Robust Identification of Metabolic Engineering Targets Via Pool Efflux Capacities (PECs)
The identification of promising genetic targets for the straightforward optimization of production strains is the epitome of metabolic engineering. The framework of metabolic control analysis provides the mindset for likewise approaches. In dynamic studies the metabolism of strains is properly perturbed for deriving characteristic parameters such as elasticities and flux control coefficients from the dynamic metabolic response. It has been shown that linlog models are a convenient tool for modelling the observed dynamics and for deducing the desired metabolic engineering targets. However likewise approaches demand for profound modelling expertise which may not always be present by wet-lab experts who are supposed to be the main applicants of the results.
Here we present the thorough application of the pool efflux capacity (PEC) criterion. PEC analyzes the observed metabolic dynamics after perturbation via mass balances for the individual pools. Accordingly a series of net efflux rates is calculated for each pool after the stimulus was set. Resulting efflux rates are ranked thus indicating the maximum capacity of the respective enzymes. Consequently high PEC values refer to low flux control and vice versa.
The approach was thoroughly applied for optimizing L-methionine production with a recombinant E. coli strain. It can be shown that PEC identifies the same dominating metabolic engineering targets as conventional methods. Noteworthy no tedious modelling is needed. Target identification is straightforward and purely data driven.
Furthermore an extended PEC approach will be presented integrating 13C-labeling information. It will be shown that experimentalists only have to follow the 13C-to-12C ratio of the respective pools for identifying the same metabolic engineering targets. This finding offers great opportunities for application because it excludes the well-known problems of measuring proper intracellular pool sizes. The latter are often biased by non-wanted cell leakage, a phenomenon that does not affect the 13C-to-12C ratio.
Summarizing PEC analysis offers the straightforward identification of metabolic engineering targets without tedious modelling and without the bias of non-accurate intracellular pool sizes.