The Community for Technology Leaders
RSS Icon
Issue No.02 - March/April (2012 vol.9)
pp: 358-371
K. Kentzoglanakis , Div. of Math. Biol., MRC Nat. Inst. of Med. Res., London, UK
M. Poole , Sch. of Comput., Univ. of Portsmouth, Portsmouth, UK
In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures.
Recurrent neural networks, Biological system modeling, Gene expression, Mathematical model, Computer architecture, Regulators,degree distribution., Gene regulatory networks, network inference, recurrent neural networks, swarm intelligence, particle swarm optimization, ant colony optimization
K. Kentzoglanakis, M. Poole, "A Swarm Intelligence Framework for Reconstructing Gene Networks: Searching for Biologically Plausible Architectures", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 2, pp. 358-371, March/April 2012, doi:10.1109/TCBB.2011.87
[1] H. Kitano, “Systems Biology: A Brief Overview,” Science, vol. 295, no. 5560, pp. 1662-1664, 2002.
[2] E.P. van Someren, L.F.A. Wessels, E. Backer, and M.J.T. Reinders, “Genetic Network Modeling,” Pharmacogenomics, vol. 3, no. 4, pp. 507-525, 2002.
[3] D.L. Donoho, “High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality,” Proc. Am. Math. Soc. Conf. Math. Challenges of the 21st Century, 2000.
[4] B. Bollobás, C. Borgs, J. Chayes, and O. Riordan, “Directed Scale-Free Graphs,” SODA '03: Proc. 14th Ann. ACM-SIAM Symp. Discrete Algorithms, pp. 132-139, 2003.
[5] M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein, “Cluster Analysis and Display of Genome-Wide Expression Patterns,” Proc. Nat'l Academy of Sciences USA, vol. 95, no. 25, pp. 14863-14868, 1998.
[6] X. Wen, S. Fuhrman, G.S. Michaels, D.B. Carr, S. Smith, J.L. Barker, and R. Somogyi, “Large-Scale Temporal Gene Expression Mapping of Central Nervous System Development,” Proc. Nat'l Academy of Sciences USA, vol. 95, no. 1, pp. 334-339, 1998.
[7] A.J. Butte and I.S. Kohane, “Mutual Information Relevance Networks: Functional Genomic Clustering Using Pairwise Entropy Measurements,” Proc. Pacific Symp. Biocomputing, pp. 418-429, 2000.
[8] K. Basso, A.A. Margolin, G. Stolovitzky, U. Klein, D.R. Favera, and A. Califano, “Reverse-Engineering of Regulatory Networks in Human B Cells,” Nature Genetics, vol. 37, no. 4, pp. 382-390, 2005.
[9] A.A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, D.R. Favera, and A. Califano, “ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context,” BMC Bioinformatics, vol. 7 (Suppl. 1):S7, 2006.
[10] N. Friedman, M. Linial, I. Nachman, and D. Pe'er, “Using Bayesian Networks to Analyze Gene Expression Data,” J. Computational Biology, vol. 7, nos. 3/4, pp. 601-620, 2000.
[11] D. Husmeier, “Sensitivity and Specificity of Inferring Genetic Regulatory Interactions from Microarray Experiments with Dynamic Bayesian Networks,” Bioinformatics, vol. 19, no. 17, pp. 2271-2282, 2003.
[12] B.E. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and D.F. Buc, “Gene Network Inference Using Dynamic Bayesian Networks,” Bioinformatics, vol. 19 (Suppl. 2): ii138-ii148, 2003.
[13] I. Pournara and L. Wernisch, “Reconstruction of Gene Networks Using Bayesian Learning and Manipulation Experiments,” Bioinformatics, vol. 20, no. 17, pp. 2934-2942, 2004.
[14] H. De Jong, “Modeling and Simulation of Genetic Regulatory Systems: A Literature Review,” J. Computational Biology, vol. 9, no. 1, pp. 69-105, 2002.
[15] S.A. Kauffman, “Metabolic Stability and Epigenesis in Randomly Constructed Genetic Nets,” J. Theoretical Biology, vol. 22, pp. 437-467, 1969.
[16] M.A. Savageau, “Power-Law Formalism: A Canonical Nonlinear Approach to Modeling and Analysis,” Proc. First World Congress of Nonlinear Analysts '92, vol. 4, pp. 3323-3334, 1995.
[17] P. D'Haeseleer, S. Liang, and R. Somogyi, “Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering,” Bioinformatics, vol. 16, no. 8, pp. 707-726, 2000.
[18] D.C. Weaver, C.T. Workman, and G.D. Stormo, “Modeling Regulatory Networks with Weight Matrices,” Proc. Pacific Symp. Biocomputing, vol. 4, pp. 112-123, 1999.
[19] E.P. van Someren, L.F.A. Wessels, and M.J.T. Reinders, “Linear Modeling of Genetic Networks from Experimental Data,” Proc. Eighth Int'l Conf. Intelligent Systems for Molecular Biology, pp. 355-366, 2000.
[20] E. Mjolsness, D.H. Sharp, and J. Reinitz, “A Connectionist Model of Development,” J. Theoretical Biology, vol. 152, pp. 429-453, 1991.
[21] E. Mjolsness, T. Mann, R. Castano, and B. Wold, “From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation among Gene Classes from Large-Scale Gene Expression Data,” Advances in Neural Information Processing Systems, vol. 12, pp. 928-934, MIT Press, 2000.
[22] J. Vohradsky, “Neural Model of the Genetic Network,” J. Biological Chemistry, vol. 276, no. 39, pp. 36168-36173, 2001.
[23] M. Wahde and J. Hertz, “Coarse-Grained Reverse Engineering of Genetic Regulatory Networks,” Biosystems, vol. 55, nos. 1-3, pp. 129-136, 2000.
[24] M. Wahde and J. Hertz, “Modeling Genetic Regulatory Dynamics in Neural Development,” J. Computational Biology, vol. 8, no. 4, pp. 429-442, 2001.
[25] R. Xu, D.C. Wunsch, and R.L. Frank, “Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 681-692, Oct.-Dec. 2007.
[26] D. Marbach, C. Mattiussi, and D. Floreano, “Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks,” Annals of New York Academy of Sciences, vol. 1158, pp. 234-245, 2009.
[27] Z. Bar-Joseph, G. Gerber, D.K. Gifford, T.S. Jaakola, and I. Simon, “A New Approach to Analyzing Gene Expression Time Series Data,” Proc. Sixth Ann. Int'l Conf. Computational Biology, pp. 39-48, 2002.
[28] S. Kimura, K. Ide, A. Kashihara, M. Kano, M. Hatakeyama, R. Masui, N. Nakagawa, S. Yokoyama, S. Kuramitsu, and A. Konagaya, “Inference of S-System Models of Genetic Networks Using a Cooperative Coevolutionary Algorithm,” Bioinformatics, vol. 21, no. 7, pp. 1154-1163, 2005.
[29] O.R. Gonzalez, C. Küper, K. Jung, P.C. Naval, and E. Mendoza, “Parameter Estimation Using Simulated Annealing for S-System Models of Biochemical Networks,” Bioinformatics, vol. 23, no. 4, pp. 480-486, 2007.
[30] S. Kikuchi, D. Tominaga, M. Arita, K. Takahashi, and M. Tomita, “Dynamic Modeling of Genetic Networks Using Genetic Algorithm and S-System,” Bioinformatics, vol. 19, no. 5, pp. 643-650, 2003.
[31] S.Y. Ho, C.H. Hsiesh, F.C. Yu, and H.L. Huang, “An Intelligent Two-Stage Evolutionary Algorithm for Dynamic Pathway Identification from Gene Expression Profiles,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 648-660, Oct.-Dec. 2007.
[32] H.W. Ressom, Y. Zhang, J. Xuan, Y. Wang, and R. Clarke, “Inference of Gene Regulatory Networks from Time Course Gene Expression Data Using Neural Networks and Swarm Intelligence,” Proc. IEEE Symp. Computational Intelligence and Bioinformatics and Computational Biology, pp. 1-8, 2006.
[33] Y. Maki, T. Ueda, M. Okamoto, N. Uematsu, K. Inamura, K. Uchida, Y. Takahashi, and Y. Eguchi, “Inference of Genetic Network Using the Expression Profile Time Course Data of Mouse P19 Cells,” Genome Informatics, vol. 13, pp. 382-383, 2002.
[34] N. Noman and H. Iba, “Inferring Gene Regulatory Networks Using Differential Evolution with Local Search Heuristics,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 634-647, Oct.-Dec. 2007.
[35] M. Vilela, I.C. Chou, S. Vinga, T.R. Vasconcelos, Voit, and J.S. Almeida, “Parameter Optimization in S-System Models,” BMC Systems Biology, vol. 2, article 35, 2008.
[36] C. Spieth, F. Streichert, N. Speer, and A. Zell, “Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments,” Proc. Genetic and Evolutionary Computation Conf. (GECCO '04), pp. 461-470, 2004.
[37] E. Keedwell and A. Narayanan, “Discovering Gene Networks with a Neural-Genetic Hybrid,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 2, no. 3, pp. 231-242, July-Sept. 2005.
[38] R. Somogyi and C. Sniegoski, “Modeling the Complexity of Genetic Networks: Understanding Multigenic and Pleiotropic Regulation,” Complexity, vol. 1, pp. 45-63, 1996.
[39] T. Akutsu, S. Miyano, and S. Kuhara, “Identification of Genetic Networks from a Small Number of Gene Expression Patterns under the Boolean Network Model,” Proc. Pacific Symp. Biocomputing, pp. 17-28, 1999.
[40] S. Liang, S. Fuhrman, and R. Somogyi, “REVEAL, a General Reverse Engineering Algorithm for Inference of Genetic Network Architectures,” Proc. Pacific Symp. Biocomputing, vol. 3, pp. 18-29, 1998.
[41] T. Akutsu, S. Miyano, and S. Kuhara, “Inferring Qualitative Relations in Genetic Networks and Metabolic Pathways,” Bioinformatics, vol. 16, no. 8, pp. 727-734, 2000.
[42] T.S. Gardner, D. Dibernardo, 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.
[43] K. Kentzoglanakis, M. Poole, and C. Adams, “Incorporating Heuristics in a Swarm Intelligence Framework for Inferring Gene Regulatory Networks from Gene Expression Time Series,” Proc. Sixth Int'l Workshop Ant Colony Optimization and Swarm Intelligence, pp. 323-330, 2008.
[44] H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabási, “The Large-Scale Organization of Metabolic Networks,” Nature, vol. 407, pp. 651-654, 2000.
[45] H. Jeong, S.P. Mason, A.L. Barabási, and Z.N. Oltvai, “Lethality and Centrality in Protein Networks,” Nature, vol. 411, pp. 41-42, 2001.
[46] I. Farkas, H. Jeong, T. Vicsek, A.L. Barabási, and Z.N. Oltvai, “The Topology of the Transcription Regulatory Networks in the Yeast, Saccharomyces Cerevisiae,” Physica A: Satistical Mechanics and Its Applications, vol. 318, nos. 3/4, pp. 601-612, 2003.
[47] A.H. Tong et al., “Global Mapping of the Yeast Genetic Interaction Network,” Science, vol. 303, no. 5659, pp. 808-813, 2004.
[48] N. Guelzim, S. Bottani, P. Bourgine, and F. Kepes, “Topological and Causal Structure of the Yeast Transcriptional Regulatory Network,” Nature Genetics, vol. 31, no. 1, pp. 60-63, 2002.
[49] N.M. Luscombe, M.M. Babu, H. Yu, M. Snyder, S.A. Teichmann, and M. Gerstein, “Genomic Analysis of Regulatory Network Dynamics Reveals Large Topological Changes,” Nature, vol. 431, pp. 308-312, 2004.
[50] A.L. Barabási and R. Albert, “Emergence of Scaling in Random Networks,” Science, vol. 286, no. 5439, pp. 509-512, 1999.
[51] H. Kitano, “Computational Systems Biology,” Nature, vol. 420, no. 6912, pp. 206-210, 2002.
[52] B. Bollobás and O. Riordan, “Robustness and Vulnerability of Scale-Free Random Graphs,” Internet Math., vol. 1, no. 1, pp. 1-35, 2003.
[53] R. Tanaka, T. Yi, and J. Doyle, “Some Protein Interaction Data Do Not Exhibit Power Law Statistics,” FEBS Letters, vol. 579, no. 23, pp. 5140-5144, 2005.
[54] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. IEEE Int'l Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995.
[55] J. Kennedy and W.M. Spears, “Matching Algorithms to Problems: An Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimodal Problem Generator,” Proc. IEEE Int'l Conf. Evolutionary Computation (ICEC '98), pp. 78-83, 1998.
[56] E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among Five Evolutionary-Based Optimization Algorithms,” Advanced Eng. Informatics, vol. 19, no. 1, pp. 43-53, 2005.
[57] M. Clerc and J. Kennedy, “The Particle Swarm - Explosion, Stability and Convergence in a Multidimensional Complex Space,” IEEE Trans. Evolutionary Computation, vol. 6, no. 1, pp. 58-73, Feb. 2002.
[58] Y. Shi and R. Eberhart, “Parameter Selection in Particle Swarm Optimization,” Proc. Int'l Conf. Evolutionary Programming VII, pp. 591-600, 1998.
[59] M. Dorigo, V. Maniezzo, and A. Colorni, “The Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Trans. Systems, Man and Cybernetics, Part B, vol. 26, no. 1, pp. 29-41, Feb. 1996.
[60] G. Theraulaz and E. Bonabeau, “A Brief History of Stigmergy,” Artificial Life, vol. 5, no. 2, pp. 97-116, 1999.
[61] R. Albert and A.L. Barabási, “Topology of Evolving Networks: Local Events and Universality,” Physical Rev. Letters, vol. 85, no. 24, pp. 5234-5237, 2000.
[62] P.L. Krapivsky and S. Redner, “Organization of Growing Random Networks,” Physical Rev. E, vol. 63, no. 6, 2001.
[63] G. Bianconi and A.L. Barabási, “Competition and Multiscaling in Evolving Networks,” Europhysics Letters, vol. 54, no. 4, pp. 436-442, 2001.
[64] 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.
[65] G. Stolovitzky, D. Monroe, and A. Califano, “Dialogue on Reverse-Engineering Assessment and Methods: The Dream of High-Throughput Pathway Inference,” Annals New York Academy of Sciences, vol. 1115, pp. 1-22, 2007.
[66] 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.
[67] B. Michel, “After 30 Years of Study, the Bacterial SOS Response Still Surprises Us,” PLoS Biology, vol. 3, no. 7, 2005.
[68] M. Ronen, R. Rosenberg, B.I. Shraiman, and U. Alon, “Assigning Numbers to the Arrows: Parameterizing a Gene Regulation Network by Using Accurate Expression Kinetics.,” Proc. Nat'l Academy of Sciences USA, vol. 99, no. 16, pp. 10555-10560, 2002.
[69] D.-Y. Cho, K.-H. Cho, and B.-T. Zhang, “Identification of Biochemical Networks by s-Tree Based Genetic Programming,” Bioinformatics, vol. 22, no. 13, pp. 1631-1640, 2006.
[70] S. Kimura, K. Sonoda, S. Yamane, H. Maeda, K. Matsumura, and M. Hatakeyama, “Function Approximation Approach to the Inference of Reduced NGnet Models of Genetic Networks,” BMC Bioinformatics, vol. 9, no. 1, p. 23, 2008.
[71] S. Kimura, S. Nakayama, and M. Hatakeyama, “Genetic Network Inference as a Series of Discrimination Tasks,” Bioinformatics, vol. 25, no. 7, pp. 918-925, 2009.
[72] M. Kabir, N. Noman, and H. Iba, “Reverse Engineering Gene Regulatory Network from Microarray Data Using Linear Time-Variant Model,” BMC Bioinformatics, vol. 11, no. Suppl. 1, article S56, 2010.
[73] N. Noman and H. Iba, “Reverse Engineering Genetic Networks Using Evolutionary Computation,” Genome Informatics, vol. 16, no. 2, pp. 205-214, 2005.
[74] Y. Wei, J.M. Lee, D.R. Smulski, and R.A. LaRossa, “Global Impact of sdiA Amplification Revealed by Comprehensive Gene Expression Profiling of Escherichia Coli,” J. Bacteriology, vol. 183, no. 7, pp. 2265-2272, 2001.
29 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool