An emerging model views gene regulation as a competition for regulatory sequences between transcription factors and nucleosomes at thermodynamic equilibrium. A computational model of the DNA affinity of nucleosomes was recently developed allowing to predict the nucleosome occupancy of DNA sequences. The aim of this project was to investigate the effect of different predicted nucleosome occupancies of an important transcription factor binding site on mammalian gene expression. To this end, a two site and three site gateway compatible system allowing integration of DNA at a specific locus of two mouse embryonic stem cell lines was developed. An important POU5F1 (previously known as OCT4)/SOX2 binding site located in the Nanog promoter was chosen to be the transcription factor binding site of interest as it was shown to play a major role for Nanog expression. A 2kb sequence from the Nanog proximal promoter (-1585 to +415) was cloned upstream of two different minimal promoters and a GFP reporter using three site gateway cloning. These constructs were integrated in both cell lines and expressed low to medium levels of GFP. Next, several variants of a 1kb sequence from the Nanog promoter (-933 to +53), previously shown to be functional, were constructed. 35bp surrounding the POU5F1/SOX2 binding site were altered resulting in differential predicted nucleosome occupancy of the binding site. These promoters were linked to GFP with or without the addition of a kozak consensus sequence and a SV40 polyadenylation signal. Surprisingly, all these variants showed low GFP expression after transfection. This was not caused by biases introduced by the gateway system as a gateway construct containing the CMV promoter linked to GFP was shown to be functional. Furthermore, direct cloning of the 1kb Nanog promoter in a GFP expressing vector without gateway showed similar GFP expression as gateway cloning. Results indicate that both 2kb and 1kb Nanog promoters express GFP at low levels and that the constructs produced may be useful to measure differences in GFP expression using flow cytometry