Abstract
Inferring regulations among the genes is a well-known and significantly important problem in systems biology for revealing the fundamental cellular processes. Although computational models can be used as tools to extract the probable structure and dynamics of such networks from gene expression data, capturing the complex nonlinear system dynamics is a challenging task. In this paper, we have proposed a method to reverse engineering Gene Regulatory Network (GRN) from microarray data. Inspired from the biologically relevant optimization algorithm ‘Particle Swarm Optimization’ (PSO), we have enhanced the PSO incorporating two genetic algorithm operators, namely crossover and mutation. Furthermore, Linear Time Variant (LTV) Model is employed to modeling the GRN appropriately. In the evaluation, the proposed method shows superiority over the state-of-the-art methods when tested with synthetic network, both for the noise free and noise in data. The strength of the proposed method has also been verified by analyzing the real expression data set of SOS DNA repair system in Escherichia coli.
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.
References
Kabir, M., Noman, N., Iba, H.: Reverse engineering gene regulatory network from microarray data using linear time-variant model. BMC Bioinformatics 11(S-1), 56 (2010)
Kennedy, J., Eberhart, R., et al.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Wang, R.-S., Saadatpour, A., Albert, R.: Boolean modeling in systems biology: an overview of methodology and applications. Physical Biology 9(5), 055001 (2012)
B.E. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, F. d’Alche-Buc: Gene networks inference using dynamic Bayesian networks. Bioinformatics 19(suppl. 2), ii138–ii148 (2003)
Chowdhury, A.R., Chetty, M.: An improved method to infer gene regulatory network using S-system. In: IEEE Congress on Evolutionary Computation, pp. 1012–1019 (2011)
Chowdhury, A.R., Chetty, M., Vinh, N.X.: Adaptive regulatory genes cardinality for reconstructing genetic networks. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)
Chowdhury, A.R., Chetty, M., Vinh, N.X.: Incorporating time-delays in S-system model for reverse engineering genetic networks. BMC Bioinformatics 14, 196 (2013)
Fogel, D.B.: The advantages of evolutionary computation. In: Biocomputing and Emergent Computation, pp. 1–11 (1997)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE (1998)
Agrawal, R.B., Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space (1994)
Raghuwanshi, M., Kakde, O.: Survey on multiobjective evolutionary and real coded genetic algorithms. In: Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 150–161 (2004)
Showkat, D., Kabir, M.: Inference of genetic networks using multi-objective hybrid spea2+ from microarray data. In: ICCI*CC, pp. 195–202 (2013)
Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K., Tomita, M.: Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 19(5), 643–650 (2003)
Ronen, M., Rosenberg, R., Shraiman, B.I., Alon, U.: Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics. National Academy of Sciences 99(16), 10555–10560 (2002)
Kimura, S., Shiraishi, Y., Hatakeyama, M.: Inference of genetic networks using linear programming machines: application of a priori knowledge. In: International Joint Conference on Neural Networks, pp. 694–701 (2009)
Cho, D.Y., Cho, K.H., Zhang, B.T.: Identification of biochemical networks by s-tree based genetic programming. Bioinformatics 22, 1631–1640 (2006)
Kimura, S., Sonoda, K., Yamane, S., Maeda, H., Matsumura, K., Hatakeyama, M.: Function approximation approach to the inference of reduced ngnet models of genetic networks. BMC Bioinformatics 9(1), 23 (2008)
Bansal, M., Gatta, G.D., di Bernardo, D.: Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22(7), 815–822 (2006)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sultana, R., Showkat, D., Samiullah, M., Chowdhury, A.R. (2014). Reconstructing Gene Regulatory Network with Enhanced Particle Swarm Optimization. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-12640-1_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12639-5
Online ISBN: 978-3-319-12640-1
eBook Packages: Computer ScienceComputer Science (R0)