Estimation of noisy gene regulatory networks

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.