The Community for Technology Leaders
RSS Icon
Issue No.06 - Nov.-Dec. (2012 vol.9)
pp: 1696-1708
S. Hashemikhabir , Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey
E. S. Ayaz , Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
Y. Kavurucu , Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
T. Can , Dept. of Comput. Eng., Middle East Tech. Univ., Ankara, Turkey
T. Kahveci , Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this paper, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of edit operations on a given reference network. The edit operations transform the reference network to a network that satisfies the RNAi observations. We show that using a reference network does not simplify the computational complexity of the problem. Therefore, we propose two methods which provide near optimal results and can scale well for reconstructing networks up to hundreds of components. We validate the proposed methods on synthetic and real data sets. Comparison with the state of the art on real signaling networks shows that the proposed methodology can scale better and generates biologically significant results.
Proteins, Network topology, Biomedical signal processing, Computational biology, Bioinformatics, Genetics,network editing, Signaling network, RNAi
S. Hashemikhabir, E. S. Ayaz, Y. Kavurucu, T. Can, T. Kahveci, "Large-Scale Signaling Network Reconstruction", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 6, pp. 1696-1708, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.128
[1] T. Akutsu, M. Hayashida, W.-K. Ching, and M.K. Ng, “Control of Boolean Networks: Hardness Results and Algorithms for Tree Structured Networks,” J. Theoretical Biology, vol. 244, no. 4, pp. 670-679, 2007.
[2] T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms. MIT Press, 2001.
[3] E.A. Dinic, “An Algorithm for the Solution of the Max-Flow Problem with the Polynomial Estimation,” Soviet Math. Dokl., vol. 11, pp. 1277-1280, 1970.
[4] M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, 1979.
[5] A. George and J.W.H. Liu, Computer Solutions of Large Sparse Positive Definite Systems. Prentice-Hall, 1990.
[6] J. Hirosumi, G. Tuncman, L. Chang, C.Z. Gorgun, K.T. Uysal, K. Maeda, M. Karin, and G.S. Hotamisligil, “A Central Role for JNK in Obesity and Insulin Resistance,” Nature, vol. 420, pp. 333-336, 2002.
[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] M. Kanehisa and S. Goto, “Kegg: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27-30, 2000.
[9] 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.
[10] A. Lan, I.Y. Smoly, G. Rapaport, S. Lindquist, E. Fraenkel, and E. Yeger-Lotem, “Responsenet: Revealing Signaling and Regulatory Networks Linking Genetic and Transcriptomic Screening Data,” Nucleic Acids Research, vol. 39, no. suppl 2, pp. W424-W429, 2011.
[11] C. Langmead and S. Jha, “Symbolic Approaches to Finding Control Strategies in Boolean Networks,” J. Bioinformatics and Computational Biology, vol. 7, no. 2, pp. 323-338, 2009.
[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,” arXiv:cond-mat/0312028, 2003.
[13] O. Ourfali, T. Shlomi, T. Ideker, E. Ruppin, and R. Sharan, “Spine: A Framework for Signaling-Regulatory Pathway Inference from Cause-Effect Experiments,” Bioinformatics, vol. 23, no. 13, pp. i359-i366, 2007.
[14] O.E. Ozsoy and T. Can, “Construction of Signaling Pathways from PPI and RNAi Data Using Linear Programming,” technical report, Dept. of Computer Eng., Middle East Technical Univ., . 2012.
[15] L.C. Platanias, “Mechanisms of Type-i- and Type-ii-Interferon-Mediated Signalling,” Nature Rev. Immunology, vol. 5, pp. 375-386, 2005.
[16] P. Polakis, “The Many Ways of Wnt in Cancer,” Current Opinion in Genetics and Development, vol. 17, no. 1, pp. 45-51, 2007.
[17] 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), pp. 108-118, 2007.
[18] 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, p. 1, 2009.
[19] 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.
[20] T. Shlomi, D. Segal, E. Ruppin, and R. Sharan, “QPath: A Method for Querying Pathways in a Protein-Protein Interaction Network,” BMC Bioinformatics, vol. 7, no. 1, p. 199, 2006.
[21] R. Singh, “Algorithms for the Analysis of Protein Interaction Networks,” PhD thesis, MIT, 2011.
[22] S.W. Sloan, “An Algorithm for Profile and Wavefront Reduction of Sparse Matrices,” Int'l J. Numerical Methods in Eng., vol. 23, pp. 239-251, 1986.
[23] D. Szklarczyk, A. Franceschini, M. Kuhn, M. Simonovic, A. Roth, P. Minguez, T. Doerks, M. Stark, J. Muller, P. Bork, L.J. Jensen, and C. von Mering, “The STRING Database in 2011: Functional Interaction Networks of Proteins, Globally Integrated and Scored,” Nucleic Acids Research, vol. 39, pp. 561-568, 2011.
[24] The Gene Ontology Consortium, “The Gene Ontology Project in 2008,” Nucleic Acids Research, vol. 36, no. suppl 1, pp. D440-D444, 2008.
[25] Z. Tu, C. Argmann, K.K. Wong, L.J. Mitnaul, S. Edwards, I.C. Sach, J. Zhu, and E.E. Schadt, “Integrating Sirna and Protein-Protein Interaction Data to Identify an Expanded Insulin Signaling Network,” Genome Research, vol. 19, no. 6, pp. 1057-1067, 2009.
[26] A. Vinayagam, U. Stelzl, R. Foulle, S. Plassmann, M. Zenkner, J. Timm, H.E. Assmus, M.A. Andrade-Navarro, and E.E. Wanker, “A Directed Protein Interaction Network for Investigating Intracellular Signal Transduction,” Science Signaling, vol. 4, no. 189, p. rs8, 2011.
[27] H.S. Wiley, “Integrating Multiple Types of Data for Signaling Research: Challenges and Opportunities,” Science Signaling, vol. 4, no. 160, p. pe9, 2011.
[28] C.H. Yeang, T. Ideker, and T. Jaakkola, “Physical Network Models,” J. Computational Biology, vol. 11, nos. 2/3, pp. 243-262, 2004.
[29] C.H. Yeang, H.C. Mak, S. McCuine, C. Workman, T. Jaakkola, and T. Ideker, “Validation and Refinement of Gene-Regulatory Pathways on a Network of Physical Interactions,” Genome Biology, vol. 6, no. 7, pp. R62.1-R62.10, 2005.
39 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool