Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks

  • Daniel Marbach
  • Claudio Mattiussi
  • Dario Floreano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4447)

Abstract

The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new method for reverse engineering genetic regulatory networks from gene expression data; 2) We demonstrate how nonlinear gene networks can be inferred from steady-state data alone. The reverse engineering method is based on an evolutionary algorithm that employs a novel representation called Analog Genetic Encoding (AGE), which is inspired from the natural encoding of genetic regulatory networks. AGE can be used with biologically plausible, nonlinear gene models where analytical approaches or local gradient based optimisation methods often fail. Recently there has been increasing interest in reverse engineering linear gene networks from steady-state data. Here we demonstrate how more accurate nonlinear dynamical models can also be inferred from steady-state data alone.

Keywords

Systems Biology Gene Networks Reverse Engineering Steady-State Data Genetic Algorithm Analog Genetic Encoding (AGE). 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gardner, T.S., di Bernardo, D., Lorenz, D., Collins, J.J.: Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629), 102–105 (2003)CrossRefGoogle Scholar
  2. 2.
    D’Haeseleer, P., Wen, X., Fuhrman, S., Somogyi, R.: Linear modeling of mRNA expression levels during CNS development and injury. In: Pac. Symp. Biocomput., pp. 41–52 (1999)Google Scholar
  3. 3.
    Brazhnik, P.: Inferring gene networks from steady-state response to single-gene perturbations. J. Theor. Biol. 237(4), 427–440 (2005)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Corne, D., Pridgeon, C.: Investigating issues in the reconstructability of genetic regulatory networks. Congress on Evolutionary Computation (2004)Google Scholar
  5. 5.
    Yeung, M.K.S., Tegnér, J., Collins, J.J.: Reverse engineering gene networks using singular value decomposition and robust regression. PNAS 99(9), 6163–6168 (2002)CrossRefGoogle Scholar
  6. 6.
    Ljung, L.: System identification: Theory for the user. Prentice Hall, Upper Saddle River (1999)Google Scholar
  7. 7.
    Bäck, T., Fogel, D., Michalewicz, Z.: Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics, Bristol (2000)Google Scholar
  8. 8.
    Mattiussi, C.: Evolutionary synthesis of analog networks. PhD thesis, Ecole Polytechnique Fédérale de Lausanne, Lausanne (2005)Google Scholar
  9. 9.
    Mattiussi, C., Floreano, D.: Analog Genetic Encoding for the Evolution of Circuits and Networks. IEEE Transactions on Evolutionary Computation (To appear, 2006)Google Scholar
  10. 10.
    Dürr, P., Mattiussi, C., Floreano, D.: Neuroevolution with analog genetic encoding. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 671–680. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Reinitz, J., Sharp, D.H.: Mechanism of eve stripe formation. Mech. Dev. 49(1-2), 133–158 (1995)CrossRefGoogle Scholar
  12. 12.
    Jaeger, J., Surkova, S., Blagov, M., Janssens, H., Kosman, D., Kozlov, K.N., Myasnikova, M.E., Vanario-Alonso, C.E., Samsonova, M., Sharp, D.H., Reinitz, J.: Dynamic control of positional information in the early drosophila embryo. Nature 430(6997), 368–371 (2004)CrossRefGoogle Scholar
  13. 13.
    Wahde, M., Hertz, J., Andersson, M.: Reverse engineering of sparsely connected genetic regulatory networks. In: Proceedings of the 2nd Workshop of Biochemical Pathways and Genetic Networks (2001)Google Scholar
  14. 14.
    Noman, N., Iba, H.: Inference of gene regulatory networks using S-system and differential evolution. In: GECCO’05 (2005)Google Scholar
  15. 15.
    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), 1154–1163 (2005)CrossRefGoogle Scholar
  16. 16.
    Kholodenko, B.N., Kiyatkin, A., Bruggeman, F.J., Sontag, E., Westerhoff, H.V., Hoek, J.B.: Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. PNAS 99(20), 12841–12846 (2002)CrossRefGoogle Scholar
  17. 17.
    de la Fuente, A., Brazhnik, P., Mendes, P.: Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet. 18(8), 395–398 (2002)CrossRefGoogle Scholar
  18. 18.
    Weaver, D.: Modeling regulatory networks with weight matrices. In: Pacific Symposium on Biocomputing (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel Marbach
    • 1
  • Claudio Mattiussi
    • 1
  • Dario Floreano
    • 1
  1. 1.Ecole Polytechnique Fédérale de Lausanne (EPFL), Laboratory of Intelligent Systems, CH-1015 LausanneSwitzerland

Personalised recommendations