Deconvolving high-throughput sequencing data to map gene regulatory networks

Abstract:
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Developing in silico organisms (i.e. accurate computational models of
biological cells) is an exciting, core endeavor of systems biology.  And an
essential component in such in silico organisms is a reconstruction of the gene
regulatory network--the network that governs how certain genes regulate the
expression of certain other genes.  

In this talk, we discuss how deconvolution, coupled with the high-throughput
data obtained from next-generation sequencing technologies, can be used to map
gene regulatory networks at very high resolution, finding the locations where
regulatory transcription factors bind to DNA to high accuracy even when such
locations are closely located (separated by as few as 57 bp).  

As the price of sequencing continues to drop, such computational techniques
that allow the data to be effectively utilized are likely to play an
increasingly important role.