Process‐centric and data‐centric strategies for enhanced production of l‐asparaginase—an anticancer enzyme, using marine‐derived Aspergillus niger

Abstract

The objective of the study was to achieve enhanced production of l‐asparaginase (LA), an anticancer enzyme, by a marine‐derived Aspergillus niger isolate. To improve LA production, optimization of pH, incubation time, and inoculum size was performed using process‐centric (response surface methodology [RSM]) and data‐centric (artificial neural network [ANN]) approaches. The optimized conditions led to a 108.62% rise in LA production. Upon comparison of 2 models for enhanced LA production, based on the R2, mean absolute percentage error, root mean square error, and mean absolute deviation values, the ANN model was observed to be superior over the RSM model. To the authors' best knowledge, the present finding is the first ever report revealing detailed analyses for RSM and ANN models for LA production using a marine‐derived fungus.

Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant Bacillus licheniformis PPD37

“…The optimum conditions for L-asparaginase production were predicted by creating a linear feed-forward ANN model using MATLAB R2018a software [ 3 9 -41]. The feed-forward model also known as multi-layer perceptron (MLP) has a gradient descent backpropagation (BP) learning algorithm and has been used expansively for biological applications [22,40,42]. BP algorithm performs this task by minimizing the error of changing weights that are inversely proportional to the negative error gradient.…”

Artificial Neural Network modelling for yield enhancement of L-Glutaminase free L-Asparaginase from Bacillus sp. associated with marine red alga Gracilaria dura

“…Erva et al (2016) reported 18.35 IU ml -1at an optimum pH 6, temperature 33 º C with 40 hrs incubation from Enterobacter aerogenes. The LA from the marine fungal source Aspergillus niger was studied for yield enhancement studies byVala et al (2018b). Initial pH, incubation period, and inoculum size were the parameters considered for optimization.…”

“…This study resulted in a percentage increase of 108.62 % up on pH 4, 6 d incubation and inoculum volume of 1.5 ml through RSM and ANN models. Further the efficacy of ANN optimization over the traditional RSM -CCD model was also complemented in the study(Vala et al 2018b). Parameters like dextrose, pH, and yeast extract were identified as the key elements that influence LA production in the actinomycete Streptomyces rochei subsp.…”