The Community for Technology Leaders
RSS Icon
Issue No.02 - March/April (2011 vol.8)
pp: 570-576
André Fujita , RIKEN, Japan
João Ricardo Sato , Universidade Federal do ABC, São Paulo, Brazil
Marcos Angelo Almeida Demasi , University of São Paulo, São Paulo, Brazil
Rui Yamaguchi , University of Tokyo, Japan
Teppei Shimamura , University of Tokyo, Japan
Carlos Eduardo Ferreira , University of São Paulo, São Paulo, Brazil
Mari Cleide Sogayar , University of São Paulo, São Paulo, Brazil
Satoru Miyano , University of Tokyo, Japan
Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However, only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.e., asymmetries in gene expression dependences of regulatory networks. Moreover, we present a bootstrap algorithm in order to test the contagion concept. This technique was applied in simulated data and, also, in an actual large sample of biological data. Literature review has confirmed some genes identified by contagion as actually belonging to the TP53 pathway.
Contagion, local correlation, regulatory network.
André Fujita, João Ricardo Sato, Marcos Angelo Almeida Demasi, Rui Yamaguchi, Teppei Shimamura, Carlos Eduardo Ferreira, Mari Cleide Sogayar, Satoru Miyano, "Inferring Contagion in Regulatory Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 2, pp. 570-576, March/April 2011, doi:10.1109/TCBB.2010.40
[1] S. Bjerve and K. Doksum, "Correlation Curves: Measures of Association as Functions of Covariate Values," Annals of Statistics, vol. 21, pp. 890-902, 1993.
[2] B.O. Bradley and M.S. Taqqu, "Framework for Analyzing Spatial Contagion between Financial Markets," Finance Letters, vol. 2, pp. 8-15, 2004.
[3] B.O. Bradley and M.S. Taqqu, "How to Estimate Spatial Contagion between Financial Markets," Finance Letters, vol. 3, pp. 64-76, 2005.
[4] B.O. Bradley and M.S. Taqqu, "Empirical Evidence on Spatial Contagion between Financial Markets," Finance Letters, vol. 3, pp. 77-86, 2005.
[5] A.J. Butte and I.S. Kohane, "Mutual Information Relevance Networks: Functional Genomic Clustering Using Pairwise Entropy Measurements," Proc. Pacific Symp. Biocomputing, vol. 5, pp. 415-426, 2000.
[6] S. Calvo and C.M. Reinhart, "Capital Flows to Latin America: Is There Evidence of Contagion Effects in Private Capital Flows to Emerging Markets," Private Capital Flows to Emerging Markets After the Mexican Crisis, pp. 151-171, Inst. for Int'l Eco nomics, 1996.
[7] K. Doksum, S. Blyth, E. Bradlow, X. Meng, and H. Zhao, "Correlation Curves as Local Measures of Variance Explained by Regression," J. Am. Statistical Assoc., vol. 89, pp. 571-582, 1994.
[8] J.J. Faith, B. Hayete, J.T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J.J. Collins, and T.S. Gardner, "Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles," vol. 5, no. e8, 2007.
[9] A. Fujita, J.R. Sato, H.M. Garay-Malpartida, P.A. Morettin, M.C. Sogayar, and C.E. Ferreira, "Time-Varying Modeling of Gene Expression Regulatory Networks Using the Wavelet Dynamic Vector Autoregressive Method," Bioinformatics, vol. 23, pp. 1623-1630, 2007.
[10] A. Fujita, J.R. Sato, H.M. Garay-Malpartida, R. Yamaguchi, S. Miyano, M.C. Sogayar, and C.E. Ferreira, "Modeling Gene Expression Regulatory Networks with the Sparse Vector Autoregressive Model," BMC Systems Biology, vol. 1, no. 39, 2007.
[11] A. Fujita, J.R. Sato, H.M. Garay-Malpartida, M.C. Sogayar, C.E. Ferreira, and S. Miyano, "Modeling Nonlinear Gene Regulatory Networks from Time Series Gene Expression Data," J. Bioinformatics and Computational Biology, vol. 6, pp. 961-979, 2008.
[12] N. Friedman, M. Linial, I. Nachman, and D. Pe'er, "Using Bayesian Networks to Analyze Expression Data," J. Computational Biology, vol. 7, pp. 601-620, 2000.
[13] R. Jansen, H. Yu, D. Greenbaum, Y. Kluger, N.J. Krogan, S. Chung, A. Emili, M. Snyder, J.F. Greenblatt, and M. Gerstein, "A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data," Science, vol. 302, pp. 449-453, 2003.
[14] S. Jin and A.J. Levine, "The p53 Functional Circuit," J. Cell Science, vol. 114, pp. 4139-4140, 2001.
[15] Y. Lin, W. Ma, and S. Benchimol, "Pidd, a New Death-Domain-Containing Protein, Is Induced by p53 and Promotes Apoptosis," Nature Genetics, vol. 26, pp. 122-127, 2000.
[16] K. Shedden, J.M.G. Taylor, S.A. Enkemann, M.S. Tsao, T.J. Yeatman, W.L. Gerald, S. Eschrich, I. Jurisica, T.J. Giordano, D.E. Misek, A.C. Chang, C.Q. Zhu, D. Strumpf, S. Hanash, F.A. Shepherd, K. Ding, L. Seymour, K. Naoki, N. Pennell, B. Weir, R. Verhaak, C. Ladd-Acosta, T. Golub, M. Gruid, A. Sharma, J. Szoke, M. Zakowski, V. Rusch, M. Kris, A. Viale, N. Motoi, W. Travis, B. Conley, V.E. Seshan, M. Meyerson, R. Kuick, K.K. Dobbin, T. Lively, J.W. Jacobson, and D.G. Beer, "Gene Expression Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study," Nature Medicine, Directorś Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma vol. 14, pp. 822-827, 2008.
[17] A.A. Margolin, K. Wang, W.K. Lim, M. Kustagi, I. Nemenman, and A. Califano, "Reverse Engineering Cellular Networks," Nature Protocols, vol. 1, pp. 662-671, 2006.
[18] J.C. Marine, S. Francoz, M. Maetens, G. Wahl, F. Toledo, and G. Lozano, "Keeping p53 in Check: Essential and Synergistic Functions of Mdm2 and Mdm4," Cell Death and Differentiation, vol. 13, pp. 927-934, 2006.
[19] M. Moriyama, Y. Hoshida, S. Nishimura, N. Kato, T. Goto, H. Taniguchi, Y. Shiratori, N. Seki, and M. Omata, "Relevance Network between Chemosensitivity and Transcriptome in Human Hepatoma Cells," Molecular Cancer Therapeutics, vol. 2, pp. 199-205, 2003.
[20] N.D. Mukhopadhyay and S. Chatterjee, "Causality and Pathway Search in Microarray Time Series Experiment," Bioinformatics, vol. 23, pp. 442-449, 2007.
[21] Y.C. Park and C.-Y. Song, "Financial Contagion in East Asian Crisis—with Special Reference to the Republic of Korea, NBER Project on Exchange Rate Crises in Emerging Market Economies: The Korean Currency Crisis," Feb. 2000.
[22] J. Pearl, Causality: Models, Reasoning, and Inference. Cambridge Univ. Press, 2000.
[23] A. Pretorius and J. de Beer, "Contagion in Africa: South Africa and a Troubled Neighbour, Zimbabwe," Economic Modelling, vol. 21, pp. 703-717, 2003.
[24] J. Schäfer and K. Strimmer, "An Empirical Bayes Approach to Inferring Large-Scale Gene Association Networks," Bioinformatics, vol. 21, pp. 754-764, 2005.
[25] M.S. Sheikh, T.F. Burns, Y. Huang, G.S. Wu, S. Amundson, K.S. Brooks, and A.J. Fornace,Jr, and W.S. el-Deiry, "p53-Dependent and -Independent Regulation of the Death Receptor KILLER/DR5 Gene Expression in Response to Genotoxic Stress and Tumor Necrosis Factor Alpha," Cancer Research, vol. 58, pp. 1593-1598, 1998.
[26] J.H. Song, K. Kandasamy, and A.S. Kraft, "ABT-737 Induces Expression of the Death Receptor 5 and Sensitizes Human Cancer Cells to TRAIL-Induced Apoptosis," J. Biological Chemistry, vol. 283, pp. 25003-25013, 2008.
[27] J.B. Telliez, K.M. Bean, and L.L. Lin, "LRDD, a Novel Leucine Rich Repeat and Death Domain Containing Protein," Biochimica et Biophysics Acta, vol. 1478, pp. 280-288, 2000.
[28] H. Toh and K. Horimoto, "Inference of a Genetic Network by a Combined Approach of Cluster Analysis and Graphical Gaussian Modeling," Bioinformatics, vol. 18, pp. 287-297, 2002.
[29] B. Vogelstein, D. Lane, and A.J. Levine, "Surfing the p53 Network," Nature, vol. 408, pp. 307-310, 2000.
[30] K.H. Vousden and D.P. Lane, "p53 in Health and Disease," Nature Rev. Molecular Cell Biology, vol. 8, pp. 275-283, 2007.
[31] B. Wilczyński, T.R. Hvidsten, A. Kryshtafovych, J. Tiuryn, J. Komorowski, and K. Fidelis, "Using Local Gene Expression Similarities to Discover Regulatory Binding Site Modules," BMC Bioinformatics, vol. 7, article no. 505, 2006.
[32] M. Xiong, J. Li, and X. Fang, "Identification of Genetic Networks," Genetics, vol. 166, pp. 1037-1052, 2004.
15 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool