Application of recurrent neural network for online prediction of cell density of recombinant Pichia pastoris producing HBsAg.
Prep Biochem Biotechnol
; 49(4): 352-359, 2019.
Article
em En
| MEDLINE
| ID: mdl-30707051
ABSTRACT
Artificial neural networking (ANN) seems to be a promising soft sensor for implementing current approaches of quality by design (QbD) and process analytical technologies (PAT) in the biopharmaceutical industry. In this study, we aimed to implement best-fitted ANN architecture for online prediction of the biomass amount of recombinant Pichia pastoris (P. pastoris) - expressing intracellular hepatitis B surface antigen (HBsAg) - during the fed-batch fermentation process using methanol as a sole carbon source. For this purpose, at the induction phase of methanol fed-batch fermentation, carbon evolution rate (CER), dissolved oxygen (DO), and methanol feed rate were selected as input vectors and total wet cell weight (WCW) was considered as output vector for the ANN. The obtained results indicated that after training recurrent ANN with data sets of four fed-batch runs, this toolbox could predict the WCW of the next fed-batch fermentation process at each specified time point with high accuracy. The R-squared and root-mean-square error between actual and predicted values were found to be 0.9985 and 13.73, respectively. This verified toolbox could have major importance in the biopharmaceutical industry since recombinant P. pastoris is widely used for the large-scale production of HBsAg.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Pichia
/
Redes Neurais de Computação
/
Biomassa
/
Carga Bacteriana
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Prep Biochem Biotechnol
Assunto da revista:
BIOQUIMICA
/
BIOTECNOLOGIA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Irã