Abstract: “The inference of regulators is the core factor in interpreting the actual regulatory conditions in gene regulatory networks (GRNs). Various methods have been developed to reconstruct GRNs with the motivation of improving the accuracy and scalability of network inference. Thus, this study will brief the structure of GRNs, discuss current methods of GRNs reconstruction and problems when dealing with gene perturbation data. Most of the information gathered from bioinformatics and system biology literature. At the end of the study several of GRNs reconstruction methods will be reviewed and identified their problem when dealing with gene perturbation data. This study is useful as a reference to develop more accurate GRNs inference methods particular for gene perturbation data”

Keywords: gene regulatory network, reconstruction of gene regulatory networks (GRNs), gene expression data, gene perturbation, machine learning methods