Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Construction of Gene Interaction Networks from Gene Expression Data Based on Evolutionary Computation

진화연산에 기반한 유전자 발현 데이터로부터의 유전자 상호작용 네트워크 구성

  • 정성훈 (한성대학교 정보공학부) ;
  • 조광현 (서울대학교 의과대학 의학과 및 서울대학교 생명공학연구원)
  • Published : 2004.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper investigates construction of gene (interaction) networks from gene expression time-series data based on evolutionary computation. To illustrate the proposed approach in a comprehensive way, we first assume an artificial gene network and then compare it with the reconstructed network from the gene expression time-series data generated by the artificial network. Next, we employ real gene expression time-series data (Spellman's yeast data) to construct a gene network by applying the proposed approach. From these experiments, we find that the proposed approach can be used as a useful tool for discovering the structure of a gene network as well as the corresponding relations among genes. The constructed gene network can further provide biologists with information to generate/test new hypotheses and ultimately to unravel the gene functions.

Keywords

References

  1. J. Reinitz and D. H. Sharp, 'Mechanism of eve stripe formation,' Mechanisms of Development, no. 49, pp. 133-158, 1995 https://doi.org/10.1016/0925-4773(94)00310-J
  2. M. B. Eisen, P. T. Spellman, P. O. Brown, and D.~Botstein, 'Cluster analysis and display of genome-wide expression patterns,' in Proceedings of the National Academy of Sciences, pp. 14863-14868, Dec. 1998
  3. T. Akutsu, S. Miyano, and S. Kuhara, 'Identification of genetic networks from a small number of gene expression patterns under the Boolean network model,' in Pacific Symposium on Biocomputing, pp. 17-28, Jan. 1999
  4. D. Weaver, C. Workman, and G. Stormo, 'Modeling regulatory networks with weight matrices,' in Pacific Symposium on Biocomputing, pp. 112-123, Jan. 1999
  5. P. D'haeseleer, X. Wen, S. Fuhrman, and R. Somogyi, 'Linear modelling of mRNA expression levels during CNS development and injury,' in Pacific Symposium on Biocomputing, pp. 41-52, Jan. 1999
  6. N. Friedman, M. Linial, I. Nachman, and D. Pe'er, 'Using bayesian networks to analyze expression data,' Journal of Computational Biology, no. 7, pp. 601-620, 2000 https://doi.org/10.1089/106652700750050961
  7. P. D'haeseleer, 'Reconstructing gene networks from large scale gene expression data'. PhD thesis, University of New Mexico, Albuquerque, 2000
  8. M. Wahde and J. Hertz, 'Coarse-grained reverse engineering of genetic regulatory networks,' BioSystems, no. 55, pp. 129-136, 2000 https://doi.org/10.1016/S0303-2647(99)00090-8
  9. M. Wahde and J. Hertz, 'Modeling genetic regulatory dynamics in neural development,' Journal of Computational Biology, vol. 8, pp. 429-442, Sep 2001 https://doi.org/10.1089/106652701752236223
  10. E. van Someren, L. Wessels, and M. Reinders, 'Linear modelling of genetic networks from experimental data,' in Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 355-366, Aug. 2000
  11. E. van Someren, L. Wessels, and M. Reinders, 'Genetic network models: A comparative study,' in Proceedings of SPIE, Micro-arrays: Optical Technologies and Informatics, pp. 236-247, Jan. 2001
  12. L. Wessels, E. van Someren, and M. Reinders, 'A comparison of genetic network models,' in Pacific Symposium on Biocomputing, pp. 508-519, Jan. 2001
  13. M. Wahde and J. Hertz, 'Modeling genetic regulatory dynamics in neural development,' Journal of Computational Biology, vol. 8, pp. 429-442, Sep 2001 https://doi.org/10.1089/106652701752236223
  14. M. Takane, 'Inference of Gene Regulatory Networks from Large Scale Gene Expression Data,' Master's thesis, McGill University, 2003
  15. T. Chen and G. C. H. He, 'Modeling gene expression with differential equations,' in Pacific Symposium on Biocomputing, pp. 29-40, Jan. 1999
  16. J. Holland, Adaptation in natural and artificial systems. 1st ed.: University of Michigan Press, Ann Arbor; 2nd ed.: 1992, MIT Press, Cambridge, 1975
  17. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989
  18. L. Davis, Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991. L. Davis (Ed.)
  19. D. B. Fogel, 'An Introduction to Simulated Evolutionary Optimization,' IEEE Trans. on Neural Networks, vol. 5, pp. 3-14, Jan. 1994 https://doi.org/10.1109/72.265956
  20. P. T. Spellman, G.Sherlock, M.Q. Zhang, V.R. Iyer, K.Anders, M.B. Eisen, P. O. Brown, D.Botstein, and B.Futcher, 'Comprehensive Identification of Cell Cycleregulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hibridization},' Molecular Biology of the Cell, vol. 9, pp. 3273-3297, 1998 https://doi.org/10.1091/mbc.9.12.3273