Abstract
In this paper, we address the problem of reverse-engineering a gene regulatory network from gene expression time series. We approach the problem by implementing an ant system to generate candidate network structures. The quality of a candidate structure is evaluated using a particle swarm optimization algorithm that tunes the parameters of the corresponding model, by minimizing the error between the actual time series and the trained model’s output. We extend this approach by incorporating domain-specific heuristics to the ant system, as a mechanism that has the potential to bias the pheromone amplification effect towards biologically plausible relationships. We apply the method to a subset of genes from a real world data set and report on the results.
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References
Alon, U.: An introduction to systems biology: design principles of biological circuits. Chapman & Hall/CRC, Boca Raton (2007)
Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002)
Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19(17), 2271–2282 (2003)
Ressom, H., Zhang, Y., Xuan, J., Wang, Y., Clarke, R.: Inference of gene regulatory networks from time course gene expression data using neural networks and swarm intelligence. In: IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, pp. 1–8 (2006)
Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. PNAS 95(25), 14863–14868 (1998)
D’Haeseleer, P., Wen, X., Fuhrman, S.: Mining the gene expression matrix: inferring gene relationships from large scale gene expression data. In: Second International Workshop on Information Processing in Cell and Tissues, pp. 203–212 (1998)
de Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 9(1), 69–105 (2002)
Somogyi, R., Fuhrman, S., Askenazi, M.: The gene expression matrix: towards the extraction of genetic network architectures. Nonlinear Analysis, Theory, Methods & Applications 30(3), 1815–1824 (1997)
Perrin, B., Ralaivola, L., Mazurie, A., Bottani, S., Mallet, J., d’Alche Buc, F.: Gene networks inference using dynamic Bayesian networks. Bioinformatics 19(suppl. 2), ii 138–ii 148 (2003)
Vohradsky, J.: Neural model of the genetic network. Journal of Biological Chemistry 276(39), 36168–36173 (2001)
Wahde, M., Hertz, J.: Modeling Genetic Regulatory Dynamics in Neural Development. Journal of Computational Biology 8(4), 429–442 (2001)
Pournara, I., Wernisch, L.: Factor analysis for gene regulatory networks and transcription factor activity profiles. BMC Bioinformatics 8(61) (2007)
Xu, R., Wunsch, D.C.I., Frank, R.: Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(4), 681–692 (2007)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: from natural to artificial systems. Oxford University Press, Oxford (1999)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kwon, A., Hoos, H., Ng, R.: Inference of transcriptional regulation relationships from gene expression data. Bioinformatics 19(8), 905–912 (2003)
Needleman, S., Wunsch, C.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48, 443–453 (1970)
Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9, 3273–3297 (1998)
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Kentzoglanakis, K., Poole, M., Adams, C. (2008). Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_33
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DOI: https://doi.org/10.1007/978-3-540-87527-7_33
Publisher Name: Springer, Berlin, Heidelberg
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