Control Systems and Computers

DOI: https://doi.org/10.15407/usim.2017.04.067

Upr. sist. maš., 2017, Issue 4 (270), pp. 67-75, 82.

UDC 574:004.2 

Fefelov Andrej A., PhD in Techn. Sciences, Associate Professor, fao1976@ukr.net  

Lytvynenko Volodymyr I., Doctor of Technical Sciences, Professor, Head of the Department, immun56@gmail.com  

Taif Muchamed Ali, Graduate student, taifmohamedali@gmail.com

Voronenko Mariia A.,  PhD in Techn. Sciences, Associate Professor,  mary_voronenko@i.ua

Department of Design of Kherson National Technical University, Bereslavskoe Shosse, 24, Kherson, 73008, Ukraine

Object-Oriented Architecture of the Information System for the Reconstruction of the Gene Regulatory Networks

Introduction. Insufficient level of understanding of the nature of regulation and functional mechanisms of gene regulatory networks does not allow to build their mathematical models, based on the fundamental laws of component’s interaction. Now, many different models and methods of gene regulatory reconstruction are developed, which have the advantages and disadvantages. At the choice of descriptive model it is necessary to consider the fact, that mathematical models, as a rule, have their own structure and a number of parameters, which need to be identified. A large number of computational methods are developed, for structural parametric model’s identification. The majority of them have increased resistance to noise and uncertainty contained in the initial data. The presence of this property is real for the selection of a computational method, used for solving the reconstructing problem of the gene regulatory network based on the gene expression data.

Purpose. The purpose of this work is the development of the information system architecture for the gene regulatory network reconstruction, based on the object-oriented approach.

Method. The authors used the method of object-oriented design for the developing of this information system.

Results. The architecture of the information system for the gene regulatory networks reconstruction, based on the objectoriented approach is proposed. The S-system is applied as a computational model. The parameters and structure are calculated using the clonal selection algorithm. The gene expression profiles are used as an input data. The developed system includes four basic components: the data source, the model, the solution converter and the identification method. The scenario of solving gene network reconstruction problem is developed. In addition, an iterative algorithm for the space optimization search of the computational model parameter values is implemented in this scenario.

Conclusion. The developed architecture is open, so that allows to add or replace the separate components by expansions. Further researches suggest to expand the range of used models, such as radial-base network model and wavelet-neural network model, as well as the system for the gene expression programming. In the future, we are planning to implement the addition of new evolutionary algorithms to the information system. In such a way, the work of the evolutionary operators by the development of new scenarios for solving the gene regulatory networks reconstruction problems can be improved.

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Keywords:  gene regulatory networks, reverse engineering, gene expression,  S-system, clonal selection algorithm, information system, structural-parametric identification.

1. DeRisi, J.L., Lyer, V.R., Brown, P.O., 1997. “Exploring the metabolic and genetic control of gene expression on a genomic scale”, Science, 278, pp. 680–686.
   https://doi.org/10.1126/science.278.5338.680
2. Akutsu, T., Miyano, S., Kuhara, S., 1999. “Identification of genetic networks from a small number of gene expression patterns under the boolean network model”, Pacific Symposium on Biocomputing (PSB’99), Jan. 1999: proc. Maui, Hawaii, USA, pp. 17–28.
3. Bower, J.M., Bolouri, H., 2001. Computational Modeling of Genetic and Biochemical Networks, The MIT Press, 336 p.
4. Maki, Y., Tominaga, D., Okamoto M. et al., 2001. “Development of a system for the inference of large scale genetic networks”. Pacific Symposium on Biocomputing (PSB’01), Jan. 2001: proc., Maui, Hawaii, USA, 2001, P. 446–458.
5. Perrin, B.-E. , Ralaivola, L., Mazurie A. et al., 2003. “Gene networks inference using dynamic bayesian networks”, Bioinformatics, 9(2), pp. 138–148.
   https://doi.org/10.1093/bioinformatics/btg1071
6. Savageau, M.A., 1988. “Introduction to S-systems and the underlying power-law formalism”, Mathematical and Computer Modelling, 11, pp. 546–551.
   https://doi.org/10.1016/0895-7177(88)90553-5
7. Tominaga, D., Koga, N., Okamoto, M., 2000. “Efficient numerical optimization algorithm based on genetic algorithm for inverse problem”, Genetic and Evolutionary Computation Conference (GECCO’00), July 2000: proc., Las Vegas, Nevada, USA, 251, pp. 251–258.
8. A.A. Fefelov, V.I. Lytvynenko, M.A. Taif et al., 2017. “Hybrid Approach for Gene Regulatory Networks Reconstruction”, Upravlausie sistemy i masiny, 3, pp. 63–72.
9. Weaver, D.C., Workman, C.T., Stormo, G.D., 1999. “Modeling regulatory networks with weight matrices”, Pacific Symposium on Biocomputing 4 (PSB’99), 4–9 Jan. 1999: proc., Maui, Hawaii, USA, 1999, P. 112–123.
10. Sakamoto, E., Iba, H., 2001. “Inferring a system of differential equations for a gene regulatory network by using genetic programming”, Congress on Evolutionary Computation, 27–30 May 2001: proc., COEX, Seoul, Korea, pp. 720–726.
    https://doi.org/10.1109/CEC.2001.934462
11. De Castro, L.N., Timmis, J., 2002. “Artificial Immune Systems: A New Computational Intelligence Approach”, Heidelberg: Springer, 357 p.
12. De Castro, L.N., Von Zuben, F.J., 2002. “Learning and optimization using the clonal selection principle”, IEEE Transactions on Evolutionary Computation, 6(3), pp. 239–251.
    https://doi.org/10.1109/TEVC.2002.1011539

Received 15.08.2017