Journal of Genetics

, Volume 89, Issue 1, pp 73–80 | Cite as

Reverse engineering large-scale genetic networks: synthetic versus real data

Research Article

Abstract

Development of microarray technology has resulted in an exponential rise in gene expression data. Linear computational methods are of great assistance in identifying molecular interactions, and elucidating the functional properties of gene networks. It overcomes the weaknesses of in vivo experiments including high cost, large noise, and unrepeatable process. In this paper, we propose an easily applied system, Stepwise Network Inference (SWNI), which integrates deterministic linear model with statistical analysis, and has been tested effectively on both simulated experiments and real gene expression data sets. The study illustrates that connections of gene networks can be significantly detected via SWNI with high confidence, when single gene perturbation experiments are performed complying with the algorithm requirements. In particular, our algorithm shows efficiency and outperforms the existing ones presented in this paper when dealing with large-scale sparse networks without any prior knowledge.

Keywords

gene regulatory network single gene perturbation linear model stepwise simulated network 

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References

  1. Albert R. and Barabasi A. L. 1999 Emergence of scaling in random networks. Science 286, 509–512.CrossRefPubMedGoogle Scholar
  2. Albert R. and Barabasi A. L. 2000 Topology of evolving networks: local events and universality. Phys. Rev. Lett. 85, 5234–5237.CrossRefPubMedGoogle Scholar
  3. Amato R., Ciaramella A., Deniskina N., Del Mondo C., di Bernardo D., Donalek C. et al. 2006 A multi-step approach to time series analysis and gene expression clustering. Bioinformatics 22, 589–596.CrossRefPubMedGoogle Scholar
  4. Basso K., Margolin A. A., Stolovitzky G., Klein U., Dalla-Favera and Califano A. 2005 Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382–390.CrossRefPubMedGoogle Scholar
  5. Beal M. J., Falciani F., Ghahramani Z., Rangel C. and Wild D. L. 2005 A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21, 349–356.CrossRefPubMedGoogle Scholar
  6. Butte A. J. and Kohane I. S. 1999 Unsupervised knowledge discovery in medical databases using relevance networks. In Fall symposium (ed. N. Lorenzi), pp. 711–715. American Medical Informatics Association. Hanley and Belfu, Washington, USA.Google Scholar
  7. Chen L. and Aihara K. 2002 Stability of genetic regulatory networks with time delay. IEEE Trans. Circuits Syst. 49, 602–608.CrossRefGoogle Scholar
  8. Chen P. C. 2004 A computational model of a class of gene networks with positive and negative controls. BioSystems 73, 13–24.CrossRefPubMedGoogle Scholar
  9. Davis J. and Goadrich M. 2006 The relationship between precisionrecall and ROC curves. In proceedings of the 23rd international conference on machine learning, pp. 233–240. ACM, New York, USA.CrossRefGoogle Scholar
  10. De Jong H. 2002 Medeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9, 67–103.CrossRefPubMedGoogle Scholar
  11. De la Fuente A., Brazhnik P. and Mendes P. 2002 Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet. 18, 295–298.CrossRefGoogle Scholar
  12. De la Fuente A., Bing N., Hoeschele I. and Mendes P. 2004 Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20, 3565–3574.CrossRefPubMedGoogle Scholar
  13. Faith J. J., Hayete B., Thaden J. T., Mogno I., Wierzbowski J. et al. 2007 Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression pro-files. PLoS Biol. 5, 54–66.CrossRefGoogle Scholar
  14. Featherstone D. E. and Broadie K. 2002 Wrestling with pleiotrophy: genomic and topological analysis of the yeast gene expression network. Bioessays 24, 267–274.CrossRefPubMedGoogle Scholar
  15. Featherstone D. E., Rushton E. and Broadie K. 2005 Developmental regulation of glutamate receptor field size by nonvesicular glutamate release. Nat. Neurosci. 5, 141–146.CrossRefGoogle Scholar
  16. Friedman N., Nachman I. and Pe’er D. 2000 Using Bayesian networks to analyze gene expression data. J. Comput. Biol. 3, 601–620.CrossRefGoogle Scholar
  17. Gardner T., di Bernardo D., Lorenz D. and Collins J. 2003 Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102–105.CrossRefPubMedGoogle Scholar
  18. Gardner T. S. and Faith J. 2005 Reverse-engineering transcription control networks. Phys. Life Rev. 2, 65–88.CrossRefGoogle Scholar
  19. Iba H. and Mimura A. 2002 Inference of a gene regulatory network by means of interactive evolutionary computing. Inform. Sci. 145, 225–236.CrossRefGoogle Scholar
  20. Kauffman S. 1974 The large scale structure and dynamics of gene control circuits: an ensemble approach. J. Theor. Biol. 44, 167–190.CrossRefPubMedGoogle Scholar
  21. Liang S., Fuhrman S. and Somogyi R. R. 1998 A general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput. 3, 18–29.Google Scholar
  22. Margolin A. A. and Califano A. 2007 Theory and limitations of genetic network inference from microarray data. Ann. N. Y. Acad. Sci. 1115, 51–72.CrossRefPubMedGoogle Scholar
  23. Mendes P., Sha W. and Ye K. 2003 Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics 19, 22–29.CrossRefGoogle Scholar
  24. Mendoza L. Thieffry D. and Alvarez-Buylla E.R. 1999 Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis. Bioinformatics 15, 593–606.CrossRefPubMedGoogle Scholar
  25. Schumacher M., Binder H. and Gerds T. 2007 Assessment of survival prediction models based on microarray data. Bioinformatics 23, 1768–1774.CrossRefPubMedGoogle Scholar
  26. Shieh G. S., Chen C., Yu C. and Huang J. 2008 Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling. BMC Bioinformatics 9, 1471–2105.CrossRefGoogle Scholar
  27. Soranzo N., Bianconi G. and Altafini C. 2007 Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics 23, 1640–1647.CrossRefPubMedGoogle Scholar
  28. Styczynski M. P. and Stephanopoulos G. 2005 Overview of computational methods for the inference of gene regulatory networks. Comp. Chem. Eng. 29, 519–534.CrossRefGoogle Scholar
  29. van Someren E. P. Wessels F. A, Backer E. and Reinders M. J. T. 2001 Robust genetic network modeling by adding noisy data. Proc. IEEE-EURASIP Workshop on nonlinear signal and image processing. Baltimore, Maryland, USA.Google Scholar
  30. Wang Y., Joshi T. and Zhang X. S., Xu D. and Chen L. 2006 Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 22, 2413–2420.CrossRefPubMedGoogle Scholar
  31. Watts D. J. and Strogatz S. H. 1998 Collective dynamics of ’smallworldâ networks. Nature 393, 440–442.CrossRefPubMedGoogle Scholar
  32. Yeung M., Tegner J. and Collins J. 2002 Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl Acad. Sci. USA 99, 6163–6168.CrossRefPubMedGoogle Scholar
  33. Yu J., Smith V. A., Wang P. P., Hartemink A. J. and Jarvis E. D. 2004 Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 3594–3603.CrossRefPubMedGoogle Scholar
  34. Zak D. E., Gonye G. E., Schwaber J. S. and Doyle F. J. 2003 Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: insights from an identifiability analysis of an in silico network. Genome Res. 13, 2396–2405.CrossRefPubMedGoogle Scholar
  35. Zomaya A. Y. 2006 Parallel computing for bioinformatics and computational biology: models, enabling technologies and case studies, 1st edition. Wiley, New Jersey, USA.Google Scholar

Copyright information

© Indian Academy of Sciences 2010

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiPeople’s Republic of China
  2. 2.Institute of Systems BiologyShanghai UniversityShanghaiPeople’s Republic of China
  3. 3.Academy of Mathematics and Systems ScienceChinese Academy of Sciences (CAS)BeijingPeople’s Republic of China

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