Skip to main content

Inference of Differential Equation Models by Multi Expression Programming for Gene Regulatory Networks

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 5755)

Abstract

This paper presents an evolutionary method for identifying the gene regulatory network from the observed time series data of gene expression using a system of ordinary differential equations (ODEs) as a model of network. The structure of ODE is inferred by the Multi Expression Programming (MEP) and the ODE’s parameters are optimized by using particle swarm optimization (PSO). The proposed method can acquire the best structure of the ODE only by a small population, and also by partitioning the search space of system of ODEs can be reduced significantly. The effectiveness and accuracy of the proposed method are demonstrated by using synthesis data from the artificial genetic networks.

Keywords

  • Evolutionary method
  • multi expression programming
  • ordinary differential equations
  • particle swarm optimization
  • artificial genetic networks

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-04020-7_105
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-04020-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Qian, L.: Inference of Noisy Nonlinear Differential Equation Models for Gene Regulatory Networks using Genetic Programming and Kalman Filtering. IEEE Transactions on Signal Processing 56(7), 3327–3339 (2008)

    CrossRef  MathSciNet  Google Scholar 

  2. Akutsu, T., Miyano, S., Kuhara, S.: Identification of Genetic Networks from a Small Number of Gene Expression Patterns under the Boolean Network Model. In: Proc. of Pacific Symposium on Biocomputing, pp. 17–28 (1999)

    Google Scholar 

  3. Murphy, K., Mian, S.: Modeling Gene Expression Data using Dynamic Bayesian Network. Computer Science Division, University of California Berkeley (1999)

    Google Scholar 

  4. Chen, T., He, H.L., Church, G.M.: Modeling Gene Expression with Differential Equations. In: Proc. of Pacific Symposium on Biocomputing, pp. 29–40 (1999)

    Google Scholar 

  5. Tominaga, D., Koga, N., Okamoto, M.: Efficient Numerical Optimization Algorithm Based on Genetic Algorithm for Inverse Problem. In: Proc. of Genetic and Evolutionary Computation Conference, pp. 251–258 (2000)

    Google Scholar 

  6. Sakamoto., E., Iba, H.: Inferring a System of Differential Equations for a Gene Regulatory Network by using Genetic Programming. In: Proc. Congress on Evolutionary Computation, pp. 720–726 (2001)

    Google Scholar 

  7. Cho, D.Y., Cho, K.H., Zhang, B.T.: Identification of Biochemical Networks by S-tree Based Genetic Programming. Bioinformatics 22, 1631–1640 (2006)

    CrossRef  Google Scholar 

  8. Bongard, J., Lipson, H.: Automated Reverse Engineering of Nonlinear Dynamical Systems. Proceedings of the National Academy of Science 104, 9943–9948 (2007)

    Google Scholar 

  9. Groşan, C., Abraham, A., Han, S.-Y.: MEPIDS: Multi-expression programming for intrusion detection system. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 163–172. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  10. Andrew, H.W., et al.: System Identification using Genetic Programming. In: Proc. of 2nd Int. Conference on Adaptive Computing in Engineering Design and Control (1996)

    Google Scholar 

  11. Oltean, M., Grosan, C.: Evolving Digital Circuits using Multi Expression Programming. In: Zebulum, R., et al. (eds.) NASA/DoD Conference on Evolvable Hardware, Washington, pp. 24–26 (2004)

    Google Scholar 

  12. Chen, Y.H., Yang, B., Abraham, A.: Ajith Abraham. Flexible Neural Trees Ensemble for Stock Index Modeling. Neurocomputing. 70, 697–703 (2007)

    Google Scholar 

  13. Gennemark, P., Wedelin, D.: Efficient Algorithms for Ordinary Differential Equation Model Identification of Biological Systems. IET Syst Biol 1, 120–129 (2007)

    CrossRef  Google Scholar 

  14. Savageau, M.A.: Biochemical Systems Analysis: a Study of Function and Design in Molecular Biology. Addison-Wesley Pub. Co., Advanced Book Program, Reading (1976)

    MATH  Google Scholar 

  15. Maki, Y., Tominaga, D., Okamoto, M., Watanabe, S., Eguchi, Y.: Development of a System for the Inference of Large Scale Genetic Networks. In: Pac. Symp. Biocomput.., pp. 446–458 (2001)

    Google Scholar 

  16. Kimura, S., Ide, K., Kashihara, A.: Inference of S-system Models of Genetic Networks using a Cooperative Coevolutionary Algorithm. Bioinformatics 21, 1154–1163 (2005)

    CrossRef  Google Scholar 

  17. Kikuchi, S., et al.: Dynamic Modeling of Genetic Networks using Genetic Algorithm and S-system. Bioinformatics 19, 643–650 (2003)

    CrossRef  Google Scholar 

  18. Bornholdt, S.: Boolean Network Models of Cellular Regulation: Prospects and Limitations. J. R. Soc. Interf. 5, 85–94 (2008)

    CrossRef  Google Scholar 

  19. Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., Guthke, R.: Gene Regulatory Network Inference: Data Integration in Dynamic Models A Review. Biosystems 96, 86–103 (2009)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, B., Chen, Y., Meng, Q. (2009). Inference of Differential Equation Models by Multi Expression Programming for Gene Regulatory Networks. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_105

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04020-7_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04019-1

  • Online ISBN: 978-3-642-04020-7

  • eBook Packages: Computer ScienceComputer Science (R0)