Genetic Algorithms and Their Application to In Silico Evolution of Genetic Regulatory Networks

  • Johannes F. KnabeEmail author
  • Katja Wegner
  • Chrystopher L. Nehaniv
  • Maria J. Schilstra
Part of the Methods in Molecular Biology book series (MIMB, volume 673)


A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through “mutation” and “recombination” in a “population” of possible solutions to a problem, encoded as strings of characters in “genomes,” and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the “fitter” individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.

In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. In this example, a GRN-like controller is sought for a developmental system based on Lewis Wolpert’s French flag model for positional specification, in which cells in a growing embryo secrete and detect morphogens to attain a specific spatial pattern of cellular differentiation.

Key words

Evolutionary computation Genetic algorithm Genetic regulatory network Modeling Simulation Gene regulation logic Developmental program 



Cellular Potts model


Cis-regulatory module


Evolutionary computing


Genetic algorithm


Gene product (protein or RNA)


Genetic regulatory network


Trans-regulatory factor


  1. 1.
    Holland, J. H. (1962) Outline for a logical theory of adaptive systems. JACM 9, 297–314.CrossRefGoogle Scholar
  2. 2.
    Holland, J. H. (1992) Adaptation in natural and artificial systems, 2nd edition, MIT Press, Cambridge, MA.Google Scholar
  3. 3.
    Mitchell, M. (1998) An introduction to genetic algorithms, MIT Press, Cambridge, MA.Google Scholar
  4. 4.
    Back, T., Fogel, D. B., and Michalewicz, Z. (1999) Evolutionary algorithms, Vol. I and II, IOP, Bristol, UK.Google Scholar
  5. 5.
    Schilstra, M. J., and Nehaniv, C. L. (2008) Bio-logic: gene expression and the laws of combinatorial logic. Artif Life 14, 121–33.PubMedCrossRefGoogle Scholar
  6. 6.
    Karlebach, G., and Shamir, R. (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9, 770–80.PubMedCrossRefGoogle Scholar
  7. 7.
    Kulasiri, D., Nguyen, L. K., Samarasinghe, S., and Xie, Z. (2008) A review of systems biology perspective on genetic regulatory networks with examples. Curr Bioinform 3, 197–225.CrossRefGoogle Scholar
  8. 8.
    Mitrophanov, A. Y., and Groisman, E. A. (2008) Positive feedback in cellular control systems. Bioessays 30, 542–55.PubMedCrossRefGoogle Scholar
  9. 9.
    Cho, K. H., Choo, S. M., Jung, S. H., Kim, J. R., Choi, H. S., and Kim, J. (2007) Reverse engineering of gene regulatory networks. IET Syst Biol 1, 149–63.PubMedCrossRefGoogle Scholar
  10. 10.
    Goutsias, J., and Lee, N. H. (2007) Computational and experimental approaches for modeling gene regulatory networks. Curr Pharm Design 13, 1415–36.CrossRefGoogle Scholar
  11. 11.
    Tomlin, C. J., and Axelrod, J. D. (2007) Biology by numbers: mathematical modelling in developmental biology. Nat Rev Genet 8, 331–40.PubMedCrossRefGoogle Scholar
  12. 12.
    Palumbo, M. C., Farina, L., Colosimo, A., Tun, K., Dhar, P. K., and Giuliani, A. (2006) Networks everywhere? Some general implications of an emergent metaphor. Curr Bioinform 1, 219–34.CrossRefGoogle Scholar
  13. 13.
    Kaznessis, Y. N. (2006) Multi-scale models for gene network engineering. Chem Eng Sci 61, 940–53.CrossRefGoogle Scholar
  14. 14.
    Facciotti, M. T., Bonneau, R., Hood, L., and Baliga, N. S. (2004) Systems biology experimental design – considerations for building predictive gene regulatory network models for prokaryotic systems. Curr Genomics 5, 527–44.CrossRefGoogle Scholar
  15. 15.
    Welch, S. M., Dong, Z. S., Roe, J. L., and Das, S. (2005) Flowering time control: gene network modelling and the link to quantitative genetics. Aust J Agric Res 56, 919–36.CrossRefGoogle Scholar
  16. 16.
    Prusinkiewicz, P. (2004) Modeling plant growth development. Curr Opin Plant Biol 7, 79–83.PubMedCrossRefGoogle Scholar
  17. 17.
    Kaern, M., Blake, W. J., and Collins, J. J. (2003) The engineering of gene regulatory networks. Annu Rev Biomed Eng 5, 179–206.PubMedCrossRefGoogle Scholar
  18. 18.
    Thieffry, D., and Sanchez, L. (2003) Dynamical modelling of pattern formation during embryonic development. Curr Opin Gen Dev 13, 326–30.CrossRefGoogle Scholar
  19. 19.
    Bolouri, H., and Davidson, E. H. (2002) Modeling transcriptional regulatory networks. Bioessays 24, 1118–29.PubMedCrossRefGoogle Scholar
  20. 20.
    De Jong, H. (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9, 67–103.PubMedCrossRefGoogle Scholar
  21. 21.
    Shmulevich, I., Dougherty, E. R., and Mang, W. (2002) From Boolean to probabilistic Boolean networks as models of genetic regulatory networks. Proc IEEE 90, 1778–92.CrossRefGoogle Scholar
  22. 22.
    Hasty, J., McMillen, D., and Collins, J. J. (2002) Engineered gene circuits. Nature 420, 224–30.PubMedCrossRefGoogle Scholar
  23. 23.
    Van Someren, E. P., Wessels, L. F. A., Backer, E., and Reinders, M. J. T. (2002) Genetic network modeling. Pharmacogenomics 3, 507–25.PubMedCrossRefGoogle Scholar
  24. 24.
    Rao, C. V., and Arkin, A. P. (2001) Control motifs for intracellular regulatory networks Annu Rev Biomed Eng 3, 391–419.PubMedCrossRefGoogle Scholar
  25. 25.
    Hasty, J., McMillen, D., Isaacs, F., and Collins, J. J. (2001) Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet 2, 268–79.PubMedCrossRefGoogle Scholar
  26. 26.
    Smolen, P., Baxter, D. A., and Byrne, J. H. (2000) Modeling transcriptional control in gene networks – methods, recent results, and future directions. Bull Math Biol 62, 247–92.PubMedCrossRefGoogle Scholar
  27. 27.
    McAdams, H. H., and Arkin, A. (1998) Simulation of prokaryotic genetic circuits. Annu Rev Biophys Biomol Struct 27, 199–224.PubMedCrossRefGoogle Scholar
  28. 28.
    Kauffman, S. A. (1993) The origins of order. Self-organization and selection in evolution, Oxford University Press, New York.Google Scholar
  29. 29.
    Wolpert, L. (1969) Positional information and the spatial pattern of cellular differentiation. J Theor Biol 25, 1–47.PubMedCrossRefGoogle Scholar
  30. 30.
    Wolpert, L. (1996) One hundred years of positional information. Trends Genet 12, 359–64.PubMedCrossRefGoogle Scholar
  31. 31.
    Jaeger, J., and Reinitz, J. (2006) On the dynamic nature of positional information. BioEssays 28, 1102–11.PubMedCrossRefGoogle Scholar
  32. 32.
    Miller, J. F. (2004) Evolving a self-repairing, self-regulating, French Flag Organism, in Proceedings of genetic and evolutionary computation conference – GECCO 2004, (Kalyanmoy Deb, Riccardo Poli, Wolfgang Banzhaf, Hans-Georg Beyer, Edmund K.Burke, Paul J. Darwen, Dipankar Dasgupta, Dario Floreano, James A. Foster, Mark Harman, Owen Holland, Pier Luca Lanzi, Lee Spector, Andrea Tettamanzi, Dirk Thierens, Andrew M. Tyrrell, Eds.) vol 1, pp 129–139, Springer, Berlin.Google Scholar
  33. 33.
    Meinhardt, H. (1982) Models of biological pattern formation, Academic Press, London.Google Scholar
  34. 34.
    Knabe, J. F., Schilstra, M. J., and Nehaniv, C. L. (2008) Evolution and morphogenesis of differentiated multicellular organisms: Autonomously generated diffusion gradients for positional information, in Artificial Life XI: Proc eleventh international conference on the simulation and synthesis of living systems (Bullock S., Noble, J., Watson, R., and Bedau, M., Eds.), pp 321–8, MIT Press, Cambridge, MA, USA.Google Scholar
  35. 35.
    Glazier, J. A., and Graner, F. (1993) Simulation of the differential adhesion driven rearrangement of biological cells. Phys Rev E 47, 2128–54.CrossRefGoogle Scholar
  36. 36.
    Merks, R. M. H., and Glazier, J. A. (2005) A cell-centered approach to developmental biology. Physica A 352, 113–30.CrossRefGoogle Scholar
  37. 37.
    De Jong, H., Geiselmann, J., Hernandez, C., and Page, M. (2003) Genetic Network Analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 19, 336–44.PubMedCrossRefGoogle Scholar
  38. 38.
    Gonzalez, A. G., Naldi, A., Sánchez, L., Thieffry, D., and Chaouiya, C. (2006) GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks. Biosystems 84, 91–100.PubMedCrossRefGoogle Scholar
  39. 39.
    Schilstra, M. J., Martin, S. R., and Keating, S. M. (2008) Methods for simulating the dynamics of complex biological processes, in Biophysical tools for the biologist (Correia, J. J., and Dietrich, W. H., Eds.), Methods in Molecular Biology, (Wilson, L., and Matsudaira, P. T., Eds.), pp 807–841, Elsevier, San Diego.Google Scholar
  40. 40.
    Szallasi, Z., Stelling, J., and Periwal, V., (Eds.) (2006) System modeling in cellular biology, MIT Press, Cambridge, MA.Google Scholar
  41. 41.
    Thain, D., Tannenbaum, T., and Livny, M. (2005) Distributed computing in practice: the Condor experience. Concurr Comput 17, 323–56.CrossRefGoogle Scholar
  42. 42.
    Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983) Optimization by simulated annealing. Science 220, 671–80.PubMedCrossRefGoogle Scholar
  43. 43.
    Cerny, V. (1985) A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. J Optim Theory App 45, 41–51.CrossRefGoogle Scholar
  44. 44.
    Beyer, H. G., and Schwefel, H. P. (2002) Evolution strategies: a comprehensive introduction. Natural Comput 1, 3–52.CrossRefGoogle Scholar
  45. 45.
    Knabe, J. F., Nehaniv, C. L., and Schilstra, M. J. (2006) Evolutionary robustness of differentiation in genetic regulatory networks, in Proc 7th German Workshop on Artificial Life 2006 (GWAL-7) (Artman, S., and Dittrich, P., Eds.), pp 75–84, Akademische Verlagsge-sellschaft Aka, Berlin.Google Scholar
  46. 46.
    Knabe, J. F., Nehaniv, C. L., and Schilstra, M. J. (2008) Genetic regulatory network models of biological clocks: evolutionary history matters. Artif Life 14, 135–48.PubMedCrossRefGoogle Scholar
  47. 47.
    Altenberg, L. (1994) The evolution of evolvability in genetic programming, in Advances in genetic programming (Kinnear, K. E., Ed.), pp 47–74, MIT Press, Cambridge, MA, USA.Google Scholar
  48. 48.
    Beer, R. D. (2004) Autopoiesis and cognition in the game of life. Artif Life 10, 309–26.PubMedCrossRefGoogle Scholar
  49. 49.
    Longabaugh, W. J. R., Davidson, E. H., and Bolouri, H. (2005) Computational representation of developmental genetic regulatory networks. Dev Biol 283, 1–16.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Johannes F. Knabe
    • 1
    Email author
  • Katja Wegner
    • 2
  • Chrystopher L. Nehaniv
    • 1
  • Maria J. Schilstra
    • 3
  1. 1.Biological and Neural Computation Laboratory and Adaptive Systems Research Group, STRIUniversity of HertfordshireHatfieldUK
  2. 2.Albstadt-Sigmaringen UniversitySigmaringenGermany
  3. 3.Biological and Neural Computation LaboratoryUniversity of HertfordshireHatfieldUK

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