A Methodology Based on MP Theory for Gene Expression Analysis

  • Luca Marchetti
  • Vincenzo Manca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7184)

Abstract

In this paper we develop an application of the MP theory to gene expression analysis. After introducing some general concepts about transcriptome analysis and about gene networks, we delineate a methodology for modelling such kind of networks by means of Metabolic P systems. MP systems were initially introduced as models of metabolic processes, but they can be successfully used in each context where we want to infer models of a system from a given set of time series. In the case of gene expression analysis, we found a standard way for translating MP grammars involving gene expressions into corresponding quantitative gene networks. Pre-processing methods of raw time series have been also elaborated in order to achieve a successful MP modelling of the underlying gene network.

Keywords

Gene Expression Analysis Gene Network Polynomial Model Expression Level Change Membrane Computing 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bolstad, B.M., Irizarry, R.A., Astrand, M., Speed, T.P.: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185–193 (2003)CrossRefGoogle Scholar
  2. 2.
    Bolouri, H., Davidson, E.H.: Modeling transcriptional regulatory networks. BioEssays 24(12), 1118–1129 (2002)CrossRefGoogle Scholar
  3. 3.
    Brazhnik, P., de la Fuente, A., Mendes, P.: Gene networks: how to put the function in genomics. TRENDS in Biotechnology 20(11) (2002)Google Scholar
  4. 4.
    Cao, H., Romero-Campero, F.J., Heeb, S., Cámara, M., Krasnogor, N.: Evolving cell models for systems and synthetic biology. Systems and Synthetic Biology 4(1), 55–84 (2010)CrossRefGoogle Scholar
  5. 5.
    Castellini, A., Franco, G., Pagliarini, R.: Data analysis pipeline from laboratory to MP models. Natural Computing 10(1), 55–76 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Costa, I.G., de Carvalho, F.A.T., de Souto, M.C.P.: Comparative analysis of clustering methods for gene expression time course data. Genetics and Molecular Biology 27(4), 623–631 (2004)CrossRefGoogle Scholar
  7. 7.
    De la Fuente, A., Brazhnik, P., Mendes, P.: Linking the genes: inferring quantitative gene networks from microarray data. TRENDS in Genetics 18(8) (2002)Google Scholar
  8. 8.
    Draper, N., Smith, H.: Applied Regression Analysis, 2nd edn. John Wiley & Sons, New York (1981)MATHGoogle Scholar
  9. 9.
    Fambrough, D., McClure, K., Kazlauskas, A., Lander, E.S.: Diverse signaling pathways activated by growth factor receptors induce broadly overlapping, rather than independent, sets of genes. Cell 97, 727–741 (1999)CrossRefGoogle Scholar
  10. 10.
    Gilman, A., Arkin, A.P.: Genetic “code”: Representations and dynamical models of genetic components and networks. Annual Review of Genomics and Human Genetics 3, 341–369 (2002)CrossRefGoogle Scholar
  11. 11.
    Hasty, J., McMillen, D., Isaacs, F., Collins, J.J.: Computational studies of gene regulatory networks: In numero molecular biology. Nature Review Genetics 2(4), 268–279 (2001)CrossRefGoogle Scholar
  12. 12.
    Hocking, R.R.: The Analysis and Selection of Variables in Linear Regression. Biometrics 32 (1976)Google Scholar
  13. 13.
    Ideker, T., Galitski, T., Hood, L.: A new approach to decoding life: Systems biology. Annual Review of Genomics and Human Genetics 2, 343–372 (2001)CrossRefGoogle Scholar
  14. 14.
    Jong, H.: Modeling and simulation of genetic regulatory systems: A literature review. Journal of Computational Biology 9(1), 69–105 (2002)Google Scholar
  15. 15.
    Johnson, S.C.: Hierarchical Clustering Schemes. Psychometrika 2, 241–254 (1967)CrossRefGoogle Scholar
  16. 16.
    Kitano, H.: Systems biology: A brief overview. Science 295(5560), 1662–1664 (2002)CrossRefGoogle Scholar
  17. 17.
    Kohane, I.S., Kho, A.T., Butte, A.J.: Microarrays for an Integrative Genomics. MIT Press, Cambridge (2003)Google Scholar
  18. 18.
    Lockhart, D.J., Winzeler, E.A.: Genomics, gene expression and DNA microarrays. Nature 405, 827–836 (2000)CrossRefGoogle Scholar
  19. 19.
    Manca, V.: Metabolic P systems. Scholarpedia 5(3), 9273 (2010)CrossRefGoogle Scholar
  20. 20.
    Manca, V.: Fundamentals of Metabolic P Systems. In: [28], ch. 19. Oxford University Press (2010)Google Scholar
  21. 21.
    Manca, V.: Log-Gain Principles for Metabolic P Systems. In: Condon, A., et al. (eds.) Algorithmic Bioprocesses. Natural Computing Series, ch. 28, pp. 585–605. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  22. 22.
    Manca, V., Bianco, L.: Biological networks in metabolic P systems. Biosystems 91, 489–498 (2008)CrossRefGoogle Scholar
  23. 23.
    Manca, V., Marchetti, L.: Log-Gain Stoichiometic Stepwise regression for MP systems. Int. Journal of Foundations of Computer Science 22(1), 97–106 (2011)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Manca, V., Marchetti, L.: Goldbeter’s Mitotic Oscillator Entirely Modeled by MP Systems. In: Gheorghe, M., Hinze, T., Păun, G., Rozenberg, G., Salomaa, A. (eds.) CMC 2010. LNCS, vol. 6501, pp. 273–284. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  25. 25.
    Manca, V., Marchetti, L.: Metabolic approximation of real periodical functions. The Journal of Logic and Algebraic Programming 79, 363–373 (2010)MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Manca, V., Marchetti, L., Pagliarini, R.: MP modelling of glucose-insulin interactions in the Intravenous Glucose Tolerance Test. Int. Journal of Natural Computing Research 2(3), 13–24 (2011)CrossRefGoogle Scholar
  27. 27.
    Păun, G.: Membrane Computing. An Introduction. Springer, Heidelberg (2002)CrossRefMATHGoogle Scholar
  28. 28.
    Păun, G., Rozenberg, G., Salomaa, A. (eds.): Oxford Handbook of Membrane Computing. Oxford University Press (2010)Google Scholar
  29. 29.
    Quackenbush, J.: Microarray data normalization and transformation. Nature Genetics Supplement 32 (2002)Google Scholar
  30. 30.
    Ross, D.T., et al.: Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics 24, 227–235 (2000)CrossRefGoogle Scholar
  31. 31.
    Smolen, P., Baxter, D.A., Byrne, J.H.: Modeling transcriptional control in gene networks: Methods, recent results, and future directions. Bulletin of Mathematical Biology 62(2), 247–292 (2000)CrossRefMATHGoogle Scholar
  32. 32.
    Wilhelm, B.T., Landry, J.R.: RNA-Seq–quantitative measurement of expression through massively parallel RNA-sequencing. Methods 48, 249–257 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luca Marchetti
    • 1
  • Vincenzo Manca
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly

Personalised recommendations