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The AR (androgen receptor) belongs to the nuclear receptor superfamily and directly regulates patterns of gene expression in response to the steroids testosterone and dihydrotestosterone. Sequences within the large N-terminal domain of the receptor have been shown to be important for transactivation and protein-protein interactions; however, little is known about the structure and folding of this region. Folding of the AR transactivation domain was observed in the presence of the helix-stabilizing solvent trifluorethanol and the natural osmolyte TMAO (trimethylamine N-oxide). TMAO resulted in the movement of two tryptophan residues to a less solvent-exposed environment and the formation of a protease-resistant conformation. Critically, binding to a target protein, the RAP74 subunit of the general transcription factor TFIIF, resulted in a similar resistance to protease digestion, consistent with induced folding of the receptor transactivation domain. Our current hypothesis is that the folding of the transactivation domain in response to specific protein-protein interactions creates a platform for subsequent interactions, resulting in the formation of a competent transcriptional activation complex.

Statistical methods allow the effects of uncertainty to be incorporated into finite element models. This has potential benefits for the analysis of biological systems where natural variability can give rise to substantial uncertainty in both material and geometrical properties. In this study, a simple model of the intervertebral disc under compression was created and analysed as both a deterministic and a stochastic system. Factorial analysis was used to determine the important parameters to be included in the stochastic analysis. The predictions from the model were compared to experimental results from 21 sheep discs. The size and shape of the distribution of the axial deformations predicted by the model was consistent with the experimental results given that the number of model solutions far exceeded the number of experimental results. Stochastic models could be valuable in determining the range and most likely value of stress in a tissue or implant.