Bayesian methods for elucidating genetic regulatory networks

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Our group has written a number of papers on Bayesian network inference, application of these methods to problems in systems biology, evaluation of these methods in a simulation framework, and extension and improvement of these methods for problems with small amounts of data.

Motivation: We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation.

SSMs 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 that 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 m RNA 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 article, variational approximations are used to perform the analogous model selection task in the Bayesian context.

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