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Today’s possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream ¹³C labeling dynamics with respect to a metabolite of interest. Showcasing the complex <span style="font-variant: small-caps;">l</span>-histidine production with engineered <i>Corynebacterium glutamicum</i><span style="font-variant: small-caps;">l</span>-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of <i>purA</i>, <i>purB</i>, <i>purH,</i> and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values.