Mapping sequence to gene expression in transcriptional networks

The cell’s control over the context of gene expression is the most important early step in the manifestation of phenotype.  At the level of single genes, elaborate mechanisms have evolved to implement complex and robust programs of mRNA expression.  These programs are coded within the DNA sequence local to the gene through the occurrence and spatial configuration of transcription factor binding sites.  A major challenge of molecular systems biology is to develop computational approaches that utilize genomic datasets of sequence and gene expression patterns to statistically identify these binding sites and to learn the ‘cis-regulatory’ logic rules that are implemented by them.  We are using machine-learning and information-theoretic methods to address these challenges.  In the case of model unicellular microbes such as the yeast, Saccharomyces cerevisiae, we are able to identify regulatory elements and highly predictive combinatorial rules that explain the majority of gene expression variation in microarray data.   We are currently extending our methods to the scale and complexity of animal genomes, including flies, worms, and human.

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