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

Weber, Gerhard Wilhelm
Kropat, Erik
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.


Conceptual design of fuzzy object-oriented databases
Yazıcı, Adnan (1998-04-23)
An important research trend in databases is to handle different types of uncertainty at conceptual level. The trend of incorporating complex objects in databases presents opportunities for representing imprecision and uncertainty that were difficult to integrate cohesively in simple database models. In this study we introduce a conceptual data model by extending ExIFO to handle both complex and uncertain, mainly fuzzy, objects and classes.
Optimising a nonlinear utility function in multi-objective integer programming
Ozlen, Melih; Azizoğlu, Meral; Burton, Benjamin A. (2013-05-01)
In this paper we develop an algorithm to optimise a nonlinear utility function of multiple objectives over the integer efficient set. Our approach is based on identifying and updating bounds on the individual objectives as well as the optimal utility value. This is done using already known solutions, linear programming relaxations, utility function inversion, and integer programming. We develop a general optimisation algorithm for use with k objectives, and we illustrate our approach using a tri-objective i...
Nonlinear and dynamic programming models for an inventory problem in a partially observable environment
Darendeliler, Alp; Serin, Yaşar Yasemin; Department of Industrial Engineering (2016)
In this study, a single-item periodic-review inventory system is considered in a partially observable environment with finite capacity, random yield and Markov modulated demand and supply processes for finite-horizon. The exact state of the real process, which determines the distribution of the demand and supply, is unobservable so the decisions must be made according to the limited observations called observed process. Partially Observable Markov Decision Process is used to model this problem. As an altern...
Handling complex and uncertain information in the ExIFO and NF2 data models
Yazıcı, Adnan; Petry, FE (1999-12-01)
Trends in databases leading to complex objects present opportunities for representing imprecision and uncertainty that were difficult to integrate cohesively in simpler database models. In fact, one can begin at the conceptual level with a model that allows uncertainty assumptions and then transform those assumptions into a logical model having the necessary semantic foundations upon which to base a meaningful query language. Here we provide such a constructive approach beginning with the ExIFO model for ex...
Analyses of Two Different Regression Models and Bootstrapping
Gökalp Yavuz, Fulya (Springer, Berlin, Heidelberg, 2011-09-02)
Regression methods are used to explain the relationship between a single response variable and one or more explanatory variables. Graphical methods are generally the first step and are used to identify models that can be explored to describe the relationship. Although linear models are frequently used and they are user friendly, many important associations are not linear and require considerably more analytical effort. This study is focused on such nonlinear models. To perform statistical inference in this ...
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: