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A Multiobjective Phenomic Algorithm for Inference of Gene Networks

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Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2010)

Abstract

Reconstruction of gene networks has become an important activity in Systems Biology. The potential for better methods of drug discovery and of disease diagnosis hinge upon our understanding of the interaction networks between the genes. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However, all these methods are based on processing of genotypic information. We have presented an evolutionary algorithm for reconstructing gene networks from expression data using phenotypic interactions, thereby avoiding the need for an explicit objective function. Specifically, we have also extended the basic phenomic algorithm to perform multiobjective optimization for gene network reconstruction. We have applied this novel algorithm to the yeast sporulation dataset and validated it by comparing the results to the links found between genes of the yeast genome at the SGD database.

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References

  1. Schulze, A., Downward, J.: Navigating gene expression using microarrays - a technology review. Nature Cell Biology 3, E190–E195 (2001)

    Google Scholar 

  2. Soinov, L.A., Krestyaninova, M.A., Brazma, A.: Towards reconstruction of gene networks from expression data by supervised learning. Genome Biology 4(1), R6 (2003)

    Google Scholar 

  3. Bansal, M., Belcastro, V., Impiombato, A.A., di Bernardo, D.: How to infer gene networks from expression profiles. Mol. Syst. Biol. 3, 78 (2007), doi:10.1038/msb4100120

    Article  Google Scholar 

  4. D’haeseleer, P., Liang, S., Somogyi, R.: Gene expression analysis and genetic network modelling: Tutorial. In: Pacific Symposium on Biocomputing (1999)

    Google Scholar 

  5. Siegal, M.L., Promislow, D.E.L., Bergman, A.: Functional and evolutionary inference in gene networks: does topology matter? Genetica 129(1), 83–103 (2007)

    Article  Google Scholar 

  6. D’Souza, R.G.L., Chandra Sekaran, K., Kandasamy, A.: A phenomic algorithm for reconstruction of gene networks. In: IV International Conference on Computational Intelligence and Cognitive Informatics, CICI 2007, pp. 53–58. WASET, Venice (2007)

    Google Scholar 

  7. D’Souza, R.G.L., Chandra Sekaran, K., Kandasamy, A.: Reconstruction of gene networks using phenomic algorithms. Intl. Journal of Artificial Intelligence Applications (IJAIA) 1(2), 1–11 (2010), doi:10.5121/ijaia.2010.1201, ISSN: 0976-2191

    Google Scholar 

  8. Spieth, C., Streichert, F., Speer, N., Zell, A.: Optimizing Topology and Parameters of Gene Regulatory Network Models from Time-Series Experiments. In: Deb, K., Tari, Z. (eds.) GECCO 2004, Part I. LNCS, vol. 3102, pp. 461–470. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Chu, S., DeRisi, J., Eisen, M., et al.: The transcriptional program of sporulation in budding yeast. Science 282, 699–705 (1998)

    Article  Google Scholar 

  10. Somogyi, R., Fuhrman, S., Askenazi, M., Wuensche, A.: The gene expression matrix: towards the extraction of genetic network architectures. In: Proc. of Second World Cong. of Nonlinear Analysts (WCNA 1996), vol. 30(3), pp. 1815–1824 (1997)

    Google Scholar 

  11. Christley, S., Nie, Q., Xie, X.: Incorporating existing network information into gene network inference. PLoS ONE 4(8), e6799 (2009), doi:10.1371/journal.pone.0006799

    Google Scholar 

  12. Liu, B., de la Fuente, A., Hoeschele, I.: Gene network inference via structural equation modeling in genetical genomics experiments. Genetics 178, 1763–1776 (2008)

    Article  Google Scholar 

  13. Qian, L., Wang, H., Dougherty, E.R.: Inference of noisy nonlinear differential equation models for gene regulatory networks using genetic programming and Kalman filtering. IEEE Trans. on Signal Processing 56(7), 3327–3339 (2008)

    Article  MathSciNet  Google Scholar 

  14. Numata, K., Imoto, S., Miyano, S.: A structure learning algorithm for inference of gene networks from microarray gene expression data using Bayesian networks. In: Proc. of the 7th IEEE Intl. Conf. on Bioinfo. and Bioengg. 2007 (BIBE 2007), pp. 1280–1284 (2007)

    Google Scholar 

  15. Ko, Y., Zhai, C., Rodriguez-Zas, S.: Inference of gene pathways using mixture Bayesian networks. BMC Systems Biology 3, 54 (2009), doi:10.1186/1752-0509-3-54

    Article  Google Scholar 

  16. Noman, N., Iba, H.: Reverse engineering genetic networks using evolutionary computation. Genome Informatics 16(2), 205–214 (2005)

    Google Scholar 

  17. Savageau, M.A.: Power-law formalism: a canonical nonlinear approach to modelling and analysis. In: Proc. of the World Congress of Nonlinear Analysts 1992, pp. 3323–3334 (1995)

    Google Scholar 

  18. Hirose, O., Yoshida, R., Imoto, S., Yamaguchi, R., Higuchi, T., Charnock-Jones, D.S., Print, C., Miyano, S.: Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics 24(7), 932–942 (2008), doi:10.1093/bioinformatics/btm639

    Google Scholar 

  19. Dougherty, J., Tabus, I., Astola, J.: Inference of gene regulatory networks based on a universal minimum description length. EURASIP Journal on Bioinformatics and Systems Biology (2008), doi:10.1155/2008/482090

    Google Scholar 

  20. Chaitankar, V., Ghosh, P., Perkins, E.J., Gong, P., Deng, Y., Zhang, C.: A novel gene network inference algorithm using predictive minimum description length approach. BMC Syst. Biol. 4(suppl. 1) (2010), doi:10.1186/1752-0509-4-S1-S7

    Google Scholar 

  21. Kentzoglanakis, K., Poole, M.: Gene network inference using a swarm intelligence framework. In: Proc. of the 11th Annual Conf. Companion on Genetic and Evolutionary Computation Conference (GECCO 2009), pp. 2709–2712 (2009)

    Google Scholar 

  22. Xu, R., Wunsch, D.C., Frank, R.L.: Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Trans. on Computational Biology and Bioinformatics 4(4), 681–692 (2007)

    Article  Google Scholar 

  23. Zarnegar, A., Vamplew, P., Stranieri, A.: Inference of gene expression networks using memetic gene expression programming. In: Mans, B. (ed.) Proc. of the 32nd Australasian Computer Science Conf. (ACSC 2009), Conferences in Research and Practice in Information Technology (CRPIT), vol. 91 (2009)

    Google Scholar 

  24. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evolutionary Computation 8(2), 125–147 (2000)

    Article  Google Scholar 

  25. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  26. Deb, K., Reddy, A.R.: Classification of two-class cancer data reliably using evolutionary algorithms. Publ. of Kanpur Genetic Algorithms Lab., India, Report No. 2003001 (2003)

    Google Scholar 

  27. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  28. Kumar, P.K., Sharath, S., D’Souza, R.G., Chandra Sekaran, K.: Memetic NSGA—A multi-objective genetic algorithm for classification of microarray data. In: 15th Intl. Conf. on Advanced Computing and Communications, ADCOM, pp. 75–80. IEEE (2007)

    Google Scholar 

  29. Jin, Y., Sendhoff, B.: Pareto-based multiobjective machine learning: An overview and case studies. IEEE Trans. on Systems, Man, and Cybernetics 38(3), 397–415 (2008)

    Article  Google Scholar 

  30. Kupiec, M., Ayers, B., Esposito, R.E., Mitchell, A.P.: The molecular and cellular biology of the yeast Saccharomyces. Cold Spring Harbour, 889–1036 (1997)

    Google Scholar 

  31. SGD project: Saccharomyces genome database (2007), http://www.yeastgenome.org/ (September 15, 2007)

  32. Spieth, C., Streichert, F., Speer, N., Zell, A.: Multi-Objective Model Optimization for Inferring Gene Regulatory Networks. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 607–620. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  33. Spieth, C., Streichert, F., Speer, N., Zell, A.: A memetic inference method for gene regulatory networks based on s-systems. In: Proc. of Congress on Evolutionary Computation (CEC 2004), Proc. Part I, pp. 152–157. IEEE Press (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. St Joseph Engineering College, Mangalore, India

    Rio G. L. D’Souza

  2. National Institute of Technology Karnataka, Surathkal, Mangalore, India

    K. Chandra Sekaran & A. Kandasamy

Authors
  1. Rio G. L. D’Souza
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  2. K. Chandra Sekaran
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  3. A. Kandasamy
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Massachusetts, 100 Morrissey Blvd, 02125, Boston, MA, USA

    Junichi Suzuki

  2. Graduate School of Engineering, 2-1 Yamada-oka, Suita, 565-0871, Osaka, Japan

    Tadashi Nakano

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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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D’Souza, R.G.L., Sekaran, K.C., Kandasamy, A. (2012). A Multiobjective Phenomic Algorithm for Inference of Gene Networks. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_42

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  • DOI: https://doi.org/10.1007/978-3-642-32615-8_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32614-1

  • Online ISBN: 978-3-642-32615-8

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