Recent advances in technologies such as DNA microarrays have provided a mass of gene expression data on the genomic scale. One of the most important projects in post-genome-era is the systemic identification of gene expression networks. However, inferring internal gene expression structure from experimentally observed time-series data is an inverse problem. We have therefore developed a system for inferring network candidates based on experimental observations. Moreover, we have proposed an analytical method for extracting common core binomial genetic interactions from among various network candidates. Common core binomial genetic interactions are reliable interactions and are important in understanding the dynamic behavior of gene expression network. Here, we discuss an efficient method for inferring genetic interactions that combines a Step-by-step strategy  with an analytical method for extracting common core binomial genetic interactions.