Neural-Network-Based Identification of Tissue-Type Plasminogen Activator Protein Production and Glycosylation in CHO Cell Culture under Shear Environment

An artificial neural network (ANN) modeling scheme has been constructed for the identification of both recombinant tissue‐type plasminogen activator (r‐tPA) protein production and glycosylation from Chinese hamster ovary (CHO) cell culture, cultivated in a stirred bioreactor. A series of hybrid feed...

Full description

Saved in:
Bibliographic Details
Published in:Biotechnology progress 2003, Vol.19 (6), p.1828-1836
Main Authors: Senger, Ryan S., Karim, M. Nazmul
Format: Article
Language:English
Subjects:
Algorithms
Ammonia - metabolism
Animals
Biological and medical sciences
Bioreactors - microbiology
Biotechnology
Cell Culture Techniques - methods
Cell Survival - physiology
CHO Cells
Cricetinae
Cricetulus
Ecosystem
Fundamental and applied biological sciences. Psychology
Models, Biological
Neural Networks (Computer)
Recombinant Proteins - biosynthesis
Reproducibility of Results
Sensitivity and Specificity
Shear Strength
Stress, Mechanical
Tissue Plasminogen Activator - analysis
Tissue Plasminogen Activator - biosynthesis
Tissue Plasminogen Activator - genetics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An artificial neural network (ANN) modeling scheme has been constructed for the identification of both recombinant tissue‐type plasminogen activator (r‐tPA) protein production and glycosylation from Chinese hamster ovary (CHO) cell culture, cultivated in a stirred bioreactor. A series of hybrid feed‐forward backpropagation neural networks were constructed to function as a software sensor. This enabled predictions of viable cell density, r‐tPA content, and r‐tPA glycosylation. The sensor was based on an initial input vector space consisting of simple metabolite concentrations, batch cultivation time, and a description of shear stress applied to the culture. Metabolite concentrations of the culture supernatant, included in the input vector space, were obtained from a single isocratic HPLC measurement. The shear stress component of the input space enabled accurate culture state prediction over a wide range of agitation rates. Coefficient of determination (r2) values between ANN predicted and experimental measurements of 0.945, 0.943, 0.956, and 0.990 were calculated to validate individual ANN prediction accuracy for total ammonia, apparent viable cell density, total r‐tPA, and Type II glycoform concentrations, respectively.
ISSN:8756-7938
1520-6033
DOI:10.1021/bp034109x