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Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications
Kathleen Van Beylen1,2, Ali Youssef1, Alberto Peña Fernández1
1Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium.
Bioengineering (Basel, Switzerland)
|July 24, 2020
Summary
This study optimizes bioprocess feeding strategies by using lactate measurements to control cell growth. Personalized feeding reduces costs and improves efficiency in cell culture processes.
Area of Science:
- Biotechnology
- Cell Culture
- Process Control
Background:
- Overfeeding in bioprocesses leads to unnecessary costs.
- Personalized feeding strategies can enhance efficiency.
- Lactate measurements indicate cell growth during culture.
Purpose of the Study:
- To develop a personalized feeding strategy for bioprocesses.
- To utilize lactate measurements for adaptive feeding control.
- To reduce overfeeding costs in cell culture.
Main Methods:
- Model predictive control (MPC) was employed.
- Lactate measurements were used as a proxy for cell growth.
- Individualized MPC controllers were developed for human progenitor cells from three donors.
Main Results:
- Process models achieved 99.80% ± 0.02% accuracy.
- Simulations to reproduce experimental runs reached 98.64% ± 0.10% accuracy.
- The adaptive feeding strategy effectively controlled cell growth.
Conclusions:
- A promising framework for controlling cell growth via adaptive feeding strategies was established.
- Lactate-based control offers an efficient method for bioprocess optimization.
- Personalized feeding strategies based on real-time measurements reduce operational costs.
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