Gene Regulatory Network Construction Parallel Technique Based on Network Component Analysis

  • Dina ElsayadEmail author
  • Safwat Hamad
  • Howida A. Shedeed
  • Mohamed F. Tolba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


Construction of a gene regulatory network is a vital process for understanding the gene functions and gene influences on the other genes. Furthermore, gene regulatory network analysis is a promising method for demonstrating the topological order of genes interactions. One technique for constructing a gene regulatory network is FastNCA that is based on network component analysis methodology. Although FastNCA is widely used to construct the gene regulatory network of cancer diseases associated genes, it is a time-consuming and computational intensive technique. As a result, this paper presents an enhanced parallel implementation of FastNCA that uses distributed parallelism methodology to enhance the performance of FastNCA. Different gene datasets are used to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm outperforms FastNCA. Where the achieved speedup is up to 250 on 256 processing nodes.


Network Component Analysis Parallel computing Regulatory network Genes High-Performance Computing 


  1. 1.
    Aluru, S.: Handbook of Computational Molecular Biology. CRC Press, Boca Raton (2006)zbMATHGoogle Scholar
  2. 2.
    Bolouri, H.: Computational Modeling of Gene Regulatory Networks a Primer. World Scientific Publishing Company, Singapore (2008)CrossRefGoogle Scholar
  3. 3.
    Pirgazi, J., Khanteymoori, A.R.: A robust gene regulatory network inference method base on Kalman filter and linear regression. PLoS One 13(7), e0200094 (2018)CrossRefGoogle Scholar
  4. 4.
    Lam, K.Y., Westrick, Z.M., Muller, C.L., Christiaen, L., Bonneau, R.: Fused regression for multi-source gene regulatory network inference. PLoS Comput. Biol. 12(12), e1005157 (2016)CrossRefGoogle Scholar
  5. 5.
    Omranian, N., Eloundou-Mbebi, J.M.O., Mueller-Roeber, B., Nikoloski, Z.: Gene regulatory network inference using fused LASSO on multiple data sets. Sci. Rep. 6, 20533 (2016)CrossRefGoogle Scholar
  6. 6.
    Guerrier, S., Mili, N., Molinari, R., Orso, S., Avella-Medina, M., Ma, Y.: A predictive based regression algorithm for gene network selection. Front. Genet. 7, 97 (2016)CrossRefGoogle Scholar
  7. 7.
    Gregoretti, F., Belcastro, V., Di Bernardo, D., Oliva, G.: A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. PLoS One 5(4), e10179–e10183 (2010)CrossRefGoogle Scholar
  8. 8.
    Jostins, L., Jaeger, J.: Reverse engineering a gene network using an asynchronous parallel evolution strategy. BMC Syst. Biol. 4(1), 17–33 (2010)CrossRefGoogle Scholar
  9. 9.
    Klinger, B., Bluthgen, N.: Reverse engineering gene regulatory networks by modular response analysis-a benchmark. Essays Biochem. 62(4), 535–547 (2018)CrossRefGoogle Scholar
  10. 10.
    Perkins, M., Daniels, K.: Visualizing dynamic gene interactions to reverse engineer gene regulatory networks using topological data analysis. In: 2017 21st International Conference Information Visualisation (IV) (2017)Google Scholar
  11. 11.
    Liu, Z.-P.: Reverse engineering of genome-wide gene regulatory networks from gene expression data. Curr. Genomics 16(1), 3–22 (2015)CrossRefGoogle Scholar
  12. 12.
    de Souza, M.C., Higa, C.H.A.: Reverse engineering of gene regulatory networks combining dynamic Bayesian networks and prior biological knowledge. In: International Conference on Computational Science and Its Applications (2018)Google Scholar
  13. 13.
    Villaverde, A.F., Banga, J.R.: Reverse engineering and identification in systems biology: strategies, perspectives and challenges. J. Roy. Soc. Interface 11(91), 1–16 (2014)CrossRefGoogle Scholar
  14. 14.
    Sales, G., Romualdi, C.: parmigene—A parallel R package for mutual information estimation and gene network reconstruction. Bioinformatics 27(13), 1876–1877 (2011)CrossRefGoogle Scholar
  15. 15.
    Shi, H., Schmidt, B., Liu, W., Muller-Wittig, W.: Parallel mutual information estimation for inferring gene regulatory networks on GPUs. BMC Res. Notes 4(1), 189–194 (2011)CrossRefGoogle Scholar
  16. 16.
    Zhang, X., Zhao, X.-M., He, K., Lu, L., Cao, Y., Liu, J., Hao, J.-K., Liu, Z.-P., Chen, L.: Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics 28(1), 98–104 (2011)CrossRefGoogle Scholar
  17. 17.
    Meyer, P.E., Lafitte, F., Bontempi, G.: minet: AR/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinf. 9(1), 461 (2008)CrossRefGoogle Scholar
  18. 18.
    Lachmann, A., Giorgi, F.M., Lopez, G., Califano, A.: ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32(14), 2233–2235 (2016)CrossRefGoogle Scholar
  19. 19.
    Barman, S., Kwon, Y.-K.: A novel mutual information-based Boolean network inference method from time-series gene expression data. PLoS One 12(2), e0171097 (2017)CrossRefGoogle Scholar
  20. 20.
    Yan, X., Mehan, M.R., Huang, Y., Waterman, M.S., Yu, P.S., Zhou, X.J.: A graph-based approach to systematically reconstruct human transcriptional regulatory modules. Bioinformatics 23(13), i577–i586 (2007)CrossRefGoogle Scholar
  21. 21.
    Jiang, H., Turki, T., Zhang, S., Wang, J.T.L.: Reverse engineering gene regulatory networks using graph mining. In: International Conference on Machine Learning and Data Mining in Pattern Recognition (2018)Google Scholar
  22. 22.
    Raychaudhuri, S., Stuart, J.M., Altman, R.B.: Principal components analysis to summarize microarray experiments: application to sporulation time series. In: Pacific Symposium on Biocomputing, pp. 455–466. NIH Public Access (2000)Google Scholar
  23. 23.
    Holter, N.S., Mitra, M., Maritan, A., Cieplak, M., Banavar, J.R., Fedoroff, N.V.: Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc. Nat. Acad. Sci. 97(15), 8409–8414 (2000)CrossRefGoogle Scholar
  24. 24.
    Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Hoboken (2001)CrossRefGoogle Scholar
  25. 25.
    Aapo, H.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)CrossRefGoogle Scholar
  26. 26.
    Liebermeister, W.: Linear modes of gene expression determined by independent component analysis. Bioinformatics 18(1), 51–60 (2002)CrossRefGoogle Scholar
  27. 27.
    Liao, J.C., Boscolo, R., Yang, Y.-L., Tran, L.M., Sabatti, C., Roychowdhury, V.P.: Network component analysis: reconstruction of regulatory signals in biological systems. In: Proceedings of the National Academy of Sciences (2003)Google Scholar
  28. 28.
    Chang, C., Ding, Z., Hung, Y.S., Fung, P.C.W.: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data. Bioinformatics 24(11), 1349–1358 (2008)CrossRefGoogle Scholar
  29. 29.
    Jayavelu, N.D., Aasgaard, L.S., Bar, N.: Iterative sub-network component analysis enables reconstruction of large scale genetic networks. BMC Bioinf. 16(1), 366 (2015)CrossRefGoogle Scholar
  30. 30.
    Elsayad, D., Ali, A., Shedeed, H.A., Tolba, M.F.: PFastNCA: parallel fast network component analysis for gene regulatory network. In: International Conference on Advanced Machine Learning Technologies and Applications (2018)Google Scholar
  31. 31.
    Shi, Q., Zhang, C., Guo, W., Zeng, T., Lu, L., Jiang, Z., Wang, Z., Liu, J., Chen, L.: Local network component analysis for quantifying transcription factor activities. Methods 124, 25–35 (2017)CrossRefGoogle Scholar
  32. 32.
    Noor, A., Ahmad, A., Serpedin, E., Nounou, M., Nounou, H.: ROBNCA: robust network component analysis for recovering transcription factor activities. Bioinformatics 29(19), 2410 (2013)CrossRefGoogle Scholar
  33. 33.
    Noor, A., Ahmad, A., Serpedin, E.: SparseNCA: sparse network component analysis for recovering transcription factor activities with incomplete prior information. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(2), 387–395 (2018)CrossRefGoogle Scholar
  34. 34.
    Wei, K., Qianni, S., Shuaiqun, W.: Transcriptional regulation analysis of breast cancer based on FastNCA algorithm. Genomics Appl. Biol. 8, 78 (2018)Google Scholar
  35. 35.
    Sun, Q., Kong, W., Mou, X., Wang, S.: Transcriptional regulation analysis of Alzheimer’s disease based on FastNCA algorithm. Curr. Bioinf. 14(8), 771–782 (2019)CrossRefGoogle Scholar
  36. 36.
    Kumar, V.: Introduction to Parallel Computing. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dina Elsayad
    • 1
    Email author
  • Safwat Hamad
    • 1
  • Howida A. Shedeed
    • 1
  • Mohamed F. Tolba
    • 1
  1. 1.Faculty of Computer and Information SciencesAin Shams UniversityCairoEgypt

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