Title :
Estimation of noisy gene regulatory networks
Author :
Chuang, Chia-Hua ; Lin, Chun-Liang
Author_Institution :
Dept. of Electr. Eng., Nat. Chung Hsing Univ., Taichung, Taiwan
Abstract :
Biological systems possess highly complex characteristics which are usually nonlinear and stochastic. How to estimate the states of that kind of systems is attractive to control engineers when the sensors are unavailable to measure desired information. In this paper, a robust estimation scheme based on the extended Kalman filter to estimate the state variables of a class of noisy gene regulatory networks are presented while the protein concentration is not individually measured. A numerical simulation is provided to confirm the proposed method.
Keywords :
Kalman filters; biology computing; estimation theory; genetics; biological system; extended Kalman filter; noisy gene regulatory network; protein concentration; robust estimation scheme; Biological systems; Estimation; Kalman filters; Mathematical model; Noise; Noise measurement; Proteins; estimation; extended Kalman filter; gene regulatory networks; stochastic model; systems biology;
Conference_Titel :
SICE Annual Conference 2010, Proceedings of
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7642-8