Advertisement

Cognitive Neurodynamics

, Volume 8, Issue 3, pp 251–259 | Cite as

Evaluating influence of microRNA in reconstructing gene regulatory networks

  • Ahsan Raja ChowdhuryEmail author
  • Madhu Chetty
  • Nguyen Xuan Vinh
Brief Communication

Abstract

Gene regulatory network (GRN) consists of interactions between transcription factors (TFs) and target genes (TGs). Recently, it has been observed that micro RNAs (miRNAs) play a significant part in genetic interactions. However, current microarray technologies do not capture miRNA expression levels. To overcome this, we propose a new technique to reverse engineer GRN from the available partial microarray data which contains expression levels of TFs and TGs only. Using S-System model, the approach is adapted to cope with the unavailability of information about the expression levels of miRNAs. The versatile Differential Evolutionary algorithm is used for optimization and parameter estimation. Experimental studies on four in silico networks, and a real network of Saccharomyces cerevisiae called IRMA network, show significant improvement compared to traditional S-System approach.

Keywords

Gene regulatory network Microarray microRNA 

Notes

Acknowledgments

This work is supported in part by NICTA (National Information and Communication Technology Australia) research in Systems Biology flagship program.

References

  1. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A (2005) Reverse engineering of regulatory networks in human b cells. Nat Genet 37(4):382–390. doi: 10.1038/ng1532 Google Scholar
  2. Butte AJ, Kohane IS (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 5:415–426Google Scholar
  3. Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V, Bansal M, Santini S, di Bernardo M, di Bernardo D, Cosma MP (2009) A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137:172–181PubMedCrossRefGoogle Scholar
  4. Chen K, Rajapaksy N (2007) The evolution of gene regulation by transcription factor and micrornas. Nat Rev Genet 8:93–103PubMedCrossRefGoogle Scholar
  5. Chowdhury AR, Chetty M, Vinh NX (2012) Adaptive regulatory genes cardinality for reconstructing genetic networks. In: IEEE congress on evolutionary computation (IEEE CEC), pp 1–8Google Scholar
  6. Della GG, Bansal M, Ambesi-Impiombato A, Antonini D, Missero C, di Bernardo D (2008) Direct targets of the trp63 transcription factor revealed by a combination of gene expression profiling and reverse engineering. Genome Res 18:939–948CrossRefGoogle Scholar
  7. Gardner TS, di Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629):102–105PubMedCrossRefGoogle Scholar
  8. Guelzim N, Bottani S, Bourgine P, Kepes F (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nat Genet 31(1):60–63. doi: 10.1038/ng873 Google Scholar
  9. He W, Cao J (2008) Robust stability of genetic regulatory networks with distributed delay. Cogn Neurodyn 2(4):355–361PubMedCentralPubMedCrossRefGoogle Scholar
  10. He L, Hannon JG (2004) Micrornas:small rnas with a big role in gene regulation. Nat Rev Genet 5:522–532PubMedCrossRefGoogle Scholar
  11. Kikuchi S, Tominaga D, Arita M, Takahashi K, Tomita M (2003) Dynamic modeling of genetic networks using genetic algorithm and s-system. Bioinformatics 19(5):643–650PubMedCrossRefGoogle Scholar
  12. Kimura S, Ide K, Kashihara A, Kano M, Hatakeyama M, Masui R, Nakagawa N, Yokoyama S, Kuramitsu S, Konagaya A (2005) Inference of s-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 21(7):1154–1163PubMedCrossRefGoogle Scholar
  13. Luo Q, Zhang R, Liao X (2010) Unconditional global exponential stability in lagrange sense of genetic regulatory networks with sum regulatory logic. Cogn Neurodyn 4(3):251–261PubMedCentralPubMedCrossRefGoogle Scholar
  14. Margolin A, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera R, Califano A (2006) Aracne: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(Suppl 1):S7PubMedCentralPubMedCrossRefGoogle Scholar
  15. Noman N, Iba H (2006) On the reconstruction of gene regulatory networks from noisy expression profiles. In: IEEE congress on evolutionary computation (IEEE CEC), pp 2543–2550Google Scholar
  16. Noman N, Iba H (2007) Inferring gene regulatory networks using differential evolution with local search heuristics. IEEE Trans Comput Biol Bioinformatics 4:634–647CrossRefGoogle Scholar
  17. Savageau M (1976) Biochemical systems analysis. A study of function and design in molecular biology. Addison-Wesley Publishing Company, MassachusettsGoogle Scholar
  18. Sheridan P, Kamimura T, Shimodaira H (2010) A scale-free structure prior for graphical models with applications in functional genomics. PLoS ONE 5(11):e13580, 11Google Scholar
  19. Shyu A-B, Wilkinson MF, van Hoof A (2008) Messenger rna regulation: to translate or to degrade. EMBO J 27(3):471–481. doi: 10.1038/sj.emboj.7601977 Google Scholar
  20. Storn R, Price KV (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optimization 11:341–359CrossRefGoogle Scholar
  21. Voit EO, Almeida J (2004) Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 20:1670–1681PubMedCrossRefGoogle Scholar
  22. Wang Z, Liu G, Sun Y, Wu H (2009) Robust stability of stochastic delayed genetic regulatory networks. Cogn Neurodyn 3(3):271–280PubMedCentralPubMedCrossRefGoogle Scholar
  23. Ye Q, Cui B (2010) Mean square exponential and robust stability of stochastic discrete-time genetic regulatory networks with uncertainties. Cogn Neurodyn 4(2):165–176PubMedCentralPubMedCrossRefGoogle Scholar
  24. Yu J, Smith VA, Wang PP, Hartemink AJ, Jarvis ED (2004) Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20:3594–3603PubMedCrossRefGoogle Scholar
  25. Zoppoli P, Morganella S, Ceccarelli M (2010) Timedelay-aracne: reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 11(1):154PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Ahsan Raja Chowdhury
    • 1
    • 2
    Email author
  • Madhu Chetty
    • 1
    • 2
  • Nguyen Xuan Vinh
    • 1
  1. 1.Gippsland School of Information TechnologyMonash UniversityVictoriaAustralia
  2. 2.National ICT Australia (NICTA)VRLMelbourneAustralia

Personalised recommendations