A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription

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, there...

• Type: Journal
• Year: 2006
• Where: BIOINFORMATICS
• Authors: Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence
• Keywords: BIOINFORMATICS 2006, Expression Levels, Target Genes, Transcription Factor