5-31: JBEI Computational Biology Core

Monday, May 4, 2009
InterContinental Ballroom (InterContinental San Francisco Hotel)
Dylan Chivian , Technology Division, Joint BioEnergy Institute, Emeryville, CA
Paramvir Dehal , Virtual Institute for Microbial Stress and Survival, Berkeley, CA
Marcin Joachimiak , Virtual Institute for Microbial Stress and Survival, Berkeley, CA
Keith Keller , Virtual Institute for Microbial Stress and Survival, Berkeley, CA
Morgan Price , Virtual Institute for Microbial Stress and Survival, Berkeley, CA
Jason Baumohl , Virtual Institute for Microbial Stress and Survival, Berkeley, CA
John Bates , Technology Division, Joint BioEnergy Institute, Emeryville, CA
Adam Arkin , Technology Division, Joint BioEnergy Institute, Emeryville, CA
Background:  The Computational Biology Core group in the Technology Division of the Joint BioEnergy Institute (JBEI) is responsible for data integration and comparative, evolutionary, and functional genomic analysis for the purpose of enabling metabolic engineering for biofuel production.  Leveraging the VIMSS MicrobesOnline website and database (http://www.microbesonline.org) for comparative and evolutionary genomics and analysis of microarray, proteomic, and metabolomic data sets, we are extending and integrating these capabilities to allow for pursuit of questions specific to biofuels challenges. 

The MicrobesOnline Database:  We have extended the MicrobesOnline database to include eukaryotic microbes that may be useful for understanding the process of biologically-mediated degradation of plant cell walls.  Building a more complete picture of the enzymes nature employs for breaking down plant biomass is essential for developing industrial processes for biofuel production, and as such we are developing computational tools for metagenomics to permit searching for and comparative analysis of such enzymes in natural microbial communities.  We are also working to combine computational structural biology with evolutionary analysis to grasp the mechanistic details of such “deconstruction” enzymes.  This will permit prediction and engineering of novel enzymes with enhanced activities and custom specificities with an eye toward building a library of parts for metabolic engineering.  Our efforts also include the study of the regulation of the expression and activity of lignocellulose deconstruction enzymes.  Finally, we are extending the visualization and analysis tools in MicrobesOnline to provide a pathway-based view of systems to permit integrated analysis of systems biology data to facilitate metabolic engineering.