Title: "Approximate Bayesian approaches for reverse-engineering networks from time-course gene expression data"
Speaker: Andrea Rau, Department of Statistics, Purdue University
Place: Physics (PHYS) 112; April 13, 2010, Tuesday, 4:30pm


Genes are known to interact with one another through proteins (such as transcription factors) by regulating the rate at which gene transcription takes place. Identifying these gene-to-gene interactions is essential to improving our knowledge of how complex biological systems work. In recent years, a growing body of work has focused on methods for reverse-engineering these so-called gene regulatory networks from time-course gene expression data. However, reconstruction of these networks is often complicated by the large number of genes potentially involved in a given network and the limited number of time points and biological replicates typically measured.

Bayesian methods are particularly well-suited for dealing with problems of this nature as they provide a systematic way to deal with different sources of variation and allow for a measure of uncertainty in parameter estimates through posterior distributions, rather than point estimates. Our current work examines the application of approximate Bayesian methodology in the context of Dynamic Bayesian Networks, which are directed graphical models of stochastic processes. We demonstrate the advantages of our proposed approaches by comparing their performance with that of previously established methods.

Associated reading:

Rau et al. (2010) An empirical Bayesian method for estimating biological networks from temporal microarray data. Statistical Applications in Genetics and Molecular Biology 9: 1-28.

Marjoram et al. (2003) Markov chain Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences 100: 15324-15328.

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