Strategies to Enhance the Yield of Biocellulose in Gluconacetobacter Xylinus

Gluconacetobacter xylinus is known to produce high quality biocellulose. The biocellulose obtained from Gluconacetobacter xylinus is devoid of the cell wall components which makes it very pure. This highly pure cellulose can be used for many applications including medical and defense applications. This project seeks to enhance biocellulose yields from Gluconacetobacter xylinus strains. Optimizing metabolic and regulatory changes to enhance chemical production is a time-consuming and expensive process. However, this process can be expedited using a genome-scale metabolic model. Metabolic models can evaluate multiple strain design strategies to identify those with the greatest strain improvements. We sequenced the genome of Gluconacetobacter xylinus ATCC 53582 and used this information to build a genome-scale metabolic model of this strain. The growth predictions of the genome-scale metabolic model were consistent with the experimental growth observations performed using Biolog phenotype microarrays. We applied the SimOptStrain algorithm to the metabolic model to identify gene deletion and reaction addition strategies to enhance the yield of bio-cellulose from glucose in the minimal media. The proposed genetic changes are predicted to enhance the biocellulose yields by almost three fold compared to the yields reported for the wildtype strain. Future work will focus on implementing and validating the effects of the metabolic changes experimentally.