Identifying Gene Regulatory Networks from Time Series Expression Data by in silico Sampling and Screening

  • Mineo Morohashi
  • Hiroaki Kitano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1674)


In order to understand and infer a principle underlying biological phenomena, it is necessary to handle massive data of expression patterns, kinetics, and metabolism, so that plausible regulative mechanisms are revealed. The in silico sampling and screening method described in this paper automatically infers possible regulatory network structures using several gene expression profiles. In an experimental evaluation of the feasibility of using this method, each of the possible topologies of three-unit networks were tested exhaustively. After a genetic algorithm was used to identify the parameter set for topology, the plausible topologies were selected by using mutant gene expression data. The experimental results demonstrate that the method can derive a set of possible network structures that includes the correct one.


Network Topology Genetic Network Boolean Network Genetic Regulatory Network Final Candidate 
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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  1. 1.
    P.O. Brown and D. Botstein. Exploring the New World of the Genome with DNA Microarrays. Nature Genetics, 21:33–37, 1999.CrossRefGoogle Scholar
  2. 2.
    P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iyer, K. Anders, M. Eisen, P.O. Brown, D. Botstein, and B. Putcher. Comprehensive Identificatino of Cell Cycle-regulated Genes of the Yeast saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell, 9:3273–3297, 1998.Google Scholar
  3. 3.
    J.L. DeRisi, V.R. Lyer, and P.O. Brown. Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. Science, 278:680–686, 1997.CrossRefGoogle Scholar
  4. 4.
    M. Morohashi and H. Kitano. A Method to Reconstruct Genetic Networks Applied to the Development of drosphila’s Eye. Proc. of the 6th International Conference on Artificial Life, pages 72–80, 1998.Google Scholar
  5. 5.
    S. Hamahashi and H. Kitano. Simulation of drosophila Embryogenesis. Proc. of the 6th International Conference on Artificial Life, pages 151–160, 1998.Google Scholar
  6. 6.
    K. Kyoda and H. Kitano. Simulation of Genetic Interaction for drosophila Leg Formation. Proc. of Pacific Symposium on Biocomputing’99, pages 77–89, 1999.Google Scholar
  7. 7.
    H.H. McAdams and L. Shapiro. Circuit Simulation of Genetic Networks. Science, 269:650–656, 1995.CrossRefGoogle Scholar
  8. 8.
    S. Liang, S. Fuhrman, and R. Somogyi. REVEAL, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures. Proc. of Pacific Symposium on Biocomputing’99, pages 18–29, 1999.Google Scholar
  9. 9.
    T. Akutsu, S. Miyano, and S. Kuhara. Identification of Genetic Networks from a Small Number of Gene Expression Patterns under the Boolean Network Model. Proc. of Pacific Symposium on Biocomputing’99, pages 17–28, 1999.Google Scholar
  10. 10.
    G.S. Michaels, D.B. Carr, M. Askenazi, S. Fuhrman, X. Wen, and R. Somogyi. Cluster Analysis and Data Visualization of Large-scale Gene Expression Data. Proc. of Pacific Symposium on Biocomputing’98, pages 42–53, 1998.Google Scholar
  11. 11.
    P. D’haeseleer, X. Wen, S. Fuhrman, and R. Somogyi. Linear Modeling of mRNA Expression Levels During CNS Development and Injury. Proc. of Pacific Symposium on Biocomputing, pages 41–52, 1999.Google Scholar
  12. 12.
    D.C. Weaver, C.T. Workman, and G.D. Stormo. Modeling Regulatory Networks with Weight Matrices. Proc. of Pacific Symposium on Biocomputing’99, pages 112–123, 1999.Google Scholar
  13. 13.
    H. Ueda and H. Kitano. A Generalized Reaction-Diffusion Simulator for Studying Molecular Basis of Pattern Formation in Biological Systems. Proc. of the 6th International Conference on Artificial Life, pages 462–466, 1998.Google Scholar
  14. 14.
    H.H. McAdams and A. Arkin. Simulation of Prokaryotic Genetic Circuits. Annu. Rev. Biophys. Biomol. Struct., 27:199–224, 1998.CrossRefGoogle Scholar
  15. 15.
    R. Somogyi and C.A. Sniegoski. Modeling the Complexity of Genetic Networks: Understanding Multigenic and Pleiotropic Regulation. Complexity, 1(6):45–63, 1996.MathSciNetGoogle Scholar
  16. 16.
    J. Dunlap. An End in the Beginning. Science, 280:1548–1549, 1998.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Mineo Morohashi
    • 1
  • Hiroaki Kitano
    • 2
    • 2
  1. 1.Kitano Symbiotic Systems ProjectERATO, JSTTokyoJapan
  2. 2.Sony Computer Science Laboratories Inc.TokyoJapan

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