(96c) Reusable Pathway Optimization with Quantitative Sequence-Expression-Flux Maps

Metabolic pathway optimization commonly relies on trial-and-error mutagenesis to vary enzyme expression levels and maximize pathway flux.  By combining biophysical models of translation initiation and advanced optimization algorithms, we present a new approach to pathway optimization that efficiently characterizes the relationship between enzyme expression level and pathway flux and identifies the optimal stoichiometries in the pathway, while using a minimal number of characterization experiments. We utilize this data to generate a Quantitative Sequence-Expression-Flux Map (QSEF Map) that enables one to rationally control the flux through a pathway for diverse applications without requiring pathway optimization to be performed again "from scratch".   We experimentally demonstrate this new approach, called QSEF Mapping, on a 3-enzyme carotenoid biosynthesis pathway. In the process, we efficiently explore the three-dimensional enzyme expression level space across a 500,000-fold range, rationally control pathway flux over a 600-fold range, and achieve a maximal carotenoid titer of at least 20 mg/g cell dry weight in 7 hours.