Skip to main content
SpringerLink
Log in

Computational Modelling Strategies for Gene Regulatory Network Reconstruction

  • Chapter
Computational Intelligence in Medical Informatics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 85))

  • 642 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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.

    Article  Google Scholar 

  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. 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. 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. 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. 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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  10. G. Fort and S. Lambert-Lacroix, “Classification using partial least squares with penalized logistic regression,” Bioinformatics, vol. 21, pp. 1104-1111, 2005 2005.

    Article  Google Scholar 

  11. P. Y. Chen and P. M. Popovich, Correlation: Parametric and Nonparametric Measures, 1st edition ed.: SAGE Publications, 2002.

    Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  15. X. Xia and Z. Xie, “AMADA: analysis of microarray data,” Bioinformatics Application Note, vol. 17, pp. 569-570, 2001.

    Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  21. G. Casella and C. P. Robert, Monte Carlo Statistical Methods: Springer, 2005.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. H. D. Jong, “Modeling and Simulation of Genetic Regulatory Systems: A Literature Review,” Journal of Computational Biology, vol. 9, pp. 67-103, 2002.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    MATH  Google Scholar 

  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. D. Husmeier, “Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks,” Bioinformatics, vol. 19, pp. 2271-2282, 2003.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. http://www.berkeley.edu/news/media/releases/2003/02/18_table.shtml

  37. http://www.union.wisc.edu/rna/newpics/bottom.jpg

  38. http://nanopedia.case.edu/image/dna.jpg

  39. http://alumni.media.mit.edu/~saul/PhD/imgs/ecoli.jpg

  40. http://www.med.monash.edu.au/assets/images/microbiology/microarray.jpg

Download references

Author information

Authors and Affiliations

  1. Monash University, VIC 3842 Churchill, Australia

    Muhammad Shoaib Sehgal (Faculty of IT), Iqbal Gondal (Faculty of IT) & Laurence Dooley (Faculty of IT)

Authors
  1. Muhammad Shoaib Sehgal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iqbal Gondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laurence Dooley
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Buffalo Neuroimaging Analysis Center The Jacobs Neurological Institute University at Buffalo, The State University of New York, 100 High Street, Buffalo, NY, 14203, USA

    Arpad Kelemen

  2. Centre for Quantifiable Quality of Service in Communication Systems (Q2S) Centre of Excellence, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  3. Biostatistics Dept. University at Buffalo, The State University of New York, 252A2 Farber Hall, 3435 Main st., Buffalo, NY, 14214, USA

    Yulan Liang

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sehgal, M.S., Gondal, I., Dooley, L. (2008). Computational Modelling Strategies for Gene Regulatory Network Reconstruction. In: Kelemen, A., Abraham, A., Liang, Y. (eds) Computational Intelligence in Medical Informatics. Studies in Computational Intelligence, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75767-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-75767-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75766-5

  • Online ISBN: 978-3-540-75767-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation