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Scientifica
Volume 2016 (2016), Article ID 1060843, 14 pages
http://dx.doi.org/10.1155/2016/1060843
Research Article

Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence

1Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector III, Salt Lake City, Kolkata, West Bengal 700 098, India
2Department of Information Technology, North Eastern Hill University, Umshing-Mawkynroh, Shillong, Meghalaya 793 022, India

Received 28 December 2015; Revised 19 April 2016; Accepted 24 April 2016

Academic Editor: Matthias Futschik

Copyright © 2016 Abhinandan Khan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. J. McLachlan, K.-A. Do, and C. Ambroise, Analyzing Microarray Gene Expression Data, vol. 422, John Wiley & Sons, New York, NY, USA, 2005. View at Publisher · View at Google Scholar
  2. R. Xu and D. Wunsch II, “Survey of clustering algorithms,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 645–678, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. Z. Bar-Joseph, “Analyzing time series gene expression data,” Bioinformatics, vol. 20, no. 16, pp. 2493–2503, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. P. D'haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, “Linear modelling of mRNA expression levels during CNS development and injury,” in Pacific Symposium on Biocomputing, vol. 4, no. 1, pp. 41–52, 1999.
  5. H. De Jong, J.-L. Gouzé, C. Hernandez, M. Page, T. Sari, and J. Geiselmann, “Qualitative simulation of genetic regulatory networks using piecewise-linear models,” The Bulletin of Mathematical Biology, vol. 66, no. 2, pp. 301–340, 2004. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. I. M. Ong, J. D. Glasner, and D. Page, “Modelling regulatory pathways in E. coli from time series expression profiles,” Bioinformatics, vol. 18, supplement 1, pp. S241–S248, 2002. View at Google Scholar · View at Scopus
  7. D. L. Donoho, “High-dimensional data analysis: the curses and blessings of dimensionality,” AMS Math Challenges Lecture, pp. 1–32, 2000. View at Google Scholar
  8. 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. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Bolouri and E. H. Davidson, “Modeling transcriptional regulatory networks,” BioEssays, vol. 24, no. 12, pp. 1118–1129, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. L. H. Augenlicht and D. Kobrin, “Cloning and screening of sequences expressed in a mouse colon tumour,” Cancer Research, vol. 42, no. 3, pp. 1088–1093, 1982. View at Google Scholar
  11. E. M. Southern, “Detection of specific sequences among DNA fragments separated by gel electrophoresis,” Journal of Molecular Biology, vol. 98, no. 3, pp. 503–517, 1975. View at Publisher · View at Google Scholar · View at Scopus
  12. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, “Cluster analysis and display of genome-wide expression patterns,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 25, pp. 14863–14868, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. X. Wen, S. Fuhrman, G. S. Michaels et al., “Large-scale temporal gene expression mapping of central nervous system development,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 1, pp. 334–339, 1998. View at Publisher · View at Google Scholar · View at Scopus
  14. A. J. Butte and I. S. Kohane, “Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements,” in Proceedings of the Pacific Symposium on Biocomputing (PSB '00), vol. 5, pp. 418–429, January 2000.
  15. K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-Favera, and A. Califano, “Reverse engineering of regulatory networks in human B cells,” Nature Genetics, vol. 37, no. 4, pp. 382–390, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. A. A. Margolin, I. Nemenman, K. Basso et al., “ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context,” BMC Bioinformatics, vol. 7, supplement 1, article S7, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. N. Friedman, M. Linial, I. Nachman, and D. Pe'er, “Using Bayesian networks to analyse expression data,” Journal of Computational Biology, vol. 7, no. 3-4, pp. 601–620, 2000. View at Publisher · View at Google Scholar
  18. 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. View at Publisher · View at Google Scholar · View at Scopus
  19. B.-E. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. D'Alché-Buc, “Gene networks inference using dynamic Bayesian networks,” Bioinformatics, vol. 19, supplement 2, pp. ii138–ii148, 2003. View at Publisher · View at Google Scholar · View at Scopus
  20. I. Pournara and L. Wernisch, “Reconstruction of gene networks using Bayesian learning and manipulation experiments,” Bioinformatics, vol. 20, no. 17, pp. 2934–2942, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. H. de Jong, “Modeling and simulation of genetic regulatory systems: a literature review,” Journal of Computational Biology, vol. 9, no. 1, pp. 67–103, 2004. View at Publisher · View at Google Scholar
  22. S. A. Kauffman, “Metabolic stability and epigenesis in randomly constructed genetic nets,” Journal of Theoretical Biology, vol. 22, no. 3, pp. 437–467, 1969. View at Publisher · View at Google Scholar · View at Scopus
  23. E. O. Voit, Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Cambridge University Press, Cambridge, UK, 2000.
  24. M. Vilela, I.-C. Chou, S. Vinga, A. T. R. Vasconcelos, E. O. Voit, and J. S. Almeida, “Parameter optimization in S-system models,” BMC Systems Biology, vol. 2, no. 1, article 35, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. M. A. Savageau, “Power-law formalism: a canonical nonlinear approach to modelling and analysis,” in Proceedings of the World Congress of Nonlinear Analysts, vol. 92, pp. 3323–3334, 1996.
  26. 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. View at Publisher · View at Google Scholar · View at Scopus
  27. D. C. Weaver, C. T. Workman, and G. D. Stormo, “Modeling regulatory networks with weight matrices,” in Proceedings of the Pacific Symposium on Biocomputing (PSB '99), vol. 4, pp. 112–123, January 1999.
  28. E. van Someren, L. F. A. Wessels, and M. Reinders, “Linear modelling of genetic networks from experimental data,” in Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, vol. 8, pp. 355–366, 2000.
  29. E. Mjolsness, D. H. Sharp, and J. Reinitz, “A connectionist model of development,” Journal of Theoretical Biology, vol. 152, no. 4, pp. 429–453, 1991. View at Publisher · View at Google Scholar · View at Scopus
  30. E. Mjolsness, T. Mann, R. Castaño, and B. Wold, “From co-expression to co-regulation: an approach to inferring transcriptional regulation among gene classes from large-scale expression data,” in Neural Information Processing Systems, 1999. View at Google Scholar
  31. J. Vohradsky, “Neural model of the genetic network,” The Journal of Biological Chemistry, vol. 276, no. 39, pp. 36168–36173, 2001. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Wahde and J. Hertz, “Coarse-grained reverse engineering of genetic regulatory networks,” BioSystems, vol. 55, no. 1–3, pp. 129–136, 2000. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Wahde and J. Hertz, “Modeling genetic regulatory dynamics in neural development,” Journal of Computational Biology, vol. 8, no. 4, pp. 429–442, 2001. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Xu, D. C. Wunsch II, and R. L. Frank, “Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 681–692, 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. D. Marbach, C. Mattiussi, and D. Floreano, “Replaying the evolutionary tape: biomimetic reverse engineering of gene networks,” Annals of the New York Academy of Sciences, vol. 1158, no. 1, pp. 234–245, 2009. View at Publisher · View at Google Scholar · View at Scopus
  36. P. D'Haeseleer, Reconstructing gene networks from large-scale gene expression data [Ph.D. thesis], University of New Mexico, 2000.
  37. E. P. van Someren, L. F. A. Wessels, and M. J. T. Reindersm, “Genetic network models: a comparative study,” in Proceedings of the International Symposium on Biomedical Optics (BiOS '01), pp. 236–247, International Society for Optics and Photonics, San Jose, Calif, USA, 2001.
  38. D. C. Weaver, C. T. Workman, and G. D. Stormo, “Modeling regulatory networks with weight matrices,” in Pacific Symposium on Biocomputing, vol. 4, pp. 112–123, 1999.
  39. Z. Bar-Joseph, G. Gerber, D. K. Gifford, T. S. Jaakkola, and I. Simon, “A new approach to analyzing gene expression time series data,” in Proceedings of the 6th Annual International Conference on Computational Biology (RECOMB '02), pp. 39–48, ACM, April 2002. View at Scopus
  40. S. Kimura, K. Ide, A. Kashihara et al., “Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm,” Bioinformatics, vol. 21, no. 7, pp. 1154–1163, 2005. View at Publisher · View at Google Scholar · View at Scopus
  41. O. R. Gonzalez, C. Küper, K. Jung, P. C. Naval Jr., and E. Mendoza, “Parameter estimation using simulated annealing for S-system models of biochemical networks,” Bioinformatics, vol. 23, no. 4, pp. 480–486, 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. S. Kikuchi, D. Tominaga, M. Arita, K. Takahashi, and M. Tomita, “Dynamic modelling of genetic networks using a genetic algorithm and S-system,” Bioinformatics, vol. 19, no. 5, pp. 643–650, 2003. View at Google Scholar
  43. S.-Y. Ho, C.-H. Hsieh, F.-C. Yu, and H.-L. Huang, “An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 648–660, 2007. View at Publisher · View at Google Scholar · View at Scopus
  44. R. Xu, G. K. Venayagamoorthy, and D. C. Wunsch II, “Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization,” Neural Networks, vol. 20, no. 8, pp. 917–927, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  45. 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,” in Proceedings of the IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB '06), pp. 1–8, IEEE, Toronto, Canada, 2006.
  46. K. Kentzoglanakis and 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, 2012. View at Publisher · View at Google Scholar · View at Scopus
  47. D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997. View at Publisher · View at Google Scholar · View at Scopus
  48. Y. Maki, T. Ueda, M. Okamoto et al., “Inference of genetic network using the expression profile time course data of mouse P19 cells,” Genome Informatics, vol. 13, pp. 382–383, 2002. View at Google Scholar
  49. N. Noman and H. Iba, “Inferring gene regulatory networks using differential evolution with local search heuristics,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 4, pp. 634–647, 2007. View at Publisher · View at Google Scholar · View at Scopus
  50. Z. Bar-Joseph, G. K. Gerber, D. K. Gifford, T. S. Jaakkola, and I. Simon, “Continuous representations of time-series gene expression data,” Journal of Computational Biology, vol. 10, no. 3-4, pp. 341–356, 2003. View at Publisher · View at Google Scholar · View at Scopus
  51. M. S. Dasika, A. Gupta, C. D. Maranas, and J. D. Varner, “A mixed integer linear programming (MILP) framework for inferring time delay in gene regulatory networks,” in Pacific Symposium on Biocomputing, vol. 9, pp. 474–485, 2003.
  52. E. P. van Someren, L. F. A. Wessels, M. J. T. Reinders, and E. Backer, “Robust genetic network modeling by adding noisy data,” in Proceedings of the IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, 2001.
  53. C. Spieth, F. Streichert, N. Speer, and A. Zell, “Optimizing topology and parameters of gene regulatory network models from time-series experiments,” in Genetic and Evolutionary Computation—GECCO 2004, pp. 461–470, Springer, Berlin, Germany, 2004. View at Google Scholar
  54. E. Keedwell and A. Narayanan, “Discovering gene networks with a neural-genetic hybrid,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 2, no. 3, pp. 231–242, 2005. View at Publisher · View at Google Scholar · View at Scopus
  55. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43, Nagoya, Japan, October 1995. View at Publisher · View at Google Scholar
  56. A. Banks, J. Vincent, and C. Anyakoha, “A review of particle swarm optimization. Part I: background and development,” Natural Computing, vol. 6, no. 4, pp. 467–484, 2007. View at Publisher · View at Google Scholar
  57. A. Banks, J. Vincent, and C. Anyakoha, “A review of particle swarm optimization. Part II: hybridisation, combinatorial, multi-criteria and constrained optimization, and indicative application,” Natural Computing, vol. 7, no. 1, pp. 109–124, 2008. View at Publisher · View at Google Scholar
  58. E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among five evolutionary-based optimization algorithms,” Advanced Engineering Informatics, vol. 19, no. 1, pp. 43–53, 2005. View at Publisher · View at Google Scholar · View at Scopus
  59. X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), vol. 284 of Studies in Computational Intelligence, pp. 65–74, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  60. A. Alihodzic and M. Tuba, “Improved bat algorithm applied to multilevel image thresholding,” The Scientific World Journal, vol. 2014, Article ID 176718, 16 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  61. N. Noman and H. Iba, “Reverse engineering genetic networks using evolutionary computation,” Genome Informatics Series, vol. 16, no. 2, pp. 205–214, 2005. View at Google Scholar
  62. L. Palafox, N. Noman, and H. Iba, “Reverse engineering of gene regulatory networks using dissipative particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 4, pp. 577–587, 2013. View at Publisher · View at Google Scholar · View at Scopus
  63. T. Schaffter, D. Marbach, and D. Floreano, “GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods,” Bioinformatics, vol. 27, no. 16, pp. 2263–2270, 2011. View at Publisher · View at Google Scholar · View at Scopus
  64. 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,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 16, pp. 10555–10560, 2002. View at Publisher · View at Google Scholar · View at Scopus
  65. A. Greenfield, A. Madar, H. Ostrer, and R. Bonneau, “DREAM4: combining genetic and dynamic information to identify biological networks and dynamical models,” PLoS ONE, vol. 5, no. 10, Article ID e13397, 2010. View at Publisher · View at Google Scholar · View at Scopus