Optimization and dynamics of gene-environment networks with intervals

There are a few areas of science and technology which are only as challenging, emerging and promising as computational biology. This area is looking for its mathematical foundations, for methods of prediction while guaranteeing robustness, and it is of a rigorous interdisciplinary nature. In this paper, we deepen and extend the approach of learning gene-expression patterns in the framework of gene-environment networks by optimization, especially, generalized semi-infinite optimization (GSIP). With respect to research done previously, we additionally imply the fact that there are measurement errors in the microarray technology and in the environmental data likewise; moreover, the effects which exists among the genes and environmental items can seldom be precisely quantified. Furthermore, we present the well-established matrix algebra for our extended model space, and we indicate further new approaches.


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An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; to fully understand its foundations requires a mathematical study. This paper surveys and mathematically expands recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic context within the framework of matrix and interval arithmetic. Given the data from DNA microarray experiments...
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An emerging research area in computational biology and biotechnology is devoted to modelling and prediction of gene-expression patterns. In this article, after a short review of recent achievements we deepen and extend them, especially, by emphasizing and analysing the elegant means of matrix algebra. Based on experimental data, ordinary differential equations with nonlinearities on the right-hand side and a generalized treatment of the absolute shift term, representing the environmental effects, are invest...
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Analysis of large networks in biology, science, technology and social systems have become very popular recently. These networks are mathematically represented as graphs. The task is then to extract relevant qualitative information about the empirical networks from the analysis of these graphs. It was found that a graph can be conveniently represented by the spectrum of a suitable difference operator, the normalized graph Laplacian, which underlies diffusions and random walks on graphs. When applied to large...
Citation Formats
Ö. Uğur, “Optimization and dynamics of gene-environment networks with intervals,” JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, pp. 357–379, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54890.