Computational Modelling Strategies for Gene Regulatory Network Reconstruction

  • Muhammad Shoaib Sehgal
  • Iqbal Gondal
  • Laurence Dooley
Part of the Studies in Computational Intelligence book series (SCI, volume 85)

Gene Regulatory Network (GRN) modelling infers genetic interactions between different genes and other cellular components to elucidate the cellular functionality. This GRN modelling has overwhelming applications in biology starting from diagnosis through to drug target identification. Several GRN modelling methods have been proposed in the literature, and it is important to study the relative merits and demerits of each method. This chapter provides a comprehensive comparative study on GRN reconstruction algorithms. The methods discussed in this chapter are diverse and vary from simple similarity based methods to state of the art hybrid and probabilistic methods. In addition, the chapter also underpins the need of strategies which should be able to model the stochastic behavior of gene regulation in the presence of limited number of samples, noisy data, multi-collinearity for high number of genes.

Keywords

Gene Regulatory Networks Deterministic Modelling Stochastic Modelling Computational Intelligence Methods for GRN Modelling 

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References

  1. 1.
    T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasen-beek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Down-ing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lan-der, “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, pp. 286(5439):531-537, 1999.CrossRefGoogle Scholar
  2. 2.
    M. S. B. Sehgal, I. Gondal, and L. Dooley, “Statistical Neural Networks and Support Vector Machine for the Classification of Genetic Mutations in Ovarian Cancer,” IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)’04, USA., pp. 140-146, 2004.Google Scholar
  3. 3.
    M. S. B. Sehgal, I. Gondal, and L. Dooley, “Missing Values Imputation for DNA Microarray Data using Ranked Covariance Vectors,” The International Journal of Hybrid Intelligent Systems (IJHIS), vol. ISSN 1448-5869, 2005.Google Scholar
  4. 4.
    S. Dudoit, J. Fridlyand, and T. P. Speed, “Comparison of discrimination methods for the classification of tumors using gene expression data,” Journal of the American Statistical Association, pp. 77-78, 2002.Google Scholar
  5. 5.
    M. S. B. Sehgal, I. Gondal, and L. Dooley, “Collateral Missing Value Estimation: Robust missing value estimation for consequent microarray data processing,” Lecture Notes in Artificial Intelligence (LNAI), Springer-Verlag, pp. 274-283, 2005.Google Scholar
  6. 6.
    J. K. Choi, U. Yu, O. J. Yoo, and S. Kim, “Differential coexpression analysis using microarray data and its application to human cancer,” Bioinformatics, vol. 21, pp. 4348-4355, December 15, 2005 2005.CrossRefGoogle Scholar
  7. 7.
    M. S. B. Sehgal, I. Gondal, L. Dooley, and R. Coppel, “AFEGRN- Adaptive Fuzzy Evolutionary Gene Regulatory Network Reconstruction Framework,” IEEE- World Congress on Computational Intelligence-FUZZ-IEEE, pp. 1737-1741, 2006 2006.Google Scholar
  8. 8.
    I. Farkas, C. Wu, C. Chennubhotla, I. Bahar, and Z. Oltvai, “Topological basis of signal integration in the transcriptional-regulatory network of the yeast, Saccharomyces cerevisiae,” BMC Bioinformatics, vol. 7, p. 478, 2006.CrossRefGoogle Scholar
  9. 9.
    A. V. Werhli, M. Grzegorczyk, and D. Husmeier, “Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks 10.1093/bioinformatics/btl391,” Bioinformatics, vol. 22, pp. 2523-2531, October 15, 2006 2006.CrossRefGoogle Scholar
  10. 10.
    G. Fort and S. Lambert-Lacroix, “Classification using partial least squares with penalized logistic regression,” Bioinformatics, vol. 21, pp. 1104-1111, 2005 2005.CrossRefGoogle Scholar
  11. 11.
    P. Y. Chen and P. M. Popovich, Correlation: Parametric and Nonparametric Measures, 1st edition ed.: SAGE Publications, 2002.Google Scholar
  12. 12.
    R. Steuer, J. Kurths, C. O. Daub, J. Weise, and J. Selbig, “The mutual information: Detecting and evaluating dependencies between variables 10.1093/bioinformatics/18.suppl_2.S231,” Bioinformatics, vol. 18, pp. S231-240, October 1, 2002 2002.Google Scholar
  13. 13.
    J. M. Stuart, E. Segal, D. Koller, and S. K. Kim, “A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules 10.1126/ science.1087447,” Science, vol. 302, pp.249-255, October 10, 2003 2003.CrossRefGoogle Scholar
  14. 14.
    G. Yona, W. Dirks, S. Rahman, and D. M. Lin, “Effective similarity measures for expression profiles 10.1093/bioinformatics/btl127,” Bioinformatics, vol. 22, pp. 1616-1622, July 1, 2006 2006.CrossRefGoogle Scholar
  15. 15.
    X. Xia and Z. Xie, “AMADA: analysis of microarray data,” Bioinformatics Application Note, vol. 17, pp. 569-570, 2001.Google Scholar
  16. 16.
    M. S. B. Sehgal, I. Gondal, L. Dooley, and R. Coppel, “AFEGRN: Adaptive Fuzzy Evolutionary Gene Regulatory Network Re-construction Framework,” World Congress on Computational Intelligence: Fuzzy Systems., 2006.Google Scholar
  17. 17.
    X. J. Zhou, Ming-Chih, J. Kao, H. Huang, A. Wong, J. Nunez-Iglesias, M. Primig, O. M. Aparicio, C. E. Finch, T. E. Morgan, and W. H. Wong, “Functional annotation and network reconstruction through cross-platform integration of microarray data,” Nature Biotechnology, vol. 23, pp. 238-243, 2005.Google Scholar
  18. 18.
    L. J. Heyer, S. Kruglyak, and S. Yooseph, “Exploring Expression Data: Identification and Analysis of Coexpressed Genes 10.1101/gr.9.11.1106,” Genome Res., vol. 9, pp. 1106-1115, November 1, 1999 1999.CrossRefGoogle Scholar
  19. 19.
    O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, and R. Altman, “Missing Value Estimation Methods for DNA Microarrays,” Bioinformatics, vol. 17, pp. 520-525, 2001.CrossRefGoogle Scholar
  20. 20.
    W. Zhao, E. Serpedin, and E. R. Dougherty, “Inferring gene regulatory networks from time series data using the minimum description length principle,” Bioinformatics, vol. 22(17), pp. 2129-2135, 2006.CrossRefGoogle Scholar
  21. 21.
    G. Casella and C. P. Robert, Monte Carlo Statistical Methods: Springer, 2005.Google Scholar
  22. 22.
    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, pp. 382-390, 2005.CrossRefGoogle Scholar
  23. 23.
    R. Balasubramaniyan, E. Hullermeier, N. Weskamp, and J. Kamper, “Clustering of gene expression data using a local shape-based similarity measure 10.1093/bioinformatics/bti095,” Bioinformatics, vol. 21, pp. 1069-1077, April 1, 2005 2005.CrossRefGoogle Scholar
  24. 24.
    A. T. Kwon, H. H. Hoos, and R. Ng, “Inference of transcriptional regulation relationships from gene expression data 10.1093/bioinformatics/btg106,” Bioinformatics, vol. 19, pp. 905-912, May 22, 2003 2003.CrossRefGoogle Scholar
  25. 25.
    J. Qian, M. Dolled-Filhart, J. Lin, H. Yu, and M. Gerstein, “Beyond Synexpression Relationships: Local Clustering of Time-shifted and Inverted Gene Expression Profiles Identifies New, Biologically Relevant Interactions,” J. Mol. Biol., pp. 1053-1066, 2001.Google Scholar
  26. 26.
    H. D. Jong, “Modeling and Simulation of Genetic Regulatory Systems: A Literature Review,” Journal of Computational Biology, vol. 9, pp. 67-103, 2002.CrossRefGoogle Scholar
  27. 27.
    T. Chen, “Modeling Gene Expression With Differential Equations,” Pacific Symposium in Bioinformatics (PSB), World Scientific, vol. 4, pp. 29-40, 1999.Google Scholar
  28. 28.
    S. Bulashevska and R. Eils, “Inferring genetic regulatory logic from expression data 10.1093/bioinformatics/bti388,” Bioinformatics, p. bti388, March 22, 2005 2005.Google Scholar
  29. 29.
    L. Mao and H. Resat, “Probabilistic representation of gene regulatory networks 10.1093/bioinformatics/bth236,” Bioinformatics, vol. 20, pp. 2258-2269, September 22, 2004 2004.CrossRefGoogle Scholar
  30. 30.
    X.-w. Chen, G. Anantha, and X. Wang, “An effective structure learning method for constructing gene networks 10.1093/bioinformatics/btl090,” Bioinformatics, vol. 22, pp. 1367-1374, June 1, 2006 2006.CrossRefGoogle Scholar
  31. 31.
    G. F. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data,” Machine Learning, vol. 9, pp. 309-347 1992.MATHGoogle Scholar
  32. 32.
    J. Suzuki, “A construction of Bayesian networks from databases based on an MDL scheme,” Ninth Conference on Uncertainty in Artificial Intelligence, pp. 266-273, 1993.Google Scholar
  33. 33.
    D. Husmeier, “Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks,” Bioinformatics, vol. 19, pp. 2271-2282, 2003.CrossRefGoogle Scholar
  34. 34.
    P. Du, J. Gong, E. S. Wurtele, and J. A. Dickerson, “Modeling gene expression networks using fuzzy logic,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 35, pp. 1351-1359, 2005.CrossRefGoogle Scholar
  35. 35.
    J. Tuikkala, L. Elo, O. S. Nevalainen, and T. Aittokallio, “Improving missing value estimation in microarray data with gene ontology 10.1093/bioinformatics/btk019,” Bioinformatics, p. btk019, December 23, 2005 2005.Google Scholar
  36. 36.
  37. 37.
  38. 38.
  39. 39.
  40. 40.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Muhammad Shoaib Sehgal
    • 1
  • Iqbal Gondal
    • 1
  • Laurence Dooley
    • 1
  1. 1.Monash UniversityAustralia

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