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Issue title: New Methods in Bioinformatics Presented at the Fifth International Conference on Bioinformatics of Genome Regulation and Structure
Guest editors: Evgenii Vityaevx and Nikolay Kolchanovy
Article type: Research Article
Authors: Miginsky, D.S.a; b; * | Suslov, V.V.a | Timonov, V.S.a | Rasskazov, D.A.a; b | Sournina, N.Yu.a | Podkolodny, N.L.a; b
Affiliations: [a] Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia | [b] Novosibirsk State University, Novosibirsk, Russia | [x] Sobolev Institute of Mathematics, Koptyug aven. 4, Novosibirsk, 630090, Russia | [y] Laboratory of Theoretical Genetics, Institute of Cytology and Genetics, Novosibirsk, 630090, Russia
Correspondence: [*] Corresponding author: Denis S. Miginsky, Institute of Cytology and Genetics SB RAS, 10 Lavrentiev ave., Novosibirsk, Russia. Tel.: +7 383 3332971; Fax: +7 383 3331278; E-mail: [email protected].
Abstract: The network modelling technique is one of the most effective ways to represent the biological system models of almost any types, and also their further analysing and simulating. The work is aimed to develop the unified software toolkit for these purposes, capable to operate with different knowledge domains. Particularly, we are taking into account ecological and molecular-genetic systems of high complexity. The theoretical part of the work is concerned with adaptation of the visual modelling technique for this area, development of principles and methods to represent network models and related knowledge domains in formal way, and approaches to handle the complex networks of thousands and even more elements. The other part of the work includes the requirements analysis, developing the software components prototypes. The main components are: the database with capabilities mentioned, and visual modelling tool for biological networks with a high grade of adaptability and flexibility.
Keywords: Computer-aided research & development, visual modelling, databases, gene networks, ecological networks
DOI: 10.3233/IDA-2008-12503
Journal: Intelligent Data Analysis, vol. 12, no. 5, pp. 463-479, 2008
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