The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - July-Aug. (2013 vol.10)
pp: 869-883
Oyku Eren Ozsoy , Inf. Inst., Middle East Tech. Univ., Ankara, Turkey
Tolga Can , Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey
ABSTRACT
Inference of topology of signaling networks from perturbation experiments is a challenging problem. Recently, the inference problem has been formulated as a reference network editing problem and it has been shown that finding the minimum number of edit operations on a reference network to comply with perturbation experiments is an NP-complete problem. In this paper, we propose an integer linear optimization (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP model guarantees the optimal solution; however, is practical only for small signaling networks of size 10-15 genes due to computational complexity. To scale for large signaling networks, we propose a divide and conquer-based heuristic, in which a given reference network is divided into smaller subnetworks that are solved separately and the solutions are merged together to form the solution for the large network. We validate our proposed approach on real and synthetic data sets, and comparison with the state of the art shows that our proposed approach is able to scale better for large networks while attaining similar or better biological accuracy.
INDEX TERMS
Network topology, Proteins, Topology, Linear programming, Data models, Optimization,linear optimization, Signaling network topology, protein-protein interactions, RNA interference
CITATION
Oyku Eren Ozsoy, Tolga Can, "A Divide and Conquer Approach for Construction of Large-Scale Signaling Networks from PPI and RNAi Data Using Linear Programming", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 4, pp. 869-883, July-Aug. 2013, doi:10.1109/TCBB.2013.80
REFERENCES
[1] A.L. Brass, D.M. Dykxhoorn, Y. Benita, N. Yan, A. Engelman, R.J. Xavier, J. Lieberman, and S.J. Elledge, "Identification of Host Proteins Required for HIV Infection through a Functional Genomic Screen," Science, vol. 319, no. 5865, pp. 921-926, 2008.
[2] A. Fire, S. Xu, M.K. Montgomery, S.A. Kostas, S.E. Driver, and C.C. Mello, "Potent and Specific Genetic Interference by Double-Stranded RNA in Caenorhabditis elegans," Nature, vol. 391, no. 6669, pp. 806-811, 1998.
[3] A. Friedman and N. Perrimon, "A Functional RNAi Screen for Regulators of Receptor Tyrosine Kinase and ERK Signalling," Nature, vol. 444, no. 7116, pp. 230-234, 2006.
[4] A. Gitter, J. Klein-Seetharaman, and J. Gupta, "Discovering Pathways by Orienting Edges in Protein Interaction Networks," Nucleic Acids Research, vol. 39, no. 4,article e22, 2011.
[5] S. Hashemikhabir, E.S. Ayaz, Y. Kavurucu, T. Can, and T. Kahveci, "Large-Scale Signaling Network Reconstruction," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 9, no. 6, pp. 1696-1708, Nov./Dec. 2012.
[6] F.S. Hillier and G.J. Lieberman, Introduction to Operations Research. pp. 586-587, McGraw-Hill, 2001.
[7] L. Kaderali, E. Dazert, U. Zeuge, M. Frese, and R. Bartenschlager, "Reconstructing Signaling Pathways from RNAi Data Using Probabilistic Boolean Threshold Networks," Bioinformatics, vol. 25, no. 17, pp. 2229-2235, 2009.
[8] L. Kaderali and N. Radde, "Inferring Gene Regulatory Networks from Expression Data," Computational Intelligence in Bioinformatics, Studies in Computational Intelligence 94, A. Kelemen, A. Abraham, Y. Chen, eds., Springer-Verlag, 2008.
[9] M. Kanehisa and S. Goto, "Kegg: Kyoto Encyclopedia of Genes and Genomes," Nucleic Acids Research, vol. 28, no. 1, pp. 27-30, 2000.
[10] S. Kerrien, B. Aranda, L. Breuza, A. Bridge, F. Broackes-Carter, C. Chen, M. Duesbury, M. Dumousseau, M. Feuermann, U. Hinz, C. Jandrasits, R.C. Jimenez, J. Khadake, U. Mahadevan, P. Masson, I. Pedruzzi, E. Pfeiffenberger, P. Porras, A. Raghunath, B. Roechert, S. Orchard, and H. Hermjakob, "The Intact Molecular Interaction Database in 2012," Nucleic Acids Research, vol. 40, no. D1, pp. D841- D846, 2012.
[11] F. Markowetz, D. Kostka, O.G. Troyanskaya, and R. Spang, "Nested Effects Models for High-Dimensional Phenotyping Screens," Bioinformatics, vol. 23, no. 13, pp. 305-312, 2007.
[12] R. Milo, N. Kashtan, S. Itzkovitz, M.E.J. Newman, and U. Alon, "On the Uniform Generation of Random Graphs with Prescribed Degree Sequences," http://arxiv.org/abs/condmat0312028, 2003.
[13] L.C. Platanias, "Mechanisms of Type-I- and Type-II-Interferon-Mediated Signalling," Nature Rev. Immunology, vol. 5, pp. 375-386, 2005.
[14] D. Ruths, J.-T. Tseng, L. Nakhleh, and P.T. Ram, "De Novo Signaling Pathway Predictions Based on Protein-Protein Interaction, Targeted Therapy and Protein Microarray Analysis," Proc. Satellite Conf. Systems Biology and Computational Proteomics (RECOMB-SAT '07), pp. 108-118, 2007.
[15] R. Sacher, L. Stergiou, and L. Pelkmans, "Lessons from Genetics: Interpreting Complex Phenotypes in RNAi Screens," Current Opinion Cell Biology, vol. 20, no. 4, pp. 483-489, 2008.
[16] O. Sahin, H. Frohlich, C. Lobke, U. Korf, S. Burmester, M. Majety, J. Mattern, I. Schupp, C. Chaouiya, D. Thieffry, A. Poustka, S. Wiemann, T. Beissbarth, and D. Arlt, "Modeling Erbb Receptor-Regulated g1/s Transition to Find Novel Targets for De Novo Trastuzumab Resistance," BMC Systems Biology, vol. 3, no. 1,article 1, 2009.
[17] J. Scott, T. Ideker, R.M. Karp, and R. Sharan, "Efficient Algorithms for Detecting Signaling Pathways in Protein Interaction Networks," J. Computational Biology, vol. 13, no. 2, pp. 133-144, 2006.
[18] R. Singh, "Algorithms for the Analysis of Protein Interaction Networks," PhD Dissertation, Massachusetts Inst. of Tech nology, 2011.
[19] M. Steffen, A. Petti, J. Aach, P. D'haeseleer, and G. Church, "Automated Modelling of Signal Transduction Networks," BMC Bioinformatics, vol. 3, article 34, 2002.
112 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool