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Issue No.03 - May/June (2011 vol.8)
pp: 592-606
Michael F. Ochs , The Johns Hopkins University, Baltimore
Erdem Yörük , The Johns Hopkins University, Baltimore
Laurent Younes , The Johns Hopkins University, Baltimore
Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited complexity due to parameter sharing and uses micorarray data of mRNA transcripts as the only observable components of signaling. Specifically, we account for cell heterogeneity and a multilevel process, representing signaling as a Bayesian network at the cell level, modeling measurements as ensemble averages at the tissue level, and incorporating patient-to-patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover receptor status from available microarray data.
Cell signaling networks, signaling protein, microarray, statistical learning, Bayesian networks, stochastic approximation expectation maximization, Gibbs sampling, Mann-Whitney-Wilcoxon test.
Michael F. Ochs, Erdem Yörük, Laurent Younes, "A Comprehensive Statistical Model for Cell Signaling", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 3, pp. 592-606, May/June 2011, doi:10.1109/TCBB.2010.87
[1] M.R. Birtwistle, M. Hatakeyama, N. Yumoto, B.A. Ogunnaike, J.B. Hoek, and B.N. Kholodenko, "Ligand-Dependent Responses of the ErbB Signaling Network: Experimental and Modeling Analyses," Molecular Systems Biology, vol. 3, article no. 144, 2007.
[2] Y. Chen and E. Dougherty, "Ratio-Based Decisions and the Quantitative Analysis of cDNA Microarray Images," J. Biomedical Optics, vol. 2, pp. 364-374, 1997.
[3] K. Chin, S. DeVries, J. Fridlyand, P.T. Spellman, R. Roydasgupta, W.L. Kuo, A. Lapuk, R.M. Neve, Z. Qian, T. Ryder, F. Chen, H. Feiler, T. Tokuyasu, C. Kingsley, S. Dairkee, Z. Meng, K. Chew, D. Pinkel, A. Jain, B.M. Ljung, L. Esserman, D.G. Albertson, F.M. Waldman, and J.W. Gray, "Genomic and Transcriptional Aberrations Linked to Breast Cancer Pathophysiologies," Cancer Cell, vol. 10, no. 6, pp. 529-541, 2006.
[4] B. Delyon, M. Lavielle, and E. Moulines, "Convergence of a Stochastic Approximation Version of the EM Algorithm," Annals of Statistics, vol. 27, no. 1, pp. 94-128, 1999.
[5] A. Djebbari and J. Quackenbush, "Seeded Bayesian Networks: Constructing Genetic Networks from Microarray Data," BMC Systems Biology, vol. 2, article no. 57, 2008.
[6] N. Friedman, M. Linial, I. Nachman, and D. Pe'er, "Using Bayesian Networks to Analyze Expression Data," J. Computational Biology, vol. 7, nos. 3-4, pp. 601-620, 2000.
[7] W. Huber, A. von Heydebreck, and M. Vingron, "Analysis of Microarray Gene Expression Data," Handbook of Statistical Genetics, second ed., Wiley, 2003.
[8] T. Ideker, V. Thorsson, A.F. Siegel, and L.E. Hood, "Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data," J. Computational Biology, vol. 7, no. 6, pp. 805-817, Dec. 2000.
[9] S. Imoto, T. Higuchi, T. Goto, K. Tashiro, S. Kuhara, and S. Miyano, "Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks," J. Bioinformatics and Computational Biology, vol. 2, no. 1, pp. 77-98, 2004.
[10] R.A. Irizarry, B. Hobbs, F. Collin, Y.D. Beazer-Barclay, K.J. Antonellis, U. Scherf, and T.P. Speed, "Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data," Biostatistics, vol. 4, no. 2, pp. 249-264, Apr. 2003.
[11] M. Kanehisa, S. Goto, S. Kawashima, and A. Nakaya, "The Kegg Databases at Genomenet," Nucleic Acids Research, vol. 30, no. 1, pp. 42-46, 2002.
[12] A. Kossenkov, F.J. Manion, E. Korotkov, T.D. Moloshok, and M.F. Ochs, "ASAP: Automated Sequence Annotation Pipeline for Web-Based Updating of Sequence Information with a Local Dynamic Database," Bioinformatics, vol. 19, no. 5, pp. 675-676, 2003.
[13] A.V. Kossenkov and M.F. Ochs, "Matrix Factorization for Recovery of Biological Processes from Microarray Data," Methods in Enzymology, vol. 467, pp. 59-77, 2009.
[14] M. Niepel, S.L. Spencer, and P.K. Sorger, "Non-Genetic Cell-to-Cell Variability and the Consequences for Pharmacology," Current Opinion in Chemical Biology, vol. 13, nos. 5-6, pp. 556-561, 200.
[15] E. Kuhn and M. Lavielle, "Coupling a Stochastic Approximation Version of EM with an MCMC Procedure," ESAIM: Probability and Statistics, vol. 8, pp. 115-131, 2004.
[16] J.C. Liao, R. Boscolo, Y.L. Yang, L.M. Tran, C. Sabatti, and V.P. Roychowdhury, "Network Component Analysis: Reconstruction of Regulatory Signals in Biological Systems," Proc. Nat'l Academy of Sciences USA, vol. 100, no. 26, pp. 15522-15527, 2003.
[17] J. Lin, C.M. Gan, X. Zhang, S. Jones, T. Sjoblom, L.D. Wood, D.W. Parsons, N. Papadopoulos, K.W. Kinzler, B. Vogelstein, G. Parmigiani, and V.E. Velculescu, "A Multidimensional Analysis of Genes Mutated in Breast and Colorectal Cancers," Genome Research, vol. 17, no. 9, pp. 1304-1318, 2007.
[18] M. Liu, A. Liberzon, S.W. Kong, W.R. Lai, P.J. Park, I.S. Kohane, and S. Kasif, "Network-Based Analysis of Affected Biological Processes in Type 2 Diabetes Models," PLoS Genetics, vol. 3, no. 6,e96, pp. 958-972, 2007.
[19] V. Matys, O.V. Kel-Margoulis, E. Fricke, I. Liebich, S. Land, A. Barre-Dirrie, I. Reuter, D. Chekmenev, M. Krull, K. Hornischer, N. Voss, P. Stegmaier, B. Lewicki-Potapov, H. Saxel, A.E. Kel, and E. Wingender, "TRANSFAC and Its Module TRANSCompel: Transcriptional Gene Regulation in Eukaryotes," Nucleic Acids Research, vol. 34, pp. D108-110, 2006.
[20] S. Mukherjee and T.P. Speed, "Network Inference Using Informative Priors," Proc. Nat'l Academy of Sciences USA, vol. 105, no. 38, pp. 14313-14318, 2008.
[21] I. Nachman, A. Regev, and N. Friedman, "Inferring Quantitative Models of Regulatory Networks from Expression Data," Bioinformatics, vol. 20, pp. i248-256, 2004.
[22] H. Parkinson, U. Sarkans, M. Shojatalab, N. Abeygunawardena, S. Contrino, R. Coulson, A. Farne, G.G. Lara, E. Holloway, M. Kapushesky, P. Lilja, G. Mukherjee, A. Oezcimen, T. Rayner, P. Rocca-Serra, A. Sharma, S. Sansone, and A. Brazma, "Arrayexpress—A Public Repository for Microarray Gene Expression Data at the EBI," Nucleic Acids Research, vol. 33, pp. D553-D555, 2005.
[23] D.W. Parsons, S. Jones, X. Zhang, J.C. Lin, R.J. Leary, P. Angenendt, P. Mankoo, H. Carter, I.M. Siu, G.L. Gallia, A. Olivi, R. McLendon, B.A. Rasheed, S. Keir, T. Nikolskaya, Y. Nikolsky, D.A. Busam, H. Tekleab, L.A. DiazJr., J. Hartigan, D.R. Smith, R.L. Strausberg, S.K. Marie, S.M. Shinjo, H. Yan, G.J. Riggins, D.D. Bigner, R. Karchin, N. Papadopoulos, G. Parmigiani, B. Vogelstein, V.E. Velculescu, and K.W. Kinzler, "An Integrated Genomic Analysis of Human Glioblastoma Multiforme," Science, vol. 321, no. 5897, pp. 1807-1812, 2008.
[24] P.J. Roberts and C.J. Der, "Targeting the Raf-MEK-ERK Mitogen-Activated Protein Kinase Cascade for the Treatment of Cancer," Oncogene, vol. 26, no. 22, pp. 3291-3310, 2007.
[25] K. Sachs, S. Itani, J. Carlisle, G.P. Nolan, D. Pe'er, and D.A. Lauffenburger, "Learning Signaling Network Structures with Sparsely Distributed Data," J. Computational Biology, vol. 16, no. 2, pp. 201-212, 2009.
[26] TGCA, "Comprehensive Genomic Characterization Defines Human Glioblastoma Genes and Core Pathways," Nature, vol. 455, no. 7216, pp. 1061-1068, 2008.
[27] I. Ulitsky and R. Shamir, "Identifying Functional Modules Using Expression Profiles and Confidence-Scored Protein Interactions," Bioinformatics, vol. 25, no. 9, pp. 1158-1164, 2009.
[28] J.J. Yeh and C.J. Der, "Targeting Signal Transduction in Pancreatic Cancer Treatment," Expert Opinion on Therapeutic Targets, vol. 11, no. 5, pp. 673-694, 2007.
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