Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor

In the current work, the attempt was made to apply best‐fitted artificial neural network (ANN) architecture and the respective training process for predicting final titer of hepatitis B surface antigen (HBsAg), produced intracellularly by recombinant Pichia pastoris Mut+ in the commercial scale. For...

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Vydáno v:Biotechnology and applied biochemistry Ročník 66; číslo 4; s. 681 - 689
Hlavní autoři: Hosseini, Seyed Nezamedin, Javidanbardan, Amin, Khatami, Maryam
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley Subscription Services, Inc 01.07.2019
Témata:
Algorithms
ANN
Antigens
Artificial neural networks
Back propagation
Bayesian analysis
Biomass
Biopharmaceuticals
Bioreactors - economics
Datasets
Fermentation
Growth rate
HBsAg
Hepatitis B
Hepatitis B surface antigen
Hepatitis B Surface Antigens - chemistry
Hepatitis B Surface Antigens - economics
Hepatitis B Surface Antigens - metabolism
large‐scale fermentation
Mathematical analysis
Mut+ Pichia pastoris
Neural networks
Neural Networks, Computer
parameter estimation
Pichia - chemistry
Pichia - metabolism
Pichia pastoris
Predictions
Recombinant Proteins - chemistry
Recombinant Proteins - economics
Recombinant Proteins - metabolism
Regression analysis
Regularization
soft sensor
titer
Yeast
ANN
Mut+ Pichia pastoris
soft sensor
titer
HBsAg
parameter estimation
large-scale fermentation
ISSN:0885-4513, 1470-8744, 1470-8744
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Shrnutí:In the current work, the attempt was made to apply best‐fitted artificial neural network (ANN) architecture and the respective training process for predicting final titer of hepatitis B surface antigen (HBsAg), produced intracellularly by recombinant Pichia pastoris Mut+ in the commercial scale. For this purpose, in large‐scale fed‐batch fermentation, using methanol for HBsAg induction and cell growth, three parameters of average specific growth rate, biomass yield, and dry biomass concentration—in the definite integral form with respect to fermentation time—were selected as input vectors; the final concentration of HBsAg was selected for the ANN output. Used dataset consists of 38 runs from previous batches; feed‐forward ANN 3:5:1 with training algorithm of backpropagation based on a Bayesian regularization was trained and tested with a high degree of accuracy. Implementing the verified ANN for predicting the HBsAg titer of the five new fermentation runs, excluded from the dataset, in the full‐scale production, the coefficient of regression and root‐mean‐square error were found to be 0.969299 and 2.716774, respectively. These results suggest that this verified soft sensor could be an excellent alternative for the current relatively expensive and time‐intensive analytical techniques such as enzyme‐linked immunosorbent assay in the biopharmaceutical industry.
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ISSN:0885-4513
1470-8744
1470-8744
DOI:10.1002/bab.1785