Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities

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. Recent experimental high-throughput techniques such as Chromatine Immunoprecipitation provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data. Results We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of pr...

• Type: Journal
• Year: 2006
• Where: BIOINFORMATICS
• Authors: Guido Sanguinetti, Neil D. Lawrence, Magnus Rattray
• Keywords: BIOINFORMATICS 2006, Regulatory, Transcription Factor, Transcription Factor Protein