초록
<P><B>Abstract</B></P> <P>The identification of promising metabolic engineering targets is a key issue in metabolic control analysis (MCA). Conventional approaches make intensive use of model-based studies, such as exploiting post-pulse metabolic dynamics after proper perturbation of the microbial system. Here, we present an easy-to-use, purely data-driven approach, defining pool efflux capacities (PEC) for identifying reactions that exert the highest flux control in linear pathways. Comparisons with linlog-based MCA and data-driven substrate elasticities (DDSE) showed that similar key control steps were identified using PEC. Using the example of <SMALL>L</SMALL>-methionine production with recombinant <I>Escherichia coli</I>, PEC consistently and robustly identified main flux controls using perturbation data after a non-labeled <SUP>12</SUP>C-<SMALL>L</SMALL>-serine stimulus. Furthermore, the application of full-labeled <SUP>13</SUP>C-<SMALL>L</SMALL>-serine stimuli yielded additional insights into stimulus propagation to <SMALL>L</SMALL>-methionine. PEC analysis performed on the <SUP>13</SUP>C data set revealed the same targets as the <SUP>12</SUP>C data set. Notably, the typical drawback of metabolome analysis, namely, the omnipresent leakage of metabolites, was excluded using the <SUP>13</SUP>C PEC approach.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Metabolic control in <SMALL> <SMALL>L</SMALL> </SMALL>-Met-producing <I>E. coli</I> studied by stimulus response experiments. </LI> <LI> Pool efflux capacity (PEC) criterion was applied to unravel flux control. </LI> <LI> Compared to conventional metabolic control analysis same key control steps are identified. </LI> <LI> Consideration of <SUP>13</SUP>C labeling signals increase information content significantly. </LI> <LI> Enable the easy use of the approach for identifying metabolic engineering targets. </LI> </UL> </P>