Reconstruction, verification and in-silico analysis of a genome-scale metabolic model of bacterial cellulose producing Komagataeibacter xylinus.

Abstract

In this study, a comprehensive genome-scale metabolic network of Komagataeibacter xylinus as the model microorganism was reconstructed based on genome annotation, for better understanding of metabolic growth and biosynthesis of bacterial cellulose (BC). The reconstructed network included 640 genes, 783 metabolic reactions and 865 metabolites. The model was completely successful to predict the lack of growth under anaerobic conditions. Model validation by the data for the growth of acetic acid bacteria with ethanol-limited chemostat cultures showed that there is a good agreement for the O2 and CO2 fluxes with actual growth conditions. Then the model was used to forecast the simultaneous production of BC and by-products. The obtained data showed that the rate of BC production is consistent with experimental data with an accuracy of 93.7%. Finally, the study of flux balance analysis (FBA) data showed that the pentose phosphate pathway and the TCA cycle play an important role in growth-promoting metabolism in K. xylinus and have a close relationship with BC biosynthesis. By integrating this model with various metabolic engineering and systems biology tools in the future, it is possible to overcome the common challenges in the large-scale BC production, such as low yield and productivity.