on Artificial Life, ed by D. Floreano, J.D. Nicoud, F. Mondada (Springer, Berlin, 1999) pp. 457–466.

    Google Scholar 

  47. D. Roggen, D. Federici, D. Floreano, Evolutionary morphogenesis for multi-cellular systems. Genet. Program Evolvable Mach. 8(1), 61–96 (2006)

    Article  Google Scholar 

  48. E. Sakamoto, H. Iba, Inferring a system of differential equations for a gene regulatory network by using genetic programming. in Proceedings of the Congress on Evolutionary Computation (IEEE Press, New York, 2001), pp. 720–726

  49. L. Spector, K. Stoffel, Ontogenetic programming. in Proceedings of the First Annual Conference on Genetic Programming, ed by J.R. Koza, D.E. Goldberg, D.B. Fogel, R.L. Riolo (MIT Press, Stanford University, CA, USA, 1996) pp. 394–399

    Google Scholar 

  50. F. Streichert, H. Planatscher, C. Spieth, H. Ulmer, A. Zell, Comparing genetic programming and evolution strategies on inferring gene regulatory networks. in Proceeding of Genetic and Evolutionary computation Conference (GECCO2004), LNCS, vol. 3102, 2004, pp. 471–480

  51. S.A. Teichmann, M.M. Babu, Gene regulatory network growth by duplication. Nat. Genet. 36(5), 492–6 (2004)

    Article  Google Scholar 

  52. D. Whitley, M. Richards, R. Beveridge, Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state gp. in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO ’06, (ACM, New York, NY, 2006), pp. 919–926

  53. M.L. Wong, K.S. Leung, Evolving recursive functions for the even-parity problem using genetic programming. in Advances in Genetic Programming, (MIT Press, Cambridge, MA, 1996), pp. 221–240

  54. M.L. Wong, T. Mun, Evolving recursive programs by using adaptive grammar based genetic programming. Genet. Program Evolvable Mach. 6(1), 421–455 (2005)

    Article  Google Scholar 

  55. T. Yu, J.F. Miller, Through the interaction of neutral and adaptive mutations, evolutionary search finds a way. Artif. Life 12(4), 525–551 (2006)

    Article  Google Scholar 

Download references

Acknowledgments

The authors express their gratitude to the editor and the anonymous reviewers for their insightful comments. The work of the first author is funded by Fundação para a Ciência e Tecnologia, grant ref. SFRH / BD / 69106 / 2010.

Author information

Authors and Affiliations

  1. Center for Informatics and Systems of the University of Coimbra, Polo II, Pinhal de Marrocos, 3030-290, Coimbra, Portugal

    Rui L. Lopes & Ernesto Costa

Authors
  1. Rui L. Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ernesto Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rui L. Lopes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lopes, R.L., Costa, E. The Regulatory Network Computational Device. Genet Program Evolvable Mach 13, 339–375 (2012). https://doi.org/10.1007/s10710-012-9160-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10710-012-9160-y

Keywords

Search

Navigation