Event
Joint Bioengineering/ChBE Seminar: Ryan Gill
Friday, December 10, 2010
11:00 a.m.-12:00 p.m.
Room 2108, Chemical and Nuclear Engineering Bldg.
Professor Ray Adomaitis
adomaiti@umd.edu
Genome Surfing for Sustainable Fuels and Chemicals
Ryan Gill (M.S. '97, Ph.D. '99, chemical engineering)
Patten Associate Professor
Department of Chemical and Biological Engineering
University of Colorado
Managing Director
Colorado Center for Biorefining and Biofuels
A fundamental goal in metabolic engineering is to develop approaches for the more effective searching of genome-space for the purpose of re-engineering relevant traits through combinatorial gene modifications. The challenge is that microbial genomes encode a mutational space that far exceeds what can be searched in laboratory settings. Directed evolution efforts have focused intently on this issue, yet these methods have not previously been extended beyond the level of individual genes. Recent advances in synthetic biology and multiplex recombineering have now extended these concepts to the level of combinations of genes comprising entire metabolic pathways. Here, we will describe an approach that expands upon these efforts through the incorporation of molecular barcoding technology and the extension of multiplex recombineering to the entire Escherichia coli genome. We have demonstrated TRackable Multiplex Recombineering (TReMR) for the rapid mapping of phenotypic landscapes that facilitates the more effective searching of combinatorial genomic diversity. TReMR replaces every promoter in the E. coli genome with a molecular barcoded heterologous promoter that either turns on or turns off translation of the downstream gene. Each unique molecular barcode can be tracked in the transformant population, thus allowing rapid and quantitative mapping of modifications in gene expression to selectable and screenable traits. We have applied this approach to rapidly map gene expression alterations affecting E. coli growth in rich or minimal media as well as in the presence of several well characterized growth inhbitors (β-gluocoside, d-fucose/L-arabinose, valine, methylglyoxal, and tetracycline). We are currently applying this method along with modifications that enable identification of optimized gene combinations to investigate a range of traits of commercial relevance; such as chemical, antimicrobial, and stress tolerance phenotypes among others.
