Erin Wilson, Qualifying Project Presentation

Synthetic biology is a growing field that develops novel biological systems to solve pressing global challenges. One goal of synthetic biology is to engineer microbes into sustainable biomolecule factories: by integrating foreign genes that encode a metabolic pathway to generate a molecule of interest, an engineered microorganism can be used to biologically convert renewable feedstocks into valuable materials. While there have been successful efforts using sugar-consuming model organisms, such as Saccharomyces cerevisiae or Escherichia coli, there is a strong case for developing an industrial production platform in a bacterium such as Methylomicrobium buryatense 5GB1, which instead consumes methane, a potent greenhouse gas second only to CO2 in its contributions to anthropogenic climate change. However, incorporating new engineering that optimally functions within a host microbe is extremely difficult without knowledge of the sequences controlling gene expression, which is in effect the genetic grammar. To better understand the DNA sequence motifs and regulatory patterns that govern M. buryatense gene expression, we have curated an ensemble of RNA-seq datasets collected across diverse growth conditions. Here we describe our computational exploration of the data which revealed unexpected ncRNA and tmRNA expression patterns, our strategy to select a candidate set of top genes near which to search for sequence patterns that confer strong expression, and our evaluation of a simplified promoter extraction method using n-gram language modeling techniques inspired by the field of natural language processing.