Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31515
Clustering Approach to Unveiling Relationships between Gene Regulatory Networks

Authors: Hiba Hasan, Khalid Raza


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.

Keywords: Gene expression, gene regulatory networks (GRNs), clustering, data preprocessing, network visualization.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1852


[1] Helen C. Causton, John Quackenbush and Alvis Brazma, “A Beginner’s guide Microarray Gene Expression data Analysis,” Blackwell Publishing.
[2] K.H. Cho, S.M. Choo, S.H. Jung, J.R. Kim, H.S. Choi and J. Kim, “Reverse engineering of gene regulatory networks,” IET systems Biology, 2007, pp.149-163.
[3] Xiujun Zhang, Xing-Ming Zhao, Kun He, Le Lu, Yongwei Cao, Jingdong Liu, Jin-Kao Hao, Zhi-Ping Liu and Luonan Chen, “inferring gene regulatory networks from gene expression data by PC-algorithm based on conditional mutual information,” Oxford university press.
[4] Shoudan Liang, Stefanie Fuhrman and Roland Somogyi, “REVEAL, A general reverse engineering algorithm for inferemce of genetic network architectures,” Pacific symposium on Biocomputing 3,1998, pp. 18-29.
[5] Khalid Raza and Rafat Parveen, "Evolutionary Algorithms in Genetic Regulatory Networks Model", Journal of Advanced Bioinformatics Applications and Research, 3(1):271-280, 2012.
[6] Xiaosheng Wang and Osamu Gotoh, “Inference of Cancer-speific gene Regulatory Networks Using Soft Computing Rules,” Gene Regulation and Systems Biology, 2010,19-34.
[7] Yuji Zhang, Jianhua Xuan, Benildo G de los Reyes, Robert Clarke and Habtom w Ressom, “Reverse engineering module networks by PSORNN hybrid modeling,”, BMC Genomics, 2009.
[8] Brueckner F, Armache KJ, Cheung A, et al., "Structure–function studies of the RNA polymerase II elongation complex". Acta Crystallogr. D Biol. Crystallogr. 65 (Pt 2): 112–20, 2009
[9] PP Amaral, ME Dinger, TR Mercer and JS Mattick, "The eukaryotic genome as an RNA machine". Science 319 (5871): 1787–9, 2008.
[10] B Schwanhäusser,D Busse, G Dittmar, J Schuchhardt, J Wolf, W Chen and M Selbach, "Global quantification of mammalian gene expression control". Nature 473 (7347): 337–42, 2011.
[11] Khalid Raza and Rafat Parveen, “Soft Computing Approach for Modeling Genetic Regulatory Networks”, 2nd International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA- 2012), Proc. published in Advances in Intelligent Systems and Computing, AISC 178, pp 1-11, Springer-Verlag Berlin Heidelberg, 2012.
[12] Chesler EJ, Lu L, Wang J, Williams RW, Manly KF, "WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior". Nat Neurosci 7 (5): 485–86, 2004.