432491 Expanding the Genome Engineering Toolkit: Increasing Signal to Noise

Sunday, November 8, 2015
Exhibit Hall 1 (Salt Palace Convention Center)
Nicholas R. Sandoval, Chemical and Biomolecular Engineering, University of Delaware, Newark, DE

Sequencing and synthesis of DNA have plummeted in price (5¢/Mb and 23¢/base, respectively), making genome engineering and analysis quicker and cheaper than ever before. Synthetic biology technologies are advancing rapidly, making it easier to manipulate cellular machinery on the genome and regulatory level. My research objective is to use directed evolution to engineer microbes for desired functions, to develop advanced synthetic biology tools, and to apply high throughput methods of analysis. A powerful strategy is to generate an initial genetic diversity (from the gene to genome level) and then select or screen for the desired trait. In the past, both the genetic diversity created and the biological mechanisms of the resultant phenotype would be black boxes, but now it is possible with next generation techniques to construct libraries with specific mutations and subsequently characterize whole genomes and transcriptomes for individuals as well as populations.

The question now turns to what/how/why mutations are to be made and analyzed for a biotechnologically relevant outcome. That is, the aims of my efforts are to expand the relevant search space to explore as many functional variations as possible. This means rethinking what the search space comprises in order to boost signal and decrease noise: reduce the irrelevant and redundant, expand the functional search space in the initial population, and develop methods to deconvolute signal from noise in the post-screen analysis. Non-model organisms and the metagenome are rich sources of enzymatic capability that are largely unexplored due to the difficulties in genome manipulation and/or difficulties in heterologous expression in a genetically tractable organism. I aim to develop a research group that expands the genome engineering toolkit to include high-throughput, quantitative methods for the greater exploration of functional capabilities in non-model organisms. Furthermore, I will apply methods on the DNA, RNA, protein, and metabolite levels to elucidate biologically relevant mechanisms.

Research Experience:

Postdoctoral Research Fellow, University of Delaware

Mentor: Prof. Eleftherios T. Papoutsakis

Award: NIH Ruth L. Kirschstein Postdoctoral National Research Service Award (NRSA)

Projects:  Enhanced production of butanol from waste glycerol with C. pasteurianum; Heterologous sigma factor expression for metagenomic library screening using flow cytometry; Engineering Synthetic Methylotrophy in E. coli

PhD Dissertation, University of Colorado Boulder

Advisor: Prof. Ryan T. Gill

Award: NSF Graduate Research Fellow

Thesis: Genome Engineering to Improve Acetate and Cellulosic Hydrolysate Tolerance in E. coli for Improved Cellulosic Biofuel Production

Teaching Experience:

Lecturer – Colorado Mesa University – University of Colorado Boulder Mechanical Engineering Partnership Program

Courses: Thermodynamics, Heat Transfer, Dynamics, Measurements Lab

Graduate Student Teaching Fellow – University of Colorado Boulder, Chemical and Biological Engineering

Course: Material and Energy Balances

Extended Abstract: File Uploaded