Stochastic master equation (SME) models can provide detailed representation of genetic regulatory system but their use is restricted by the large data requirements for parameter inference and inherent computational complexity involved in its simulation. In this paper, we approximate the expected value of the output distribution of the SME by the output of a… (More)
In this article, we will review approaches to study the robustness of GRN modeling and control strategies with emphasis on the steps of model selection, model inference, and network intervention.
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)
Over the past few years, we have witnessed a growing popularity of new wireless architectures such as 3G, Wi-Fi and Wi-Max due to the increase in demand for wireless Internet access. The all-IP based future mobile and wireless network model is expected to be the most dominant architecture for QoS provisioning in next-generation wireless networks, mainly due… (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)