Artificial neural network‐genetic algorithm (ANN‐GA) based medium optimization for the production of human interferon gamma (hIFN‐γ) in Kluyveromyces lactis cell factory

Artificial neural network‐genetic algorithm (ANN‐GA) based medium optimization for the production of human interferon gamma (hIFN‐γ) in Kluyveromyces lactis cell factory

In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network‐genetic algorithm (ANN‐GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis). In the initial...

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Bibliographic Details
Published in:Canadian journal of chemical engineering Vol. 97; no. 4; pp. 843 - 858
Main Authors: Unni, Silpa, Prabhu, Ashish A., Pandey, Rajat, Hande, Rohit, Veeranki, Venkata Dasu
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 01-04-2019
Subjects:
ANN‐GA
Artificial neural networks
Box‐Behnken
Genetic algorithms
Glycine
hIFN‐γ
Interferon
K. lactis
Microorganisms
Model testing
Neural networks
Optimization
Parameter identification
Proteins
Reaction kinetics
Response surface methodology
Sodium hydrogen phosphate
Sorbitol
Substrate inhibition
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Summary:In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network‐genetic algorithm (ANN‐GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis). In the initial screening studies, sorbitol and glycine emerged as a carbon and nitrogen source respectively having higher influence on hIFN‐γ production. Substrate inhibition studies were performed by varying the initial substrate concentration, and we found maximum hIFN‐γ concentration at 50 g L−1 of sorbitol. Inhibition kinetics studies were carried out using 3 and 4‐parametric models. Among the estimated models, the Moser model was observed as the best fitted model followed by the Luong model with R2 values of 0.882 and 0.75, respectively. The model acceptability test was carried out using the extra sum of squares F‐test and Akaike information criterion (AIC). The Plackett‐Burman multifactorial design identified sorbitol, glycine, Na2HPO4, and MgSO4.7H2O as the parameters significantly influencing the hIFN‐γ production. Further, the Box‐Behnken design (BBD) followed by the artificial neural network coupled with genetic algorithm (ANN‐GA) was employed for the precise optimization of medium components. With ANN‐GA a maximum hIFN‐γ yield of 2.1 ±0.3 mg L−1 in shake flask level and 3.5 ±0.1 mg L−1 in reactor level was achieved. The findings of this study serve as a model for a process development strategy (bench scale to reactor scale) to achieve a high productivity of the desired protein from a microbial cell factory.
ISSN:0008-4034
1939-019X
DOI:10.1002/cjce.23350