Abstract
Constructing genetic regulatory networks (GRNs) from expression data is one of the most important issues in systems biology research. To automate the procedure of network construction, we develop an evolution framework to infer the S-system network models. Our framework mainly includes a sensitivity analysis method and a hybrid GA-PSO method to infer appropriate network parameters. To validate the proposed methods, experiments have been conducted and the results show the promise of our approach.
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References
Ingolia, N.T., Weissman, J.S.: Systems Biology: Reverse Engineering the Cell. Nature 454, 1059–1062 (2008)
Lee, W.-P., Tzou, W.-S.: Computational Methods for Discovering Gene Networks from Expression Data. Briefings in Bioinformatics 10(4), 408–423 (2009)
Kikuchi, S., Tominaga, D., Arita, M., et al.: Dynamic Modeling of Genetic Networks Using Genetic Algorithm and S-system. Bioinformatics 19(5), 643–650 (2003)
Ho, S.-Y., Hsieh, C.-H., Yu, F.-C., et al.: An Intelligent Two-Stage Evolutionary Algorithm for Dynamic Pathway Identification From Gene Expression Profiles. IEEE/ACM Trans. on Computational. Biology and Bioinformatics 4(4), 648–704 (2007)
Cho, K., Shin, S., Kolch, W., Wolkenhauer, O.: Experimental Design in Systems Biology, Based on Parameter Sensitivity Analysis Using a Monte Carlo Method: A Case Study for the TNFα-Mediated NF-kB Signal Transduction Pathway. Simulation 79(12), 726–729 (2003)
van Riel, N.A.W.: Dynamic Modeling and Analysis of Biochemical Networks: Mechanism-based Models And Model-based Experiments. Briefings in Bioinformatics 7(4), 364–374 (2006)
Fomekong-Nanfack, Y., Postma, M., Kaandorp, J.: Inferring Drosophila Gap Gene Regulatory Network: A Parameter Sensitivity and Perturbation Analysis. BMC Systems Biology 3(1), 94 (2009)
Settles, M., Soule, T.: Breeding Swarms: a GA/PSO Hybrid. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 161–168 (2005)
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Lee, W.-P., Yang, K.-C.: A Clustering-Based Approach for Inferring Recurrent Neural Networks as Gene Regulatory Networks. Neurocomputing 71(4-6), 600–610 (2008)
Ho, S.-Y., Hsieh, C.-H., Yu, F.-C., et al.: An Intelligent Two-Stage Evolutionary Algorithm for Dynamic Pathway Identification From Gene Expression Profiles. Biology and Bioinformatics 4(4), 648–704 (2007)
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Hsiao, YT., Lee, WP. (2012). Evolving Gene Regulatory Networks: A Sensitivity-Based Approach. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_67
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DOI: https://doi.org/10.1007/978-3-642-24553-4_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24552-7
Online ISBN: 978-3-642-24553-4
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