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.
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References
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)
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)
Brazhnik, P.: Inferring gene networks from steady-state response to single-gene perturbations. J. Theor. Biol. 237(4), 427–440 (2005)
Corne, D., Pridgeon, C.: Investigating issues in the reconstructability of genetic regulatory networks. Congress on Evolutionary Computation (2004)
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)
Ljung, L.: System identification: Theory for the user. Prentice Hall, Upper Saddle River (1999)
Bäck, T., Fogel, D., Michalewicz, Z.: Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics, Bristol (2000)
Mattiussi, C.: Evolutionary synthesis of analog networks. PhD thesis, Ecole Polytechnique Fédérale de Lausanne, Lausanne (2005)
Mattiussi, C., Floreano, D.: Analog Genetic Encoding for the Evolution of Circuits and Networks. IEEE Transactions on Evolutionary Computation (To appear, 2006)
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)
Reinitz, J., Sharp, D.H.: Mechanism of eve stripe formation. Mech. Dev. 49(1-2), 133–158 (1995)
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)
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)
Noman, N., Iba, H.: Inference of gene regulatory networks using S-system and differential evolution. In: GECCO’05 (2005)
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)
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)
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)
Weaver, D.: Modeling regulatory networks with weight matrices. In: Pacific Symposium on Biocomputing (1999)
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Marbach, D., Mattiussi, C., Floreano, D. (2007). Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_15
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DOI: https://doi.org/10.1007/978-3-540-71783-6_15
Publisher Name: Springer, Berlin, Heidelberg
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