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Robust Estimator Design for Noisy Gene Networks
|關鍵字:||biological system;生物系統;gene network;stochastic model;extended Kalman filter;stability analysis;基因網路;隨機模型;擴展型卡曼濾波器;穩定性分析||出版社:||電機工程學系所||引用:|| E. O. Voit, Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Cambridge University Press, New York, 2000.  B. Q. Palsson, Systems Biology: Properties of Reconstructed Networks, Cambridge University Press, New York, 2006.  U. Alon, An Introduction to Systems Biology: Design Principles of Biological Circuits, Chapman & Hall/CRC, Boca Raton, 2007.  H. Kitano, “Systems biology: a brief overview,” Science, vol. 295, pp. 1662-1664, 2002.  M. Khammash and H. El-Samad, “Systems biology: from physiology to gene regulation,” IEEE Control Systems Magazine, vol. 24, pp.62-76, 2004.  G. Karlebach and R. Shamir, “Modelling and analysis of gene regulatory networks,” Nature Reviews Molecular Cell Biology, vol. 9, pp. 770-780, 2008.  H. H. McAdams and A. 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Looze, “Minimax state estimation for linear stochastic systems with noise uncertainty,” IEEE Transactions on Automatic Control, vol. 26, pp. 902-906, 1981.||摘要:||
Gene networks in biological systems are highly complicated due to nonlinear and stochastic features that they involve many crosstalk and feedback regulatory mechanisms and may be suffered from noise corruption due to the molecular noises from intrinsic fluctuations, transmitted noise from upstream genes, and the global noise affecting all genes. In this environment, how to filter noises and measure the internal states of noisy gene networks by systematic control strategies and biological techniques are attractive issues to control engineers and biologists. However, the internal states of most biological systems might not be directly accessible. In this thesis, a robust extended Kalman filter for state estimation of a class of noisy nonlinear gene networks with uncertain process noise and multiple noises is presented. Quantitative analysis of the estimation performance based on Lyapunov stability theory is conducted. Numerical simulations for real gene networks are provided to effectively confirm the proposed method.
|Appears in Collections:||電機工程學系所|
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