Bioinformatics Advance Access originally published online on April 21, 2006
Bioinformatics 2006 22(14):1753-1759; doi:10.1093/bioinformatics/btl154
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A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
1 Department of Computer Science Regent Court, 211 Portobello Road, Sheffield S1 4DP, UK
2 School of Computer Science, University of Manchester Oxford Road, Manchester M13 9PL, UK
*To whom correspondence should be addressed.
Motivation: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. This task, however, is difficult for a number of reasons: transcription factors’ expression levels are often low and noisy, and many transcription factors are post-transcriptionally regulated. It is therefore useful to infer the activity of the transcription factors from the expression levels of their target genes.
Results: We introduce a novel probabilistic model to infer transcription factor activities from microarray data when the structure of the regulatory network is known. The model is based on regression, retaining the computational efficiency to allow genome-wide investigation, but is rendered more flexible by sampling regression coefficients independently for each gene. This allows us to determine the strength with which a transcription factor regulates each of its target genes, therefore providing a quantitative description of the transcriptional regulatory network. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates of the activities. We demonstrate our model on two yeast datasets. In both cases the network structure was obtained using chromatin immunoprecipitation data. We show how predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell.
Availability: MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma
Contact: guido{at}dcs.shef.ac.uk
Supplementary information: Supplementary data are available on Bioinformatics online.
Received on January 23, 2006; revised on April 13, 2006; accepted on April 19, 2006
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