Gene regulatory networks （GRN）, which focuses on the complex interactions of genes in life, is an important part in the study of the functional genomics and is the frontier of bioinformatics research. Application of gene-chip technique in bioinformatics provides a great number of basic data for the research of GRN. This paper reviews the origin and recent development of GRN, explicates the preconditions and rationales for construction of GRN, and analyzes several classic GRN models： Boolean networks, linear models, non-linear models and Bayesian networks. The rationales, basic algorithms, advantages, disadvantages and applicability of the models are reviewed based on the characteristics of gene-chip data.