nfang Jia

Global Sensitivity Analysis of Cell Signalling Transduction Networks Based on Latin Hypercube Sampling Method

The dynamic behavior of a cell model is affected by its structural complexity and parametric uncertainties. Two important issues in systems biology are how to quantitatively determine the relationship between system behaviors and parameter variations, and how to study the interactions between parameters. Using an NF-kB signaling pathway model as an example, and assuming that the parameters of this model are independent of each other and obey the identical uniform distribution in the range of variations, the global sensitivity analysis on the system output of NF-kB in the nucleus with respect to parameters is studied by means of the Latin hypercube sampling method. Simulation results demonstrate that the oscillation behavior of the concentration of NF-kB in the nucleus is sensitive to 6 key rate constants, which relates to reactions of IKKBa mRNA degradation, IkBa inducible mRNA synthesis, IKK adaption, constitutive IkBa mRNA translation, IKK-IkBa NF-kB association, and IkBbeta mRNA degradation, respectively.

Model Simplification of Signal Transduction Pathway Networks Via a Hybrid Inference Strategy

Abstract A full-scale mathematical model of cellular networks normally involves a large number of variables and parameters. How to effectively develop manageable and reliable models is crucial for effective computation, analysis and design of such systems. The aim of model simplification is to eliminate parts of a model that are unimportant for the properties of interest. In this work, a model reduction strategy via hybrid inference is proposed for signal pathway networks. It integrates multiple techniques including conservation analysis, local sensitivity analysis, principal component analysis and flux analysis to identify the reactions and variables that can be considered to be eliminated from the full-scale model. Using an IκB-NF-κB signalling pathway model as an example, simulation analysis demonstrates that the simplified model quantitatively predicts the dynamic behaviours of the network.

A Wavelet-based Approach to Improve Foggy Image Clarity

Under foggy viewing conditions, image contrast is often significantly degraded by atmospheric aerosols, which makes it difficult to quickly detect and track moving objects in intelligent transportation systems (ITS). A foggy image visibility enhancing algorithm based on an imaging model and wavelet transform technique is proposed in this paper. An optical imaging model in foggy weather conditions is established to determine the image degradation factors and compensation strategies. Based on this, the original image is firstly transferred into YUV color space of a luminance and two chrominance components. Then the luminance component is decomposed through wavelet transform into low- and high-frequency subbands. In the low-frequency subband, the medium scattered light component is estimated using Gaussian blur and removed from the image. Nonlinear transform for enhancement of foggy images is applied to high-frequency subbands. In the end, a new image is recovered by combining the chrominance components and the corrected luminance component altogether. Experimental results demonstrate that this algorithm can handle the problem of image blurring caused by atmospheric scattering effectively, and has a better real-time performance compared with a standard model-based procedure.

Minimum Entropy Control Algorithm for General Dynamic Stochastic Systems

In order to measure the uncertainty of the stochastic systems subjected to arbitrary noise disturbance instead of Gaussian white noise, the minimum entropy control of tracking errors for dynamic stochastic systems is presented in this paper. Different from conventional hypothesis, it is assumed that the system output and noise obey multi-to-one mapping, which is more general in the practical application. A controller design was described based on minimizing system output error entropy and a recursive optimization algorithm was set up for dynamic, non-Gaussian and nonlinear system. This approach only used the formula of the probability density function of the tracking error to calculate the controller and it did not need to know the style of the system model and the probability density function of noise, which often is difficult to measure in fact. An illustrative example is utilized to demonstrate the efficiency of the minimum entropy control algorithm and the approving simulation results have been gained

Parameter Estimation of Signal Transduction Pathways Using Probability Density Function of Measurement

Parameter estimation of signal transduction pathway models is a challenging task as such models are normally nonlinear, high dimensional, and the measurement data is limited and corrupted by noise. In this paper, a novel method for parameter estimation is proposed, in which the distance between the probability density function (PDF) of the model output and the PDF of the measurement data is minimized. This method has been applied to estimate unknown parameters of a TNFalpha- mediated NF-kappaB signal transduction pathway model. The simulation results show the effectiveness of this new algorithm.