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Learning Genetic Regulatory Network Connectivity from Time Series Data

  • Nathan Barker
  • Chris Myers
  • Hiroyuki Kuwahara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)

Abstract

Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This paper proposes an efficient method to generate the genetic regulatory network inferred from time series data. Our method first encodes the data into levels. Next, it determines the set of potential parents for each gene based upon the probability of the gene’s expression increasing. After a subset of potential parents are selected, it determines if any genes in this set may have a combined effect. Finally, the potential sets of parents are competed against each other to determine the final set of parents. The result is a directed graph representation of the genetic network’s repression and activation connections. Our results on synthetic data generated from models for several genetic networks with tight feedback are promising.

Keywords

Time Series Data Genetic Network Potential Parent Dynamic Bayesian Network System Biology Markup Language 
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.

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References

  1. 1.
    Brown, P.A., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nature Genet. 21, 33–37 (1999)CrossRefGoogle Scholar
  2. 2.
    Eisen, M.B., Spellman, P.T., Browndagger, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)CrossRefGoogle Scholar
  3. 3.
    Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In: Pacific Symposium on Biocomputing, vol. 3, pp. 18–29 (1998)Google Scholar
  4. 4.
    Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the boolean network model (1999)Google Scholar
  5. 5.
    Ideker, T.E., Thorsson, V., Karp, R.M.: Discovery of regulatory interactions through perturbation: Inference and experimental design (2000)Google Scholar
  6. 6.
    Lähdesmäki, H., Shmulevich, I., Yli-Harja, O.: On learning gene regulatory networks under the boolean network model. Machine Learning 52, 147–167 (2003)CrossRefMATHGoogle Scholar
  7. 7.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using bayesian networks to analyze expression data. Journal of Computational Biology 7(3–4), 601–620 (2000)CrossRefGoogle Scholar
  8. 8.
    Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D.A., Nolan, G.P.: Causal protein-signaling networks derived from multiparameter single-cell data. Science 22, 523–529 (2005)CrossRefGoogle Scholar
  9. 9.
    Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D.: Advances to bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20, 3594–3603 (2004)CrossRefGoogle Scholar
  10. 10.
    Ptashne, M.: A Genetic Switch. Cell Press & Blackwell Scientific Publishing (1992)Google Scholar
  11. 11.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977)CrossRefGoogle Scholar
  12. 12.
    Guet, C.C., Elowitz, M.B., Hsing, W., Leibler, S.: Combinatorial synthesis of genetic networks. Science 296, 1466–1470 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nathan Barker
    • 1
  • Chris Myers
    • 1
  • Hiroyuki Kuwahara
    • 1
  1. 1.University of UtahSalt Lake City

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