Abstract
With the invention of microarray technology, researchers are able to measure the expression levels of ten thousands of genes in parallel at various time points of a biological process. The investigation of gene regulatory networks has become one of the major topics in Systems Biology. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We suggest to use a multi-objective evolutionary algorithm to identify the parameters of a non-linear system given by the observed data. Currently, only limited information on gene regulatory pathways is available in Systems Biology. Not only the actual parameters of the examined system are unknown, also the connectivity of the components is a priori not known. However, this number is crucial for the inference process. Therefore, we propose a method, which uses the connectivity as an optimization objective in addition to the data dissimilarity (relative standard error - RSE) between experimental and simulated data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Akutsu, T., Miyano, S., Kuhura, S.: Identification of genetic networks from a small number of gene expression patterns under the boolean network model. In: Proceedings of the Pacific Symposium on Biocomputing, pp. 17–28 (1999)
Akutsu, T., Miyano, S., Kuhura, S.: Algorithms for identifying boolean networks and related biological networks based on matrix multiplication and fingerprint function. In: Proceedings of the fourth annual international conference on Computational molecular biology, Tokyo, Japan, pp. 8–14. ACM Press, New York (2000)
Ando, S., Sakamoto, E., Iba, H.: Evolutionary modeling and inference of gene network. Information Sciences 145(3-4), 237–259 (2002)
Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings of the, IEEE Int. Conf. on Evolutionary Computation, pp. 312–317. IEEE Service Center, Piscataway (1996)
Herz, J.: Statistical issues in reverse engineering of genetic networks. In: Proceedings of the Pacific Symposium on Biocomputing (1998)
Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., Miyano, S.: Combining microarrays and biological knowledge for estimating gene networks via bayesian networks. In: Proceedings of the IEEE Computer Society Bioinformatics Conference (CSB 2003), pp. 104–113. IEEE, Los Alamitos (2003)
Kauffman, S.A.: The Origins of Order. Oxford University Press, New York (1993)
Keedwell, E., Narayanan, A., Savic, D.: Modelling gene regulatory data using artificial neural networks. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2002), vol. 1, pp. 183–188 (2002)
Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K., Tomita, M.: Dynamic modeling of genetic netowrks using genetic algorithm and s-sytem. Bioinformatics 19(5), 643–650 (2003)
Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In: Proceedings of the Pacific Symposium on Biocomputing, vol. 3, pp. 18–29 (1998)
Maki, Y., Tominaga, D., Okamoto, M., Watanabe, S., Eguchi, Y.: Development of a system for the inference of large scale genetic networks. In: Proceedings of the Pacific Symposium on Biocomputing, vol. 6, pp. 446–458 (2001)
Ono, I., Yoshiaki Seike, R., Ono, N., Matsui, M.: An evolutionary algorithm taking account of mutual interactions among substances for inference of genetic networks. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2004), pp. 2060–2067 (2004)
Pridgeon, C., Corne, D.: Genetic network reverse-engineering and network size; can we identify large grns? In: Proceedings of the Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2004), pp. 32–36 (2004)
Savageau, M.A.: 20 years of S-systems. In: Voit, E. (ed.) Canonical Nonlinear Modeling. S-systems Approach to Understand Complexity, New York, pp. 1–44. Van Nostrand Reinhold (1991)
Spieth, C., Streichert, F., Speer, N., Zell, A.: Iteratively inferring gene regulatory networks with virtual knockout experiments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 102–111. Springer, Heidelberg (2004)
Spieth, C., Streichert, F., Speer, N., Zell, A.: Optimizing topology and parameters of gene regulatory network models from time-series experiments. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 461–470. Springer, Heidelberg (2004)
Spieth, C., Streichert, F., Speer, N., Zell, A.: Utilizing an island model for ea to preserve solution diversity for inferring gene regulatory networks. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2004), pp. 146–151 (2004)
Streichert, F., Ulmer, H., Zell, A.: Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem. In: Proceedings of the 2004 Congress on Evolutionary Computation, pp. 932–939 (2004)
Thieffry, D., Thomas, R.: Qualitative analysis of gene networks. In: Proceedings of the Pacific Symposium on Biocomputing, pp. 77–87 (1998)
Tominaga, D., Kog, N., Okamoto, M.: Efficient numeral optimization technique based on genetic algorithm for inverse problem. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 251–258 (2000)
Weaver, D., Workman, C., Stormo, G.: Modeling regulatory networks with weight matrices. In: Proceedings of the Pacific Symposium on Biocomputing, vol. 4, pp. 112–123 (1999)
Yeung, M.K.S., Tegner, J., Collins, J.J.: Reverse engineering gene networks using singular value decomposition and robust regression. Proceedings of the National Academy of Science USA 99, 6163–6168 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Spieth, C., Streichert, F., Speer, N., Zell, A. (2005). Multi-objective Model Optimization for Inferring Gene Regulatory Networks. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-31880-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24983-2
Online ISBN: 978-3-540-31880-4
eBook Packages: Computer ScienceComputer Science (R0)