Production, characterization and techno-economic evaluation of Aspergillus fusant L-asparaginase.

Protoplast fusion is one of the most reliable methods of introducing desirable traits into industrially-promising fungal strains. It harnesses the entire genomic repertoire of fusing microorganisms by routing the natural barrier and genetic incompatibility between them. In the present study, the axenic culture of a thermo-halotolerant strain of Aspergillus candidus (Asp-C) produced an anti-leukemic L-asparaginase (L-ASNase) while a xylan-degrading strain of Aspergillus sydowii (Asp-S) produced the acrylamide-reduction type.

Interspecific protoplast fusion of atmospheric and room-temperature plasma mutants of generates an L-asparaginase hyper-producing hybrid with techno-economic benefits.

The axenic culture of (Asp-C) produced an anti-leukemic L-asparaginase while (Asp-S) produced the acrylamide-reduction type. Upon mutagenesis by atmospheric and room-temperature plasma (ARTP), their individual L-asparaginase activities improved 2.3-folds in each of IleThrAsp-C-180-K and ValAsp-S-180-E stable mutants.

A novel strain of Stenotrophomonas acidaminiphila produces thermostable alkaline peptidase on agro-industrial wastes: process optimization, kinetic modeling and scale-up.

Bacterial alkaline peptidases, especially from Bacillus species, occupy the frontline in global enzyme market, albeit with poor production economics. Here, we report the deployment of response surface methodology approximations to optimize fermentation parameters for enhanced yield of alkaline peptidase by the non-Bacillus bacterium; Stenotrophomonas acidaminiphila. Shake flask production under optimized conditions was scaled up in a 5-L bench-scale bioreactor.

Overproduction of a thermo-stable halo-alkaline protease on agro-waste-based optimized medium through alternate combinatorial random mutagenesis of .

A strain of , isolated from fermenting bean-processing wastewater, produced alkaline protease in pretreated cassava waste-stream, but with low yield. Strain improvement by alternate combinatorial random mutagenesis and bioprocess optimization using comparative statistical and neural network methods enhanced yield by 17.8-fold in mutant kGy-04-UV-25.

Kinetic modeling and quasi-economic analysis of fermentative glycolipopeptide biosurfactant production in a medium co-optimized by statistical and neural network approaches.

This study presents the kinetics of production of a glycolipopeptide biosurfactant in a medium previously co-optimized by response surface and neural network methods to gain some insight into its volumetric and specific productivities for possible scale-up towards industrial production. Significant kinetic parameters including maximum specific growth rate, µmax, specific substrate consumption rate, qs and specific biosurfactant yield, Yp/x were determined from logistic model parameters after comparison with other kinetic models. Results showed that bio-catalytic rates of lipase and urease reached exponential values within the first 12 h of fermentation leading to high specific rates of substrate consumption and bacterial growth.