Massively Parallel Methods to Map Sequence-Function Relationships in Metabolic Pathways

Massively Parallel Methods to Map Sequence-Function Relationships in

Metabolic Pathways

Tim Whitehead, Justin R. Klesmith, Emily E. Wrenbeck

The synthesis of advanced biofuels from renewable carbon is a pressing need for our country. Metabolic routes have been demonstrated for most important molecules including alcohols, fatty acid esters, alkenes, and even alkanes. However, many of these existing routes suffer from weak productivity and low final product titers. We hypothesize that the specific productivity of synthetic pathways is limited by an incomplete understanding of how flux depends on the concentration of primary enzymes and key metabolites. Partly because of this, small changes in environmental conditions or genetic background of the host strain can result in large and unpredictable decreases in function for implanted, high-flux metabolic pathways. What is needed is a different way to approach the traditional design-build-test cycle of synthetic biology. In this talk I will present a high-throughput experimental method that enables mapping of tens of thousands of growth-associated metabolic pathways across many different genetic or environmental conditions. I will show the utility of the method in tuning robust pathway flux for two different experimental systems. Further, I will show the use of
the method in refining computational dynamic flux models of our experimental systems. Future directions to be discussed in the talk include extending the method to complicated,
multi-gene pathways and extending the methodology to improve flux of non growth-
associated products.