Inferring genetic regulatory logic from expression data

Open Access

22 March 2005

journal article
research article
Published by Oxford University Press (OUP) in Bioinformatics

Vol. 21 (11) , 2706-2713
https://doi.org/10.1093/bioinformatics/bti388

Abstract

Motivation: High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an insight into the mechanisms of genetic regulation. Results: We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge. Availability: The code for the software BUGS is available upon request. Contact:s.bulashevska@dkfz.de Supplementary information:http://oslo.inet.dkfz-heidelberg.de/ibios_old/people/bulashev/Supplement/

Keywords

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