INFERENCE OF GENE REGULATORY NETWORKS BY EXTENDED KALMAN FILTERING USING GENE EXPRESSION TIME SERIES DATA

Ramouna Fouladi, Emad Fatemizadeh, S. Shahriar Arab

2012

Abstract

In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model’s parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions’ true values. Through the extended Kalman filtering process, these coefficients are updated in such a way that the predicted gene expressions follow the ones observed. Finally, connections between each two genes are inferred based on these coefficients.

References

  1. Bansal, M., Della Gatta, G. and DI Bernardo, D., 2006. Inference of Gene Regulatory Networks and Compound Mode of Action from Time Course Gene Expression Profiles. Bioinformatics, vol. 22, no. 7, 815-822.
  2. Cantone, L., Marucci, L., Lorio, F., Ricci, M., Belcastro, V., Bansal, M., Santini, S., DI Bernardo, M., DI Bernardo, D. and Cosma, M., 2009. A Yeast Synthetic Network for In Vivo Assesment of ReverseEngineering and Modeling Approaches. Cell, 137, 172-181.
  3. Chen, T. and Aihara, K. Year. Modeling Gene Expression with Differential Equations. In: proc. pacific symp. Biocomputing, 1999. 29-40.
  4. Cook, D. L., Gerber, A. N. and Tapscott, S. J. Year. Modeling Stochastic Gene Expression: Implications for Haploinsufficiency. In: Proc. Nat'l Academy of Science, USA, 1998. 15641-15646.
  5. D'haeseleer, P., Wen, X., Fuhrman, S. and SOMOGYI, R. Year. "Linear Modeling of mRNA Expression Levels during CNS Development and Injury,". In: Proc. Pacific Symp. Biocomputing, 1999. 41-52.
  6. Gardner, T. 2003. Inferring genetic networks and identifying compound mode of action via expression profiling. Science, vol. 301, 102-105.
  7. Ghahramani, Z. 1998. Learning Dynamic Bayesian Networks. Adaptive Processing of Sequences and Data Structures, Springer-Verlag, 168-197.
  8. Holter, N. S., Maritanm, A., Cieplak, M., Fedoroff, N. V. and Banavar, J. R. 2001. "Dynamic Modeling of Gene Expression Data,". Proc. Nat'l Academy of Science. USA.
  9. Liu, T., Sung, W. and Mittal, A. 2006. Model Gene Network by Semi-Fixed Bayesian Network. Expert Systems with Applications, vol. 30, no.1, 42-49.
  10. Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R. and Califano, A. 2006. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7 Suppl 1, S7.
  11. Murphy, K. and Mian, S. 1999. Modeling Gene Expression Data Using Dynamic Bayesian Networks. technical report, Univ. of California.
  12. Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Sotheran, E. A., Gaiba, A., Wild, D. L. and Falciani, F. 2004. Modeling T-Cell Activation Using Gene Expression Profiling and State Space Models. Bioinformatics, vol. 20, no. 9, 1361-1372.
  13. Ronen, M., Rosenberg, R., Shraiman, B. and Alon, U. Year. Assigning Numbers to the Arrows: Parameterizing a Gene Regulation Network by Using Accurate Expression Kinetics. In: Proc Nat'l Academy Science, USA, 2002. 10555-10560.
  14. Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botsein, D. and B, F. 1998. Comprehensive Identification of Cell Cycleregulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell, vol. 9, no. 12, 3273-3297.
  15. Tian, T. and Burrage, K. Year. Stochastic Neural Network Models for Gene Regulatory Networks. In: Proc. 2003 IEEE Congress Evolutionary Computation, 2003. 162- 169.
  16. Wang, Z., Gao, H., Cao, J. and Liu, X., 2008a. “On Delayed Genetic Regulatory Networks with Polytopic Uncertainties: Robust Stability Analysis,”. IEEE Trans. NanoBioscience, vol. 7, no. 2, 154-163.
  17. Wang, Z., Liu, X., Liang, J. and Vinciotti, V., 2009. An Extended Kalman Filtering Approach to Modeling Nonlinear Dynamic Gene Regulatory Networks via Short Gene Expression Time Series. IEEE/ACM Transactions on Computational Biology and BioinformaticS, vol. 6, no. 3, 410-419.
  18. Wang, Z., Yang, F., Ho, D. W. C., Swift, S., Tucker, A. and Liu, X. 2008b. Stochastic Dynamic Modeling of Short Gene Expression Time Series Data. IEEE Trans. NanoBioscience, vol. 7, no. 1, 44-55.
  19. Wu, F., Zhang, W. and Kusalik, A. J. Year. Modeling Gene Expression from Microarray Expression Data with State-Space Equations. In: Proc. Pacific Symp. Biocomputing, 2004. 581-592.
  20. Yu, J., Smith, V., Wang, P. and Hartemink, A. 2004. Advances to Bayesian Network Inference for Generating Causal Networks from Observational Biological Data. Bioinformatics, vol. 20, no. 18, 3594- 3603.
  21. Zoppoli, P., Morganella, S. and Ceccarelli, M. 2010. TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics, 11, 154.
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Paper Citation


in Harvard Style

Fouladi R., Fatemizadeh E. and Shahriar Arab S. (2012). INFERENCE OF GENE REGULATORY NETWORKS BY EXTENDED KALMAN FILTERING USING GENE EXPRESSION TIME SERIES DATA . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012) ISBN 978-989-8425-90-4, pages 150-155. DOI: 10.5220/0003754801500155


in Bibtex Style

@conference{bioinformatics12,
author={Ramouna Fouladi and Emad Fatemizadeh and S. Shahriar Arab},
title={INFERENCE OF GENE REGULATORY NETWORKS BY EXTENDED KALMAN FILTERING USING GENE EXPRESSION TIME SERIES DATA},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)},
year={2012},
pages={150-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003754801500155},
isbn={978-989-8425-90-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)
TI - INFERENCE OF GENE REGULATORY NETWORKS BY EXTENDED KALMAN FILTERING USING GENE EXPRESSION TIME SERIES DATA
SN - 978-989-8425-90-4
AU - Fouladi R.
AU - Fatemizadeh E.
AU - Shahriar Arab S.
PY - 2012
SP - 150
EP - 155
DO - 10.5220/0003754801500155