Mehmet Umut Caglar

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T here are two primary objectives for modeling of genetic regulatory networks (GRNs): 1) to better understand the gene interactions and relationships on a holistic level and predict the behavior of biological systems ; and 2) to design and analyze control strategies for moving the state of a network from an undesirable location to a desirable one. The(More)
With the developments of Information High Technology, all applications of the instruction start to have tendency towards technology based instruction instead of directed, teacher-centered instruction. It is important to mention that computers are the main instructional support to the learning and teaching process. As a human being, there is an adaptation(More)
Probabilistic Models are regularly applied in Genetic Regulatory Network modeling to capture the stochastic behavior observed in the generation of biological entities such as mRNA or proteins. Several approaches including Stochastic Master Equations and Probabilistic Boolean Networks have been proposed to model the stochastic behavior in genetic regulatory(More)
BACKGROUND Design of drug combination cocktails to maximize sensitivity for individual patients presents a challenge in terms of minimizing the number of experiments to attain the desired objective. The enormous number of possible drug combinations constrains exhaustive experimentation approaches, and personal variations in genetic diseases restrict the use(More)
The fine-scale stochastic behavior of genetic regulatory networks is often modeled using stochastic master equations. The inherently high computational complexity of the stochastic master equation simulation presents a challenge in its application to biological system modeling even when the model parameters can be properly estimated. In this article, we(More)
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