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Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Published on: September 28, 2018

Developing a Computational Framework To Advance Bioprocess Scale-Up.

Guan Wang¹, Cees Haringa², Henk Noorman³

¹State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology (ECUST), Shanghai, People's Republic of China.

Trends in Biotechnology

|June 5, 2020

Summary

This summary is machine-generated.

Bioprocess scale-up challenges like reduced performance can be overcome. Advanced computational models integrating cell physiology and fluid dynamics enable faster, more efficient scale-up for biotech innovations.

Keywords:

computational fluid dynamics industrial metabolic model metabolomics population heterogeneity scale-down

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Area of Science:

Biotechnology
Bioprocess Engineering
Computational Biology

Background:

Bioprocess scale-up is crucial for commercializing biotech innovations.
Performance losses (titer, yield, productivity) during scale-up hinder commercialization.
Current scale-up methods often result in significant performance degradation.

Purpose of the Study:

To address challenges in bioprocess scale-up.
To propose a computational framework for accelerated scale-up.
To minimize performance losses during scale-up.

Main Methods:

Utilizing scale-down studies.
Employing computational fluid dynamics (CFD) models.
Integrating stimulus-response metabolic models and spatiotemporal multiscale cellular models.

Main Results:

Scale-down studies with CFD and metabolic models improve process prediction.
Integrated in silico models offer potential for ideal bioprocess design.
Identified challenges in predictive metabolic modeling and CFD coupling.

Conclusions:

A computational framework integrating cellular physiology and fluid dynamics can accelerate bioprocess scale-up.
Addressing challenges in model development and coupling is key.
This approach promises faster scale-up with minimal performance losses.