The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - March/April (2012 vol.9)
pp: 438-450
L. Acharya , Dept. of Comput. Sci., Univ. of New Orleans, New Orleans, LA, USA
T. Judeh , Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
Zhansheng Duan , Dept. of Comput. Sci., Univ. of New Orleans, New Orleans, LA, USA
M. Rabbat , Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada
Dongxiao Zhu , Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
ABSTRACT
Reconstruction of signaling pathway structures is essential to decipher complex regulatory relationships in living cells. The existing computational approaches often rely on unrealistic biological assumptions and do not explicitly consider signal transduction mechanisms. Signal transduction events refer to linear cascades of reactions from the cell surface to the nucleus and characterize a signaling pathway. In this paper, we propose a novel approach, Gene Set Gibbs Sampling (GSGS), to reverse engineer signaling pathway structures from gene sets related to the pathways. We hypothesize that signaling pathways are structurally an ensemble of overlapping linear signal transduction events which we encode as Information Flows (IFs). We infer signaling pathway structures from gene sets, referred to as Information Flow Gene Sets (IFGSs), corresponding to these events. Thus, an IFGS only reflects which genes appear in the underlying IF but not their ordering. GSGS offers a Gibbs sampling like procedure to reconstruct the underlying signaling pathway structure by sequentially inferring IFs from the overlapping IFGSs related to the pathway. In the proof-of-concept studies, our approach is shown to outperform the existing state-of-the-art network inference approaches using both continuous and discrete data generated from benchmark networks in the DREAM initiative. We perform a comprehensive sensitivity analysis to assess the robustness of our approach. Finally, we implement GSGS to reconstruct signaling mechanisms in breast cancer cells.
INDEX TERMS
Bioinformatics, Educational institutions, Computational biology, Joints, USA Councils, Computer science, Aerospace electronics,signal transduction., Gene sets, Gibbs sampling, signaling pathways
CITATION
L. Acharya, T. Judeh, Zhansheng Duan, M. Rabbat, Dongxiao Zhu, "GSGS: A Computational Approach to Reconstruct Signaling Pathway Structures from Gene Sets", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 2, pp. 438-450, March/April 2012, doi:10.1109/TCBB.2011.143
REFERENCES
[1] G. Altay and F. Emmert-Streib, “Differences in Gene Network Inference Algorithms on the Network-Level by Ensemble Methods,” Bioinformatics, vol. 26, no. 14, pp. 1738-1744, 2010.
[2] A.J. Butte, P. Tamayo, D. Slonim, T. Golub, and I.S. Kohane, “Discovering Functional Relationships between RNA Expression and Chemotherapeutic Susceptibility Using Relevance Networks,” Proc. Nat'l Academy of Sciences USA, vol. 97, no. 22, pp. 12182-12186, 2000.
[3] A.J. Butte and I.S. Kohane, “Relevance Networks: A First Step toward Finding Genetic Regulatory Networks within Microarray Data,” Analysis of Gene Expression Data, G. Parmigiani, E.S. Garett, R.A. Irizarry, and S.L. Zeger, eds, pp. 428-446, Springer, 2003.
[4] G.F. Cooper and E. Herskovits, “A Bayesian Method for the Induction of Probabilistic Networks from Data,” Machine Learning, vol. 9, no. 4, pp. 309-347, 1992.
[5] A. Dobra, C. Hans, B. Jones, J.R. Nevins, and M. West, “Sparse Graphical Models for Exploring Gene Expression Data,” J. Multivariate Analysis, vol. 90, pp. 196-212, 2004.
[6] 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,” PLoS Biology, vol. 5, no. 1, p. e8, 2007.
[7] N. Friedman, M. Linial, I. Nachman, and D. Peer, “Using Bayesian Networks to Analyze Expression Data,” J. Computational Biology, vol. 7, pp. 601-620, 2000.
[8] T.S. Gardner, D. di Bernardo, D. Lorenz, and J.J. Collins, “Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling,” Science, vol. 301, no. 5629, pp. 102-105, 2003.
[9] A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin, Bayesian Data Analysis, second ed. Chapman and Hall, 2003.
[10] G.H. Givens and J.A. Hoeting, Computational Statistics, John Wiley and Sons, 2005.
[11] F. Iorio, R. Bosotti, E. Scacheri, V. Belcastro, P. Mithbaokar, R. Ferriero, L. Murino, R. Tagliaferri, N. Brunetti-Pierri, A. Isacchi, and D. di Bernardo, “Discovery of Drug Mode of Action and Drug Repositioning from Transcriptional Responses,” Proc. Nat'l Academy of Sciences USA, vol. 107, no. 33, pp. 14621-14626, 2010.
[12] M. Kanehisa and S. Goto, “Kegg: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research, vol. 28, pp. 27-30, 2000.
[13] H. Kishino and P.J. Waddell, “Correspondence Analysis of Genes and Tissue Types and Finding Genetic Links from Microarray Data,” Genome Informatics, vol. 11, pp. 83-95, 2000.
[14] J. Kubica, A. Moore, D. Cohn, and J. Schneider, “cGraph: A Fast Graphbased Method for Link Analysis and Queries,” Proc. IJCAI Text-Mining and Link-Analysis Workshop, 2003.
[15] J. Lamb, E.D. Crawford, D. Peck, J.W. Modell, I.C. Blat, M.J. Wrobel, J. Lerner, J.P. Brunet, A. Subramanian, K.N. Ross, M. Reich, H. Hieronymus, G. Wei, S.A. Armstrong, S.J. Haggarty, P.A. Clemons, R. Wei, S.A. Carr, E.S. Lander, and T.R. Golub, “The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease,” Science, vol. 313, no. 5795, pp. 1929-1935, 2006.
[16] G. Lurje and H.J. Lenz, “EGFR Signaling and Drug Discovery,” Oncology, vol. 77, no. 6, pp. 400-410, 2009.
[17] D. Marbach, T. Schaffter, C. Mattiussi, and D. Floreano, “Generating Realistic in Silico Gene Networks for Performance Assessment of Reverse Engineering Methods,” J. Computational Biology, vol. 16, no. 2, pp. 229-239, 2009.
[18] D. Marbach, R.J. Prill, T. Schaffter, C. Mattiussi, D. Floreano, and G. Stolovitzky, “Revealing Strengths and Weaknesses of Methods for Gene Network Inference,” Proc. Nat'l Academy of Sciences USA, vol. 107, no. 14, pp. 6286-6291, 2010.
[19] A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. Favera, and A. Califano, “ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context,” BMC Bioinformatics, vol. 7, article S7, 2006.
[20] F. Markowetz, D. Kostka, O.G. Troyanskaya, and R. Spang, “Nested Effects Models for High-Dimensional Phenotyping Screens,” Bioinformatics, vol. 23, no. 13, pp. i305-i312, 2007.
[21] P. Mendes, “Framework for Comparative Assessment of Parameter Estimation and Inference Methods in Systems Biology,” Learning and Inference in Computational Systems Biology, N.D. Lawrence, M. Girolami, M. Rattray, G. Sanguinetti, eds., pp. 33-58, MIT Press, 2009.
[22] P.E. Meyer, K. Kontos, and G. Bontempi, “Information-Theoretic Inference of Large Transcriptional Regulatory Networks,” EUROSIP J. Bioinformatics and Systems Biology, vol. 2007, p. 79879, 2007.
[23] P.E. Meyer, F. Lafitte, and Bontempi, “MINET: An Open Source R/Bioconductor Package for Mutual Information Based Network Inference,” BMC Bioinformatics, vol. 9, article 461, 2008.
[24] K. Murphy, “Active Learning of Causal Bayes Net Structure,” technical report, UC Berkeley, 2001.
[25] K. Murphy, “The Bayes Net Toolbox for Matlab,” Computing Science and Statistics, vol. 33, p. 331-350, 2001.
[26] P.M. Navolanic, L.S. Steelman, and J.A. McCubrey, “EGFR Family Signaling and Its Association with Breast Cancer Development and Resistance to Chemotherapy (Review),” Int'l J. Oncology, vol. 22, no. 2, pp. 237-252, 2003.
[27] M.A. Olayioye, “Update on HER-2 as a Target for Cancer Therapy: Intracellular Signaling Pathways of ErbB2/HER-2 and Family Members,” Breast Cancer Research, vol. 3, no. 6, pp. 385-389, 2001.
[28] H. Pang, A. Lin, M. Holford, B.E. Enerson, B. Lu, M.P. Lawton, E. Floyd, and H. Zhao, “Pathway Analysis Using Random Forests Classification and Regression,” Bioinformatics, vol. 22, pp. 2028-2036, 2006.
[29] H. Pang and H. Zhao, “Building Pathway Clusters from Random Forests Classification Using Class Votes,” BMC Bioinformatics, vol. 9, no. 1, article 87, 2008.
[30] R.J. Prill, D. Marbach, J. Saez-Rodriguez, P.K. Sorger, L.G. Alexopoulos, X. Xue, N.D. Clarke, G. Altan-Bonnet, and G. Stolovitzky, “Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges,” PLoS ONE, vol. 5, no. 2, p. e9202, 2010.
[31] M.G. Rabbat, J.R. Treichler, S.L. Wood, and M.G. Larimore, “Understanding the Topology of a Telephone Network via Internally Sensed Network Tomography,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, vol. 3, pp. 977-980, 2005.
[32] M.G. Rabbat, M.A.T. Figueiredo, and R.D. Nowak, “Network Inference from Co-Occurrences,” IEEE Trans. Information Theory, vol. 54, no. 9, pp. 4053-4068, Sept. 2008.
[33] A.J. Richards, B. Muller, M. Shotwell, L.A. Cowart, R. Baerbel, and X. Lu, “Assessing the Functional Coherence of Gene Sets with Metrics Based on the Gene Ontology Graph,” Bioinformatics, vol. 26, no. 12, pp. i79-i87, 2010.
[34] J. Schäfer and K. Strimmer, “An Empirical Bayes Approach to Inferring Large-Scale Gene Association Networks,” Bioinformatics, vol. 21, pp. 754-764, 2005.
[35] G. Schwartz, “Estimating the Dimension of a Model,” The Annals of Statistics, vol. 6, no. 2, pp. 461-464, 1978.
[36] E. Segal, M. Shapira, A. Regev, D. Peer, D. Botstein, D. Koller, and N. Friedman, “Module Networks: Identifying Regulatory Modules and Their Condition-Specific Regulators from Gene Expression Data,” Nature Genetics, vol. 34, pp. 166-176, 2003.
[37] P. Shannon, A. Markiel, O. Ozier, N.S. Baliga, J.T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker, “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks,” Genome Research, vol. 13, no. 11, pp. 2498-2504, 2003.
[38] I. Shmulevich, E.R. Dougherty, S. Kim, and W. Zhang, “Probabilistic Boolean Networks: A Rule-Based Uncertainty Model for Gene Regulatory Networks,” Bioinformatics, vol. 18, no. 2, pp. 261-274, 2002.
[39] I. Shmulevich, I. Gluhovsky, R. Hashimoto, E.R. Dougherty, and W. Zhang, “Probabilistic Boolean Networks: A Rule-Based Uncertainty Model for Gene Regulatory Networks,” Comparative and Functional Genomics, vol. 4, no. 6, pp. 601-608, 2003.
[40] G. Stolovitzky, R.J. Prill, and A. Califano, “Lessons from the DREAM2 Challenges,” Annals of the New York Academy of Sciences, vol. 1158, pp. 159-195, 2009.
[41] A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, and J.P. Mesirov, “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles,” Proc. Nat'l Academy of Sciences USA, vol. 102, pp. 15545-15550, 2005.
[42] A.L. Tarca, S. Draghici, P. Khatri, S.S. Hassan, P. Mittal, J.S. Kim, C.J. Kim, J.P. Kusanovic, and R. Romero, “A Novel Signaling Pathway Impact Analysis,” Bioinformatics, vol. 25, no. 1, pp. 75-82, 2009.
[43] J. Tegner, M.K.S. Yeung, J. Hasty, and J.J. Collins, “Reverse Engineering Gene Networks: Integrating Genetic Perturbations with Dynamical Modeling,” Proc. Nat'l Academy of Sciences USA, vol. 100, no. 10, pp. 5944-5949, 2003.
[44] C.J. Vaske, S.C. Benz, J.Z. Sanborn, D. Earl, C. Szeto, J. Zhu, D. Haussler, and J.M. Stuart, “Inference of Patient-Specific Pathway Activities from Multi-Dimensional Cancer Genomics Data Using Paradigm,” Bioinformatics, vol. 26, no. 12, pp. i237-i245, 2010.
[45] T.R. Xu, V. Vyshemirsky, A. Gormand, A. von Kriegsheim, M. Girolami, G.S. Baillie, D. Ketley, A.J. Dunlop, G. Milligan, M.D. Houslay, and W. Kolch, “Inferring Signaling Pathway Topologies from Multiple Perturbation Measurements of Specific Biochemical Species,” Science Signaling, vol. 3, no. 134 p. ra20, 2010.
[46] D. Zhu, A.O. Hero, Z.S. Qin, and A. Swaroop, “High Throughput Screening of Co-Expressed Gene Pairs with Controlled False Discovery Rate (FDR) and Minimum Acceptable Strength (MAS),” J. Computational Biology, vol. 12, no. 7, pp. 1029-1045, 2005.
[47] D. Zhu, M.G. Rabbat, A.O. Hero, R. Nowak, and M.A.G. Figueirado, “De Novo Reconstructing Signaling Pathways from Multiple Data Sources,” New Research in Signaling Transduction, Nova Publisher, 2006.
38 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool