Bayesian model-based inference of transcription factor activity

Background: In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true activity of the protein it produces. There are many instances where this is inaccurate due to post-translational modifications of the transcription factor protein. Inference of the activity of the transcription factor from the expression of its targets has predominantly involved linear models that do not reflect the nonlinear nature of transcription. We extend a recent approach to inferring the transcription factor activity based on nonlinear MichaelisMenten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed. Results: We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the mod...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Simon Rogers, Raya Khanin, Mark Girolami
• Keywords: BMCBI 2007, Microarray Data, Transcription Factor, Transcription Factor Activity