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Bioinformatics Advance Access published online on September 7, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti014
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received December 1, 2004
Revised August 30, 2004
Accepted August 31, 2004

Article

A Bayesian approach to reconstructing genetic regulatory networks with hidden factors

Matthew J. Beal 1, Francesco Falciani 2, Zoubin Ghahramani 3, Claudia Rangel 4, and David L. Wild 4*

1 Department of Computer Science & Engineering, State University of New York at Buffalo, 201 Bell Hall, Buffalo, NY, 14260-2000, USA
2 School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
3 Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London, WC1N 3AR, UK
4 Keck Graduate Institute of Applied Life Sciences, 535 Watson Drive, Claremont, CA, 91171, USA

* To whom correspondence should be addressed. E-mail: David_Wild{at}kgi.edu.


  Abstract

Motivation: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. State-space models are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects which cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation etc.

Results: We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods. In our previous work, a bootstrap procedure was used to derive classical confidence intervals for parameters representing ‘gene-gene’ interactions over time. In this paper variational approximations are used to perform the analogous model selection task in the Bayesian context. Certain interactions are present in both the classical and the Bayesian analyses of these regulatory networks. The resulting models place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation. These mechanisms are key for clonal expansion and for controlling the long term behavior (e.g. programmed cell death) of these cells.

Availability: Supplementary data is available at http://public.kgi.edu/~wild/index.htm and Matlab source code for variational Bayesian learning of state-space models is available at http://www.cs.toronto.edu/~beal/software.html.


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