S105: Extracting knowledge during pathway optimization: quantitative expression-flux mapping and optimal criteria

Synthetic metabolic pathways enable microbes to manufacture high-value chemicals from low-value feedstock. A key challenge is to identify the optimal enzyme expression levels that maximize a pathway's productivity. New quantitative theory and experimental techniques are needed to simultaneously optimize pathways  while generating the necessary knowledge to understand, predict, and control metabolic fluxes for a variety of future applications. To create this quantitative theory, we use our recently developed methods - the RBS Calculator and the RBS Library Calculator - to quantitatively control and uniformly vary protein expression across a 100,000-fold scale.

We have developed a novel approach to systematically optimize a synthetic metabolic pathway while determining the relationship between its regulatory DNA sequences, enzyme expression levels, and metabolic flux. We call this relationship a Quantitative Sequence-Expression-Flux Map (QSEF Map). We experimentally demonstrate QSEF Mapping on a carotenoid biosynthesis pathway in Escherichia coli showing that: (i) characterizing only 100 pathway variants is sufficient to construct the  4-dimensional QSEF Map; (ii) the QSEF Map identifies the optimal enzyme expression levels to achieve a targeted metabolic flux; (iii) the QSEF Map is consistent with existing theory from Metabolic Control Analysis; and (iv) the absolute maximum pathway flux for a given pathway may be calculated, given a cellular constraint.  

The QSEF Map encapsulates the knowledge generated during metabolic pathway optimization, eliminating the need to "start from scratch" when the genetic context of the pathway has changed. It is a necessary step towards creating synthetic genomes with rationally designed metabolic networks.