Modeling, inference and optimization of regulatory networks based on time series data

Date

2011-05-16

Author

Weber, Gerhard Wilhelm
DEFTERLİ, ÖZLEM
ALPARSLAN GÖK, Sırma Zeynep
Kropat, Erik

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by various kinds of optimization techniques, ranging from linear, mixed-integer, spline, semi-infinite and robust optimization to conic, e.g., semi-definite programming. We present different kinds of uncertainties and a new time-discretization technique, address aspects of data preprocessing and of stability, related aspects from game theory and financial mathematics, we work out structural frontiers and discuss chances for future research and OR application in our real world.

Subject Keywords

Nonlinear programming, Uncertainty modeling, Computational biology, Data mining, Games

URI

https://hdl.handle.net/11511/57363

Journal

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

DOI

https://doi.org/10.1016/j.ejor.2010.06.038

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

G. W. Weber, Ö. DEFTERLİ, S. Z. ALPARSLAN GÖK, and E. Kropat, “Modeling, inference and optimization of regulatory networks based on time series data,” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, pp. 1–14, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57363.