Hematopoiesis is a complex and dynamic process, which generates mature blood cells throughout the life of organisms. It is believed that the blood lineage choice of HSCs is governed by a stepwise cell fate decision. However, recent studies have raised questions about the hierarchical hematopoietic system. Many studies based on genome-wide gene expression profiling have demonstrated that specific extrinsic and intrinsic regulators play key roles in hematopoiesis. Recently, high-throughput sequencing techniques have been applied widely, which have provided new insights into in vivo transcription factor binding and epigenetic modifications. Systems biology approaches are also enhancing our understanding of the regulatory dynamics of hematopoiesis. Despite the biological importance of the formation of all blood cells via a transition from LT-HSC to ST-HSC, little is known about the mechanism that underlies this early differentiation. A major explanation for this deficiency is a lack of comprehensive genome-wide identification studies and characterizations of the regulatory elements that govern gene expression in HSCs. The profiling of potential key regulators and the large-scale integration of datasets have improved our understanding greatly. However, these studies are limited to a small number of factors that function in heterogeneous HSCs, which were isolated using Methyldopa different combinations of monoclonal antibodies. Lofexidine hydrochloride Therefore, unconsidered key regulators may exist at this early stage of hematopoiesis. Indeed, novel key factors and new multipotent progenitors have been identified recently. To address these deficiencies, we developed a computational method on the basis of novel transcriptome data from adult mouse bone marrow HSCs; CD34{KSL LT-HSCs and CD34zKSL ST-HSCs, a widely used strategy to isolate HSCs at high purity. Our method uses a regression-based approach to model the linear relationships between gene expression and the characteristics of regulatory elements compiled from a database.In the present study, we extended this regression modeling-based approach using large-scale log-linear modeling, which considered the combinatorial nature of TFs.