{"references": ["Helen C. Causton, John Quackenbush and Alvis Brazma, \"A Beginner's\nguide Microarray Gene Expression data Analysis,\" Blackwell\nPublishing.", "K.H. Cho, S.M. Choo, S.H. Jung, J.R. Kim, H.S. Choi and J. Kim,\n\"Reverse engineering of gene regulatory networks,\" IET systems\nBiology, 2007, pp.149-163.", "Xiujun Zhang, Xing-Ming Zhao, Kun He, Le Lu, Yongwei Cao,\nJingdong Liu, Jin-Kao Hao, Zhi-Ping Liu and Luonan Chen, \"inferring\ngene regulatory networks from gene expression data by PC-algorithm\nbased on conditional mutual information,\" Oxford university press.", "Shoudan Liang, Stefanie Fuhrman and Roland Somogyi, \"REVEAL, A\ngeneral reverse engineering algorithm for inferemce of genetic network\narchitectures,\" Pacific symposium on Biocomputing 3,1998, pp. 18-29.", "Khalid Raza and Rafat Parveen, \"Evolutionary Algorithms in Genetic\nRegulatory Networks Model\", Journal of Advanced Bioinformatics\nApplications and Research, 3(1):271-280, 2012.", "Xiaosheng Wang and Osamu Gotoh, \"Inference of Cancer-speific gene\nRegulatory Networks Using Soft Computing Rules,\" Gene Regulation\nand Systems Biology, 2010,19-34.", "Yuji Zhang, Jianhua Xuan, Benildo G de los Reyes, Robert Clarke and\nHabtom w Ressom, \"Reverse engineering module networks by PSORNN\nhybrid modeling,\", BMC Genomics, 2009.", "Brueckner F, Armache KJ, Cheung A, et al., \"Structure\u2013function studies\nof the RNA polymerase II elongation complex\". Acta Crystallogr. D\nBiol. Crystallogr. 65 (Pt 2): 112\u201320, 2009", "PP Amaral, ME Dinger, TR Mercer and JS Mattick, \"The eukaryotic\ngenome as an RNA machine\". Science 319 (5871): 1787\u20139, 2008.\n[10] B Schwanh\u00e4usser,D Busse, G Dittmar, J Schuchhardt, J Wolf, W Chen\nand M Selbach, \"Global quantification of mammalian gene expression\ncontrol\". Nature 473 (7347): 337\u201342, 2011.\n[11] Khalid Raza and Rafat Parveen, \"Soft Computing Approach for\nModeling Genetic Regulatory Networks\", 2nd International Conference\non Artificial Intelligence, Soft Computing and Applications (AIAA-\n2012), Proc. published in Advances in Intelligent Systems and\nComputing, AISC 178, pp 1-11, Springer-Verlag Berlin Heidelberg,\n2012.\n[12] Chesler EJ, Lu L, Wang J, Williams RW, Manly KF, \"WebQTL: rapid\nexploratory analysis of gene expression and genetic networks for brain\nand behavior\". Nat Neurosci 7 (5): 485\u201386, 2004."]}

Reverse engineering of genetic regulatory network involves the modeling of the given gene expression data into a form of the network. Computationally it is possible to have the relationships between genes, so called gene regulatory networks (GRNs), that can help to find the genomics and proteomics based diagnostic approach for any disease. In this paper, clustering based method has been used to reconstruct genetic regulatory network from time series gene expression data. Supercoiled data set from Escherichia coli has been taken to demonstrate the proposed method.