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Parallel Computing for Gene Networks Reverse Engineering

  • Jaroslaw ZolaEmail author
Chapter

Abstract

Gene networks provide a mathematical representation of gene interactions that govern biological processes in every living organism. Given a gene expression data, the goal of network inference is to reconstruct the underlying regulatory network. The problem is challenging owing to the convoluted nature of biological interactions and imperfection of experimental data. In many cases, the resulting computational models are too complex to execute on a sequential computer and require scalable parallel approaches. In this chapter, we describe network inference methods based on information theory and show a parallel algorithm that enables whole-genome networks reconstruction.

Keywords

Mutual Information Gene Regulatory Network Reverse Engineering Message Passing Interface Inference Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aluru M, Zola J, Nettleton D et al (2013) Reverse engineering and analysis of large genome-scale gene networks. Nucl Acids Res 41(1):e24CrossRefGoogle Scholar
  2. 2.
    Basso K, Margolin A, Stolovitzky G et al (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37(4):382–390CrossRefGoogle Scholar
  3. 3.
    Butte AJ, Kohane IS (1999) Unsupervised knowledge discovery in medical databases using relevance networks. In: Proceedings of the American medical informatics association symposium, Washington, DC, pp. 711–715Google Scholar
  4. 4.
    Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, HobokenzbMATHGoogle Scholar
  5. 5.
    Daub CO, Steuer R, Selbig J et al (2004) Estimating mutual information using B-spline functions – an improved similarity measure for analysing gene expression data. BMC Bioinform 5:118CrossRefGoogle Scholar
  6. 6.
    de la Fuente A, Bing N, Hoeschele I et al (2004) Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18):3565–3574Google Scholar
  7. 7.
    D’haeseleer P, Wen X, Fuhrman S et al (1998) Mining the gene expression matrix: inferring gene relationships from large scale gene expression data. In: Information processing in cells and tissues. Plenum Press, New YorkGoogle Scholar
  8. 8.
  9. 9.
    Faith JJ, Hayete B, Thaden JT et al (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8CrossRefGoogle Scholar
  10. 10.
    Friedman N, Linial M, Nachman I et al (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7:601–620CrossRefGoogle Scholar
  11. 11.
    Gregoretti F, Belcastro V, di Bernardo D et al (2010) A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. PLoS One 5(4):e10179Google Scholar
  12. 12.
    Hoops S, Sahle S, Gauges R, et al (2006) COPASI – a complex pathway simulator. Bioinformatics 22(24):3067–3074CrossRefGoogle Scholar
  13. 13.
    Kraskov A, Stogbauer H, Grassberger P (2004) Estimating mutual information. Phys Rev E 69(6 Pt 2):066138CrossRefMathSciNetGoogle Scholar
  14. 14.
    Long J, Roth M (2008) Synthetic microarray data generation with RANGE and NEMO. Bioinformatics 24(1):132–134CrossRefGoogle Scholar
  15. 15.
    Marbach D, Prill RJ, Schaffter T et al (2010) Revealing strengths and weaknesses of methods for gene network inference. PNAS 107(14):6286–6291CrossRefGoogle Scholar
  16. 16.
    NASC European Arabidopsis Stock Centre. http://www.arabidopsis.info/
  17. 17.
    NCBI Gene Expression Omnibus. http://www.ncbi.nlm.nih.gov/geo/
  18. 18.
    Nikolova O, Zola J, Aluru S (2013) Parallel globally optimal structure learning of Bayesian networks. J Parallel Distrib Comput 73(8):1039–1048. ISSN 0743-7315, http://dx.doi.org/10.1016/j.jpdc.2013.04.001 Google Scholar
  19. 19.
    Schafer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6):754–764CrossRefGoogle Scholar
  20. 20.
    Schaffter T, Marbach D, Floreano D (2011) GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27(16):2263–2270CrossRefGoogle Scholar
  21. 21.
    Shi H, Schmidt B, Liu W et al (2011) Parallel mutual information estimation for inferring gene regulatory networks on GPUs. BMC Res Notes 4:189Google Scholar
  22. 22.
  23. 23.
    van den Bulcke T, Van Leemput K, Naudts B et al (2006) SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinform 7:43CrossRefGoogle Scholar
  24. 24.
    Yu H, Smith A, Wang P et al (2002) Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. In: Proceedings of the international conference on systems biology, EdmontonGoogle Scholar
  25. 25.
    Zola J, Aluru M, Sarje A et al (2010) Parallel information-theory-based construction of genome-wide gene regulatory networks. IEEE Trans Parall Distrib Syst 21(12):1721–1733CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Rutgers Discovery Informatics InstituteRutgers UniversityPiscatawayUSA

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