Abstract
To enable a more rational optimization approach to drive the transition from lab-scale to large industrial bioprocesses, a systematic framework coupling both experimental design and integrated modeling was established to guide the workflow executed from small flask scale to bioreactor scale. The integrated model relies on the coupling of biotic cell factory kinetics to the abiotic bioreactor hydrodynamics to offer a rational means for an in-depth understanding of two-way spatiotemporal interactions between cell behaviors and environmental variations. This model could serve as a promising tool to inform experimental work with reduced efforts via full-factorial in silico predictions. This chapter thus describes the general workflow involved in designing and applying this modeling approach to drive the experimental design towards rational bioprocess optimization.
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Acknowledgments
We would like to thank the support from Singapore National Research Foundation (NRF 2015 NRF-POC002-030), the Synthetic Biology Initiative of the National University of Singapore (DPRT/943/ 09/14), Summit Research Programmes of the National University Health System (NUHSRO/2016/053/SRP/05), and NUS Startup Grant (R-397-000-257-133).
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Yeoh, J.W., Poh, C.L. (2023). Designing a Model-Driven Approach Towards Rational Experimental Design in Bioprocess Optimization. In: Selvarajoo, K. (eds) Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology. Methods in Molecular Biology, vol 2553. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2617-7_9
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DOI: https://doi.org/10.1007/978-1-0716-2617-7_9
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