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Computational Models for Gene Control Networks in Pseudomonas syringae
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Our group uses a variety of laboratory and computational methods to study regulation of gene expression in an important bacterial plant pathogen, Pseudomonas syringae.  The primary focus of our work is the DC3000 strain of the tomato pathovar that has the unique property of infecting both tomato and Arabidopsis thaliana, a model plant species with a fully sequenced genome.  Since the DC3000 genome is also available [Buell et al, 2003], the DC3000-A. thaliana system is the ideal platform for studying the molecular basis of plant pathogenesis. 

Our particular interest is a systems-level approach to molecular pathogenesis from the viewpoint of the pathogen -- how the bacteria senses its environment and responds to those stimuli.   The genomic sequence of DC3000 is exploited to design integrated experimental methods and associated analytical techniques that shed light on systems-level behavior.  For example, we employ genome-scale microarrays, promoter trapping, transposon reporter screens and shotgun proteomics to detect differential expression coupled with de novo motif identification methods such as Gibbs sampling to infer gene regulation networks and develop specific hypotheses regarding regulatory mechanisms. 

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