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Bioinformatics Advance Access originally published online on March 22, 2005
Bioinformatics 2005 21(11):2706-2713; doi:10.1093/bioinformatics/bti388
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Inferring genetic regulatory logic from expression data

Svetlana Bulashevska 1,* and Roland Eils 1,2

1Division ‘Theoretical Bioinformatics’, German Cancer Research Center Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
2Department ‘Bioinformatics and Functional Genomics’, Institute of Pharmacy and Molecular Biotechnology (IPMB), University of Heidelberg Germany

*To whom correspondence should be addressed.

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{at}dkfz.de

Supplementary information: http://oslo.inet.dkfz-heidelberg.de/ibios_old/people/bulashev/Supplement/


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[Abstract] [Full Text] [PDF]


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