Robust identification of metabolic control for microbial l-methionine production following an easy-to-use puristic approach

Abstract

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 l-methionine production with recombinant Escherichia coli, PEC consistently and robustly identified main flux controls using perturbation data after a non-labeled ¹²C-l-serine stimulus. Furthermore, the application of full-labeled ¹³C-l-serine stimuli yielded additional insights into stimulus propagation to l-methionine. PEC analysis performed on the ¹³C data set revealed the same targets as the ¹²C data set. Notably, the typical drawback of metabolome analysis, namely, the omnipresent leakage of metabolites, was excluded using the ¹³C PEC approach.

Keywords: Metabolic control analysis; Quantitative metabolomics; Stimulus response experiment.