Engineered metabolic pathways often suffer from flux imbalances that can overburden the cell and accumulate intermediate metabolites, resulting in reduced flux.  One method for alleviating these imbalances is to adjust the expression levels of each pathway enzyme in a combinatorial manner.  We have developed a method for rapidly assembling combinatorial expression libraries and applied this towards the optimization of both xylose utilization as well as production of compounds in the highly branched violacein biosynthetic pathway in S. cerevisiae.  Xylose comprises approximately 30% of the total biomass and accordingly has attracted a great deal of attention for optimization.  We explore the benefits of expression co-optimization of the entire pathway under different relevant conditions using growth as a selection to enrich for optimal genotypes.  The branched violacein pathway was used as model for pathways where a selection is not available.  We tested the efficacy of employing a linear regression modeling approach on a small training set of samples to predict what expression combinations in the overall library is optimal such that a manageable number of strains can be synthesized and tested.  This approach promises to be valuable for biofuel biosynthetic pathways that lack a high-throughput screen or selection.

Expression optimization of multi-enzyme pathways for xylose utilization and chemical production

247th National Meeting (2014)

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