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This article is part of the supplement: Probabilistic Modeling and Machine Learning in Structural and Systems Biology

Open Access Research

Bayesian model-based inference of transcription factor activity

Simon Rogers1*, Raya Khanin2 and Mark Girolami1

Author Affiliations

1 Bioinformatics Research Centre, Department of Computing Science, University of Glasgow, Glasgow, UK

2 Department of Statistics, University of Glasgow, Glasgow, UK

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BMC Bioinformatics 2007, 8(Suppl 2):S2  doi:10.1186/1471-2105-8-S2-S2

Published: 3 May 2007



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 Michaelis-Menten kinetics of transcription from maximum likelihood to fully Bayesian inference and give an example of how the model can be further developed.


We present results on synthetic and real microarray data. Additionally, we illustrate how gene and replicate specific delays can be incorporated into the model.


We demonstrate that full Bayesian inference is appropriate in this application and has several benefits over the maximum likelihood approach, especially when the volume of data is limited. We also show the benefits of using a non-linear model over a linear model, particularly in the case of repression.