Genome-scale metabolic modeling to provide insight into the production of storage compounds during feast–famine cycles of activated sludge

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Courtesy of IWA Publishing

Studying storage metabolism during feast–famine cycles of activated sludge treatment systems provides profound insight in terms of both operational issues (e.g., foaming and bulking) and process optimization for the production of value added by-products (e.g., bioplastics). We examined the storage metabolism (including poly-β-hydroxybutyrate [PHB], glycogen, and triacylglycerols [TAGs]) during feast–famine cycles using two genome-scale metabolic models: Rhodococcus jostii RHA1 (iMT1174) and Escherichia coli K-12 (iAF1260) for growth on glucose, acetate, and succinate. The goal was to develop the proper objective function (OF) for the prediction of the main storage compound produced in activated sludge for given feast–famine cycle conditions. For the flux balance analysis, combinations of three OFs were tested. For all of them, the main OF was to maximize growth rates. Two additional sub-OFs were used: (1) minimization of biochemical fluxes, and (2) minimization of metabolic adjustments (MoMA) between the feast and famine periods. All (sub-)OFs predicted identical substrate–storage associations for the feast–famine growth of the above-mentioned metabolic models on a given substrate when glucose and acetate were set as sole carbon sources (i.e., glucose–glycogen and acetate–PHB), in agreement with experimental observations. However, in the case of succinate as substrate, the predictions depended on the network structure of the metabolic models such that the E. coli model predicted glycogen accumulation and the R. jostii model predicted PHB accumulation. While the accumulation of both PHB and glycogen was observed experimentally, PHB showed higher dynamics during an activated sludge feast–famine growth cycle with succinate as substrate. These results suggest that new modeling insights between metabolic predictions and population ecology will be necessary to properly predict metabolisms likely to emerge within the niches of activated sludge communities. Nonetheless, we believe that the development of this approach will help guide the optimization of the production of storage compounds as valuable by-products of wastewater treatment.

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