Human endotoxemia is a well-accepted surrogate model for studying the acute inflammatory responses. In order to discover the complex underlying dynamics, identifying biologically relevant transcriptional regulators as well as their putative regulatory interactions with target genes is an essential step. However, prediction of relevant transcriptional regulators in higher eukaryotes remains a challenge both in silico and in vivo. In this study, we analyzed gene expression data from human blood leukocytes to extract four significant patterns of highly coexpressed genes that capture the essence of inflammatory phases. Upon identification of these patterns, a number of inflammation-specific pathways are selected by evaluating the enrichment of the corresponding subsets. Subsequently, statistically significant cis-regulatory modules (CRMs) are selected and decomposed into a list of relevant transcription factors (34 TFs) which are further validated from prior experiments and computational studies in literature. Additionally, our analysis also allows for the construction of a putative dynamic representation of the transcriptional regulatory program, making it become a critical enabler for unraveling regulatory interactions which is an essential step towards a quantification of dynamic transcriptional regulatory networks.