Abstract
The interplay of interactions between DNA, RNA and proteins leads to genetic regulatory networks (GRN) and in turn controls the gene regulation. Directly or indirectly in a cell such molecules either interact in a positive or in repressive manner therefore it is hard to obtain the accurate computational models through which the final state of a cell can be predicted with certain accuracy. This paper describes biological behaviour of actual regulatory systems and we propose a novel method for GRN discovery of a large number of genes from multiple time series gene expression observations over small and irregular time intervals. The method integrates a genetic algorithm (GA) to select a small number of genes and a Kalman filter to derive the GRN of these genes. After GRNs of smaller number of genes are obtained, these GRNs may be integrated in order to create the GRN of a larger group of genes of interest.
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References
Baeck, T., Fogel, D.B., et al.: Evolutionary Computation I and II. Advanced algorithm and operators. Institute of Physics Pub., Bristol (2000)
Bay, J.S. (ed.): Fundamentals of Linear State Space Systems. WCB/McGraw-Hill (1999)
Bolouri, H., Bower, J.M. (eds.): Computational modelling of Genetic and Biochemical Networks. The MIT Press, London (2001)
Brown, R.G. (ed.): Introduction to Random Signal Analysis and Kalman Filtering. John Wiley & Son, Chichester (1983)
Brownstein, M.J., Trent, J.M., Boguski, M.S.: Functional genomics. In: Patterson, M., Handel, M. (eds.) Trends Guide to Bioinformatics, pp. 27–29 (1998)
Collado-Vides, J.: A transformational-grammar approach to study the regulation of gene expression. J. Theor. Biol. 136, 403–425 (1989)
Dorf, R., Bishop, R.H.: Modern Control Systems. Prentice-Hall, Englewood Cliffs (1998)
Fields, S., Kohara, Y., Lockhart, D.J.: Functional genomics. Proc. Natl. Acad. Sci. USA 96, 8825–8826 (1999)
Friedman, L., Nachman, P.: Using Bayesian networks to analyze expression data. Journal of Computational Biology 7, 601–620 (2000)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and machine Learning. Addison-Wesley, Reading (1989)
Hofestadt, R., Meineke, F.: Interactive modelling and simulation of biochemical networks. Comput. Biol. Med. 25, 321–334 (1995)
Holland, H.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)
Kasabov, N., Dimitrov, D.: A method for gene regulatory network modelling with the use of evolving connectionist systems. In: ICONIP - International Conference on Neuro-Information Processing. IEEE Press, Singapore (2002)
Likhoshvai, V.A., Matushkin, Y.G., Vatolin, Y.N., Bazan, S.I.: A generalized chemical kinetic method for simulating complex biological systems. A computer model of lambda phage ontogenesis. Computational technol. 5(2), 87–89 (2000)
Loomis, W.F., Sternberg, P.W.: Genetic networks. Science 269, 649 (1995)
Mc Adams, H.H., Aarkin, A.: Stochastic mechanism in gene expression. Proc. Natl. Acad. Sci. USA 94, 814–819 (1997)
Muhlenbein, H.: How genetic algorithms really work: I. mutation and hillclimbing. In: Manderick, B. (ed.) Parallel Problem Solving from Nature 2. Elsevier, Amsterdam (1992)
Sanchez, L., van Helden, J., Thieffry, D.: Establishment of the dorso-ventral pattern during embryonic development of Drosophila melanogaster. A logical analysis. J. Theor. Biol. 189, 377–389 (1997)
Tchuraev, R.N.: A new method for the analysis of the dynamics of the molecular genetic control systems. I. Description of the method of generalized threshold models. J. Theor. Biol. 151, 71–87 (1991)
Thieffry, D.: From global expression data to gene networks. BioEssays 21(11), 895–899 (1999)
Thieffry, D., Thomas, R.: Dynamical behaviour of biological regulatory networks-II. Immunity control in bacteriophage lambda. Bull. Math. Biol. 57, 277–297 (1995)
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Kasabov, N.K., Chan, Z.S.H., Jain, V., Sidorov, I., Dimitrov, D.S. (2004). Gene Regulatory Network Discovery from Time-Series Gene Expression Data – A Computational Intelligence Approach. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_209
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DOI: https://doi.org/10.1007/978-3-540-30499-9_209
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