Clone Less, Know More: Rational Circuit and Pathway Engineering with Quantitative Sequence-Expression-Activity Maps

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Iman Farasat, Jason Collens, Howard Salis *

Penn State University, United States

The genetic tuning of circuits and pathways to hit their “sweet spot” remains a key bottleneck of the design-build-test cycle of Synthetic Biology, and will continue to stymie efforts to engineer more complicated genetic systems. New approaches are needed that systematically identify the optimal DNA sequence to carry out a targeted activity, while performing the fewest number of characterization experiments. We present a new model-designed, experimental approach that efficiently maps the relationship between a genetic system’s DNA sequence, protein expression levels, and system activity (QSEA Map), combining our next-generation RBS Calculator v1.2 with an optimal search algorithm. Using QSEA Maps, synthetic biologists can design synthetic sequences to rationally navigate the multi-dimensional protein expression level space and to target regions with a desired behavior. We first demonstrate our approach on a collection of NOT gates, enabling the rapid connection of multiple genetic circuits to create multi-layer logical programs. We then employ QSEA Mapping to optimize a three enzyme biosynthesis pathway, using fewer than 100 characterization experiments to chart its pathway activity space, across a 1000-fold range, and to design synthetic pathways with maximized activities. QSEA Maps encapsulate many possible behaviors of a genetic module, and predicts the sequences that control its function, revolutionizing our ability to rationally connect circuits and pathways together, and promising to dramatically reduce our cloning efforts.