As the PPARγ ChIP did not appear to support the competitive binding model, we next used ChIP to examine the MMP-1 and MMP-13 promoters for evidence of nuclear receptor-associated coactivator and corepressor activity by detecting changes in histone acetylation.
DOI: http://dx.doi.org/10.7554/eLife.02391.012 The equations describing competition data are more complicated than those describing a single-site binding model, but we used this simplified model to fit our ITC data to allow comparison with the results described in Kummel et al. (2011).
To model for RBP-mRNA binding occupancy, we used a biophysical model similar to that presented by Foat et al. (2006).
Analyzed uniquely mapped reads used for analysis (Bold) To identify LRH-1 binding peaks, we used Model-based Analysis of ChIP-seq (MACS), which was designed to analyze data generated by short read sequencers such as from the SOLiD platform [ 15] to first estimate peak size and location, using BED files as an input.
In modeling the distance between binding sites, we used the Grantham chemical difference matrix [26].
Although the Hill function is typically used to model cooperative molecular binding, we used it here to describe the apparent cooperativity of PAV activity with respect to UL, allowing us to predict mosaic PAV responses given proteolytic inputs Ii.
Therefore, to directly assess the effect of altered cell-surface uPA on plg-binding capacity, we used a model breast cancer cell line (MCF-7) that has very low constitutive cell-surface uPAR and modulated its expression by PMA stimulation [ 32].
To model potential D3 binding to ERα, we used three-dimensional molecular docking.
The binding models were determined using HT-SELEX, the same method we have used previously to determine specificities of mammalian TFs.
