Transomics data-driven, ensemble kinetic modeling for system-level understanding and engineering of the cyanobacteria central metabolism.

In silico kinetic modeling is an essential tool for rationally designing metabolically engineered organisms based on a system-level understanding of their regulatory mechanisms. However, an estimation of enzyme parameters has been a bottleneck in the computer simulation of metabolic dynamics. In this study, the ensemble-modeling approach was integrated with the transomics data to construct kinetic models. Kinetic metabolic models of a photosynthetic bacterium, Synechocystis sp. PCC 6803, were constructed to identify engineering targets for improving ethanol production based on an understanding of metabolic regulatory systems. A kinetic model ensemble was constructed by randomly sampling parameters, and the best 100 models were selected by comparing predicted metabolic state with a measured dataset, including metabolic flux, metabolite concentrations, and protein abundance data. Metabolic control analysis using the model ensemble revealed that a large pool size of 3-phosphoglycerate could be a metabolic buffer responsible for the stability of the Calvin-Benson cycle, and also identified that phosphoglycerate kinase (PGK) is a promising engineering target to improve a pyruvate supply such as for ethanol production. Overexpression of PGK in the metabolically engineered PCC 6803 strain showed that the specific ethanol production rate and ethanol titers at 48h were 1.23- and 1.37-fold greater than that of the control strain. PGK is useful for future metabolic engineering since pyruvate is a common precursor for the biosynthesis of various chemicals.