Computational Biology / Bioinformatics Core Group
Mission
The Computational Biology / Bioinformatics Core Group enables JBEI scientists to study the components, systems, organisms, communities, and their metabolic networks using comparative phylogenetic genomics and functional analysis of systems biology data. Our aim is to understand systems at a level to permit engineering of the enzymes, strains, and even community structure to promote desirable properties such as lignocellulose degradation and biofuel production. We provide expert collaboration on the design and analysis of experiments towards these goals and provide useful analytical tools and disseminate data through the resources MicrobesOnline, metaMicrobesOnline, and GLAMM.
Research and Development
MicrobesOnline (www.microbesonline.org) provides comparative genomic and functional analysis tools to understand the systems biology of Bacteria, Archaea, and Fungi. We are using MicrobesOnline to study the tolerance of organisms to biofuel molecules, why certain strains are better suited for metabolic engineering of biofuel pathways, the phylogenetic history of lignocellulose degrading enzymes, ways of engineering enzymes more suitable for industrial conditions, and other similar studies.
metaMicrobesOnline (meta.microbesonline.org) offers phylogenetic analysis of millions of genes from isolates with environmental genomic sequence data to facilitate functional annotation and comparative analysis.
metaMicrobesOnline's phylogenetic gene trees offer the evolutionary history of enzymes discovered directly from the environment and permit comparison of environmental sequences with one another and with those from more complete isolate genomes. Additionally, the domain architecture of the genes is easily analyzed. We are using metaMicrobesOnline to analyze lignocellulose degrading enzymes and species found in rainforest and compost communities that are efficient degraders of plant biomass.
GLAMM (www.microbesonline.org/cgi-bin/glamm), the "Genome-Linked Application for Metabolic Maps" allows a user to visualize functional data on metabolic networks and facilitates the determination of pathways and genes for retrosynthesis of biofuel molecules.
Representing functional data on metabolic networks can aid a researcher in rapid hypothesis generation when determining the cell-wide impact on genetic perturbations or the metabolic response to altered conditions, such as increased concentrations of toxic biofuel product.
Determining viable pathways that can be engineered from a base metabolic network to a desired target product requires identifying which genes can accomplish the necessary chemical transformations. GLAMM allows the user to select a base "host" organism, such as E. coli, and finds pathways between molecules, providing candidates for the necessary genes from other organisms to consider for transgenic engineering into the host, completing the pathway. The researcher can then utilize the functional data features of GLAMM to analyze the impact of their engineered pathway on the host metabolism and the biofuel yield.
Links
- MicrobesOnline (www.microbesonline.org)
- metaMicrobesOnline (meta.microbesonline.org)
- GLAMM (www.microbesonline.org/cgi-bin/glamm)
- The Virtual Institute for Microbial Stress and Survival (vimss.lbl.gov)
- Arkin Laboratory for Systems and Synthetic Biology (genomics.lbl.gov)
People
- Adam Arkin, Director
- Dylan Chivian, Lead Scientist
- John Bates
- Greg Friedland
- Vinay Satish Kumar
