Control Systems and Computers, N5, 2017, Article 4


Upr. sist. maš., 2017, Issue 5 (271), pp. 43-53.

UDC 574:004.2

A.A. Fefelov1, V.I. Lytvynenko2, M.A. Taif3, I.A. Lurie4

1 PhD in Techn. Sciences, Associate Professor, Department of Design of Kherson National Technical University, Bereslavskoe Shosse, 24, Kherson, 73008, Ukraine,
2 Doctor of Technical Sciences, Professor, Head of the Department of Informatics and Computer Science of Kherson National Technical University, Bereslavskoe Shosse, 24, Kherson, 73008, Ukraine,
3 Graduate student of the Department of Informatics and Computer Science, Kherson National Technical University, Bereslavskoe Shosse, 24, Kherson, 73008, Ukraine,
4 PhD in Techn. Sciences, Associate Professor, Department of Informatics and Computer Science of Kherson National Technical University, Bereslavskoe Shosse, 24, Kherson, 73008, Ukraine,

Parametric Identification of the S-System by the Modified Clonal Selection Algorithm

Introduction. Modeling the biological systems and their interactions has the key value for the mechanisms of the functioning understanding. Recently, a lot of tools and technologies have been developed. They allow us to build and study the models of the biological systems and processes. One of such tools is the reconstruction or reverse engineering of gene regulatory networks, which helps us identify the structural and dynamic properties of the system based on the observations of its behavior and certain knowledge in the relevant subject area. Using the gene expression profiles obtained by DNA microarray, gene sets involved in a certain biological process are revealed. However, at this stage of development, the use of expression for reconstructing architecture and behavior of regulatory networks remains unsolved.
Purpose. The aim of this work is to create an effective method of the optimal parameters of the mathematical model of a gene regulatory network searching based on the ordinary differential equations system represented in the form of S-system.
Method. A method is based on the successive transformation of decision space, guided by the results of the separate starting of clonal selection algorithm, hereupon space compresses in the vicinity of the global optimum.
Results. A method for reconstructing the gene regulatory networks based on a modified clonal selection algorithm is developed. The method uses time series data of the gene expression profiles for searching interconnections between GRN components. The efficiency of the proposed method is confirmed by the experimental studies.
Conclusion. The developed method and the algorithm increase the speed of the convergence of the optimization algorithms, and at the same time improve their accuracy in solving the problem of parametric identification of S-System. The proposed method can be used for modification of the evolutionary algorithms or artificial immune systems. Besides, in our future research we plan to test the method effectiveness on the real biological data.

Keywords: gene regulatory networks, reverse engineering, gene expression, ordinary differential equations, clonal selection algorithm, wavelet-neural network.

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Received 03.10.2017