Sparse Reconstruction Based Structure Estimation of GRNs Using Time Series Experimental Data

A strategy for estimating structure of gene regulatory networks (GRNs) is proposed on basis of time series experimental data in this paper. This strategy is an essential for transforming the problem of identifying a GRN into that of sparse reconstruction whose measurement matrix is composed of the nonlinear functions of a GRN. Differently from tradition methods, this method is well suitable for a large scale network identification, and may avoid both linearized inaccuracy and limitation of the length of time series data. We utilize an algorithm of the stagewise modified orthogonal matching pursuit (SmOMP) to estimate the sparse vector that denote a gene regulated by other genes. Compared with robust state estimator (RSE), the well known extended Kalman filter (EKF) and unscented Kalman filter (UKF) based method, the efficiency of this method has been verified by numerical simulations. Actual results show that this suggested method not only has better convergence speed and higher estimate accuracy, but also can significantly reduce the computational complexity in GRN inferences.