Medium optimization for the production of penicillin V acylase fromBacillus sphaericus

Novel chemometric strategy based on the application of artificial neural networks to crossed mixture design for the improvement of recombinant protein production in continuous culture

The optimal blends of six compounds that should be present in culture media used in recombinant protein production were determined by means of artificial neural networks (ANN) coupled with crossed mixture experimental design. This combination constitutes a novel approach to develop a medium for cultivating genetically engineered mammalian cells. The compounds were collected in two mixtures of three elements each, and the experimental space was determined by a crossed mixture design. Empirical data from 51 experimental units were used in a multiresponse analysis to train artificial neural networks which satisfy different requirements, in order to define two new culture media (Medium 1 and Medium 2) to be used in a continuous biopharmaceutical production process. These media were tested in a bioreactor to produce a recombinant protein in CHO cells. Remarkably, for both predicted media all responses satisfied the predefined goals pursued during the analysis, except in the case of the specific growth rate (mu) observed for Medium 1. ANN analysis proved to be a suitable methodology to be used when dealing with complex experimental designs, as frequently occurs in the optimization of production processes in the biotechnology area. The present work is a new example of the use of ANN for the resolution of a complex, real life system, successfully employed in the context of a biopharmaceutical production process.

Optimization of actinomycin V production by Streptomyces triostinicus using artificial neural network and genetic algorithm

Artificial neural network (ANN) and genetic algorithm (GA) were applied to optimize the medium components for the production of actinomycin V from a newly isolated strain of Streptomyces triostinicus which is not reported to produce this class of antibiotics. Experiments were conducted using the central composite design (CCD), and the data generated was used to build an artificial neural network model. The concentrations of five medium components (MgSO(4), NaCl, glucose, soybean meal and CaCO(3)) served as inputs to the neural network model, and the antibiotic yield served as outputs of the model. Using the genetic algorithm, the input space of the neural network model was optimized to find out the optimum values for maximum antibiotic yield. Maximum antibiotic yield of 452.0 mg l(-1) was obtained at the GA-optimized concentrations of medium components (MgSO(4) 3.657; NaCl 1.9012; glucose 8.836; soybean meal 20.1976 and CaCO(3) 13.0842 gl(-1)). The antibiotic yield obtained by the ANN/GA was 36.7% higher than the yield obtained with the response surface methodology (RSM).

A statistical approach to study the interactive effects of process parameters on succinic acid production from Bacteroides fragilis

A statistical approach response surface methodology (RSM) was used to study the production of succinic acid from Bacteroides fragilis. The most influential parameters for succinic acid production obtained through one-at-a-time method were glucose, tryptone, sodium carbonate, inoculum size and incubation period. These resulted in the production of 5.4gL(-1) of succinic acid in 48h from B. fragilis under anaerobic conditions. Based on these results, a statistical method, face-centered central composite design (FCCCD) falling under RSM was employed for further enhancing the succinic acid production and to monitor the interactive effect of these parameters, which resulted in a more than 2-fold increase in yield (12.5gL(-1) in 24h). The analysis of variance (ANOVA) showed the adequacy of the model and the verification experiments confirmed its validity. On subsequent scale-up in a 10-L bioreactor using conditions optimized through RSM, 20.0gL(-1) of succinic acid was obtained in 24h. This clearly indicated that the model stood valid even on large scale. Thus, the statistical optimization strategy led to an approximately 4-fold increase in the yield of succinic acid. This is the first report on the use of FCCCD to improve succinic acid production from B. fragilis. The present study provides useful information about the regulation of succinic acid synthesis through manipulation of various physiochemical parameters.