Data-based modeling and analysis of bioprocesses: some real experiences
Related Experiment Video
Updated: Aug 30, 2026

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
Published on: May 18, 2020
M Nazmul Karim1, David Hodge, Laurent Simon
1Department of Chemical Engineering, Colorado State University, Fort Collins, Colorado 80523, USA. karim@engr.colostate.edu
Abstract:
Data-generated models find numerous applications in areas where the speed of collection and logging of data surpasses the ability to analyze it. This work is meant to addresses some of the challenges and difficulties encountered in the practical application of these methods in an industrial setting and, more specifically, in the bioprocess industry. Neural network and principal component models are the two topics that are covered in detail in this paper. A review of these modeling technologies as applied to bioprocessing is provided, and four original case studies using industrial fermentation data are presented that utilize these models in the context of prediction and monitoring of bioprocess performance.
Related Concept Videos
Upstream Processing
Bioreactor Design and Operational System
Scale-Up Processes
Bioreactor Controls-II
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Bioreactor Controls-III

