an industrial E. coli case study

The authors thank Chi Chung (Tim) Choi and Paul Rice for their collaboration in batch production and performing off-line sample analysis and Carl Hémond for providing the RP-HPLC assay and results. The authors thank Paul Rice also for critical reading of the manuscript. © Copyright 2017 Elsevier B.V., All rights reserved.Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.publishersversionpublishe

Evaluation of temperature rise and bonding strength in cements used for permanent head attachments in rats and mice

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