An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.
In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.
Wang
Zidong
Z
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB83PH, UK. zidong.wang@brunel.ac.uk
Liu
Xiaohui
X
Liu
Yurong
Y
Liang
Jinling
J
Vinciotti
Veronica
V
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D015870:Gene Expression; D053263:Gene Regulatory Networks; D023281:Genomics; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D014780:Viruses / Q000235:genetics; D015003:Yeasts / Q000235:genetics