Abstract
Background: Inflammation is a hallmark of many human diseases. When primary pathogenetic events are unknown, construction of gene regulatory network of inflammation is sometimes the best way to gain more insight into it. To better elucidate the mechanisms underlying systemic inflammation is an important topic to monitor disease progress for individual treatment regimens. It is more appealing to construct a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Results: In this study, we present a gene regulatory network via database (Ensembl, JASPAR), Cross-correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC) to describe genome-wide transcriptional responses in the context of dynamic genes, which are regulated by transcription factors (TFs) including family. This approach is based on the dynamic equation of blood leukocyte gene expression profiles of human subject to receive an inflammatory stimulus (bacterial endotoxin). Based on the magnitudes of kinetic parameters of dynamic gene regulatory network, we could identify significant properties (such as susceptibility to infection) of inflammation systems, which are useful for clinic research. Conclusion: It is important to find that the transcriptional programs are modified as cell progresses a reaction to change environmental conditions. In this study, a computational analysis of multiple types of data was developed to efficiently select candidates of regulators of the network in the inflammation system. Compared with previous results in literature, the proposed gene network construction method is found with significant improvement.