432879 Pathway Engineering in Yeast: Overcoming Challenges in Design and Optimization By Scaling and Parallelizing Elements of the Design-Build-Test-Learn Cycle

Sunday, November 8, 2015
Exhibit Hall 1 (Salt Palace Convention Center)
Eric M. Young1,2, Johannes A. Roubos3, Ben Meijrink3, D. Benjamin Gordon2 and Christopher A. Voigt1,2, (1)Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, (2)MIT-Broad Foundry, Broad Technology Labs, Broad Institute of MIT and Harvard, Cambridge, MA, (3)DSM Biotechnology Center, Delft, Netherlands

To achieve the goal of rapid and reliable pathway optimization in yeast, strategies for a fast and standardized design-build-test-learn cycle must be developed.  Herein, we describe an approach to yeast engineering by large-scale assembly of combinatorial pathway variants.  To enable this approach, a semi-automated platform for yeast parts characterization, semi-automated assembly of pathway variants, and computational modeling for part and pathway performance were developed. 

These developments were applied to understanding the behavior and bottlenecks of a six-gene pathway in Saccharomyces cerevisiae.  In the process, over a thousand individual yeast transcription units were characterized for expression and nearly two hundred pathway variants constructed in the first combinatorial library.  With these advances in the parallelization and scale of pathway engineering, effects and interactions that were not previously possible to characterize may be observed.  Specifically, eukaryotic promoters and terminators both influence gene expression, thus understanding the interactions between them are essential for controlling pathway protein levels.  As a result of the large-scale parts characterization, we have found that genome-integrated transcription unit expression of GFP can be modeled with a simple second-order expression.  Furthermore, by constructing many pathway variants in parallel, enzymes that must be overexpressed for pathway function are easily identifiable, and interdependencies in pathway gene expression may be observed.

With these advances, we demonstrate a design-build-test-learn cycle that can be generally applied to any multi-gene system in yeast.

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