Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities.
BACKGROUND
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 Chromatin Immunoprecipitation (ChIP) 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 protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast datasets in which the network structure has previously been obtained using ChIP data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell.


BACKGROUND
MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma
Sanguinetti
Guido
G
Department of Computer Science, Regent Court 211 Portobello Road, Sheffield, S1 4DP, UK. guido@dcs.shef.ac.uk
Lawrence
Neil D
ND
Rattray
Magnus
M
D000465:Algorithms; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D020407:Internet; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011336:Probability; D050436:Regulatory Elements, Transcriptional; D012984:Software; D014157:Transcription Factors / Q000737:chemistry*; D014158:Transcription, Genetic
D014157:Transcription Factors