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Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation

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Abstract

This mathematical contribution is addressed towards the wide interface of life and human sciences that exists between biological and environmental information. Like very few other disciplines only, the modeling and prediction of genetical data is requesting mathematics nowadays to deeply understand its foundations. This need is even forced by the rapid changes in a world of globalization. Such a study has to include aspects of stability and tractability; the still existing limitations of modern technology in terms of measurement errors and uncertainty have to be taken into account. In this paper, the important role played by the environment is rigorously introduced into the biological context and connected with employing the theories of optimization and dynamical systems. Especially, a matrix-vector and interval concept and algebra are used; some special attention is paid to splines.

From data got by DNA microarray experiments and environmental measurements we extract nonlinear ordinary differential equations. This is done by Chebychev approximation and semi-infinite optimization. Then, time-discretized dynamical systems are studied. By a combinatorial algorithm which constructs and follows polyhedra sequences, the region of parametric stability is detected. This is used for testing and maybe improving the goodness of the achieved model. We analyze the topological landscape of gene-environment networks in terms of structural stability which we characterize.

This pioneering practically motivated and theoretically elaborated work is devoted to a contribution to better health care, progress in medicine, better education, and to recommending more healthy living conditions. The present paper mainly bases on the authors’ and their coauthors’ contributions of the last few years, it critically discusses structural frontiers and future challenges, while respecting related research contributions, giving access and referring to alternative concepts that exist in the literature.

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Authors and Affiliations

  1. Institute of Applied Mathematics, Middle East Technical University, 06531, Ankara, Turkey

    G.-W. Weber, P. Taylan, S. Z. Alparslan-Gök, S. Özöğür-Akyüz & B. Akteke-Öztürk

  2. Faculty of Economics, Management and Law, Hölderlinstraße 3, 57068, Siegen, Germany

    G.-W. Weber

  3. Department of Mathematics, Süleyman Demirel University, 32260, Isparta, Turkey

    S. Z. Alparslan-Gök

  4. Department of Mathematics, Dicle University, 21280, Diyarbakır, Turkey

    P. Taylan

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Weber, GW., Taylan, P., Alparslan-Gök, S.Z. et al. Optimization of gene-environment networks in the presence of errors and uncertainty with Chebychev approximation. TOP 16, 284–318 (2008). https://doi.org/10.1007/s11750-008-0052-5

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