Noise analysis of time series data in gene regulatory networks

One of the most important properties in gene expression is the stochasticity. Gene expression process is noisy and fluctuant. In this paper, the quantitative analysis of noisy time-series gene expression data on inference of gene regulatory networks is performed. We propose a two-step algorithm to solve the problem. In the first step, B-Spline is introduced to interpolate between data points. In the second step, Kalman filter or H_∞ filter is introduced to infer the gene structure. If the statistical noise is known, Kalman filter is applied; Otherwise H_∞ filter is applied. Both synthetic data and real experiment data are used to evaluate the procedure.