Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks
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.
KeywordsSystems Biology Gene Networks Reverse Engineering Steady-State Data Genetic Algorithm Analog Genetic Encoding (AGE).
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- 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
- 4.Corne, D., Pridgeon, C.: Investigating issues in the reconstructability of genetic regulatory networks. Congress on Evolutionary Computation (2004)Google Scholar
- 6.Ljung, L.: System identification: Theory for the user. Prentice Hall, Upper Saddle River (1999)Google Scholar
- 7.Bäck, T., Fogel, D., Michalewicz, Z.: Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics, Bristol (2000)Google Scholar
- 8.Mattiussi, C.: Evolutionary synthesis of analog networks. PhD thesis, Ecole Polytechnique Fédérale de Lausanne, Lausanne (2005)Google Scholar
- 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
- 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.Noman, N., Iba, H.: Inference of gene regulatory networks using S-system and differential evolution. In: GECCO’05 (2005)Google Scholar
- 18.Weaver, D.: Modeling regulatory networks with weight matrices. In: Pacific Symposium on Biocomputing (1999)Google Scholar