EGIA – Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach

Part of the Studies in Computational Intelligence book series (SCI, volume 549)

Abstract

Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored to date. Here, we present a novel integrative framework for different types of data that aims to enhance model inference. This is based on evolutionary computation and uses different types of knowledge to introduce a novel customised initialisation and mutation operator and complex evaluation criteria, used to distinguish between candidate models. Specifically, the algorithm uses information from (i) knockout experiments, (ii) annotations of transcription factors, (iii) binding site motifs (expressed as position weight matrices) and (iv) DNA sequence of gene promoters, to drive the algorithm towards more plausible network structures. Further, the evaluation basis is also extended to include structure information included in these additional data. This framework is applied to both synthetic and real gene expression data. Models obtained by data integration display both quantitative and qualitative improvement.

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References

  1. 1.
    Baeck, T., Fogel, D.B., Michalewicz, Z.: Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics Publishing, Bristol and Philadelphia (2000)CrossRefGoogle Scholar
  2. 2.
    Bergman, C.M., Carlson, J.W., Celniker, S.E.: Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster. Bioinformatics 21(8), 1747–1749 (2005)CrossRefGoogle Scholar
  3. 3.
    modENCODE Consortium, T.: Identification of functional elements and regulatory circuits by drosophila modencode. Science (2010)Google Scholar
  4. 4.
    Elgar, S.J., Han, J., Taylor, M.V.: Mef2 activity levels differentially affect gene expression during drosophila muscle development. Proceedings of the National Academy of Sciences of the United States of America 105(3), 918–923 (2008)CrossRefGoogle Scholar
  5. 5.
    Estrada, B., Choe, S.E., Gisselbrecht, S.S., Michaud, S., Raj, L., Busser, B.W., Halfon, M.S., Church, G.M., Michelson, A.M.: An integrated strategy for analyzing the unique developmental programs of different myoblast subtypes. PLoS Genetics 2(2), e16 (2006)Google Scholar
  6. 6.
    Fox, R.M., Hanlon, C.D., Andrew, D.J.: The CrebA/Creb3-like transcription factors are major and direct regulators of secretory capacity. The Journal of Cell Biology 191(3), 479–492 (2010)CrossRefGoogle Scholar
  7. 7.
    Heath, A., Kavraki, L.: Computational challenges in systems biology. Computer Science Review 3(1), 1–17 (2009)CrossRefGoogle Scholar
  8. 8.
    Huttenhower, C., Mutungu, K.T., Indik, N., Yang, W., Schroeder, M., Forman, J.J., Troyanskaya, O.G., Coller, H.A.: Detailing regulatory networks through large scale data integration. Bioinformatics 25(24), 3267–3274 (2009)CrossRefGoogle Scholar
  9. 9.
    Keedwell, E., Narayanan, A.: Discovering gene networks with a neural-genetic hybrid. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2(3), 231–242 (2005)CrossRefGoogle Scholar
  10. 10.
    Lee, W.P., Tzou, W.S.: Computational methods for discovering gene networks from expression data. Briefings in Bioinformatics 10(4), 408–423 (2009)Google Scholar
  11. 11.
    Liu, J., Ghanim, M., Xue, L., Brown, C.D., Iossifov, I., Angeletti, C., Hua, S., Negre, N., Ludwig, M., Stricker, T., Al-Ahmadie, H.A., Tretiakova, M., Camp, R.L., Perera-Alberto, M., Rimm, D.L., Xu, T., Rzhetsky, A., White, K.P.: Analysis of Drosophila Segmentation Network Identifies a JNK Pathway Factor Overexpressed in Kidney Cancer. Science 323(5918), 1218–1222 (2009)CrossRefGoogle Scholar
  12. 12.
    Marbach, D., Prill, R.J., Schaffter, T., Mattiussi, C., Floreano, D., Stolovitzky, G.: Revealing strengths and weaknesses of methods for gene network inference. Proceedings of the National Academy of Sciences of the United States of America 107(14), 6286–6291 (2010)CrossRefGoogle Scholar
  13. 13.
    Murali, T., Pacifico, S., Yu, J., Guest, S., Roberts, G.G., Finley, R.L.: DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila. Nucleic Acids Research 39(suppl. 1), D736–D743 (2011)Google Scholar
  14. 14.
    Ontology, G.: http://www.geneontology.org/ (accessed December 11, 2013)
  15. 15.
    Pollard, D.: Drosophila sequence specific transcription factor binding site matrices (2011), http://www.danielpollard.com/matrices.html (accessed March 2011)
  16. 16.
    Przytycka, T.M., Singh, M., Slonim, D.K.: Toward the dynamic interactome: it’s about time. Briefings in Bioinformatics 11(1), 15–29 (2010)CrossRefGoogle Scholar
  17. 17.
    Shabalin, A.A., Tjelmeland, H., Fan, C., Perou, C.M., Nobel, A.B.: Merging two gene-expression studies via cross-platform normalization. Bioinformatics 24(9), 1154–1160 (2008)CrossRefGoogle Scholar
  18. 18.
    Sîrbu, A., Ruskin, H.J., Crane, M.: Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics 11(59) (2010)Google Scholar
  19. 19.
    Sîrbu, A., Ruskin, H.J., Crane, M.: Regulatory network modelling: Correlation for structure and parameter optimisation. In: Karim, M., Lee, K., Ling, H., Maroudas, D., Sobh, T. (eds.) Proceedings of The IASTED Technology Conferences (International Conference on Computational Bioscience), Cambridge, Massachusetts (2010)Google Scholar
  20. 20.
    Toledano-Katchalski, H., Nir, R., Volohonsky, G., Volk, T.: Post-transcriptional repression of the drosophila midkine and pleiotrophin homolog miple by how is essential for correct mesoderm spreading. Development 134(19), 3473–3481 (2007)CrossRefGoogle Scholar
  21. 21.
    Tomancak, P., Beaton, A., Weiszmann, R., Kwan, E., Shu, S., Lewis, S., Richards, S., Ashburner, M., Hartenstein, V., Celniker, S., Rubin, G.: Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biology 3(12) (2002)Google Scholar
  22. 22.
    Wessels, L.F.A., Reinders, M.J.T., Backer, E.: Robust genetic network modeling by adding noisy data. In: IEEE - EURASIP Workshop on Nonlinear Signal and Image Processing (2001)Google Scholar
  23. 23.
    Xulvi-Brunet, R., Li, H.: Co-expression networks: graph properties and topological comparisons. Bioinformatics 26(2), 205–214 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alina Sîrbu
    • 1
    • 2
  • Martin Crane
    • 2
  • Heather J. Ruskin
    • 2
  1. 1.Institute for Scientific Interchange FoundationTurinItaly
  2. 2.Center for Scientific Computing and Complex Systems Modelling, School of ComputingDublin City UniversityDublinIreland

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