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Abstract Recombinant adeno‐associated virus (rAAV) is among the most commonly used in vivo gene delivery vehicles and has seen a number of successes in clinical application. Current manufacturing processes of rAAV employ multiple plasmid transfection or rely on virus infection and face challenges in scale‐up. A synthetic biology approach was taken to generate stable cell lines with integrated genetic modules, which produced rAAV upon induction albeit at a low productivity. To identify potential factors that restrained the productivity, we systematically characterized virus production kinetics through targeted quantitative proteomics and various physical assays of viral components. We demonstrated that reducing the excessive expression of gene of interest by its conditional expression greatly increased the productivity of these synthetic cell lines. Further enhancement was gained by optimizing induction profiles and alleviating proteasomal degradation of viral capsid protein by the addition of proteasome inhibitors. Altogether, these enhancements brought the productivity close to traditional multiple plasmid transfection. The rAAV produced had comparable full particle contents as those produced by conventional transient plasmid transfection. The present work exemplified the versatility of our synthetic biology‐based viral vector production platform and its potential for plasmid‐ and virus‐free rAAV manufacturing. 

Abstract Mammalian cells are commonly used as hosts in cell culture for biologics production in the pharmaceutical industry. Structured mechanistic models of metabolism have been used to capture complex cellular mechanisms that contribute to varying metabolic shifts in different cell lines. However, little research has focused on the impact of temporal changes in enzyme abundance and activity on the modeling of cell metabolism. In this work, we present a framework for constructing mechanistic models of metabolism that integrate growth‐signaling control of enzyme activity and transcript dynamics. The proposed approach is applied to build models for three Chinese hamster ovary (CHO) cell lines using fed‐batch culture data and time‐series transcript profiles. Leveraging information from the transcriptome data, we develop a parameter estimation approach based on multi‐cell‐line (MCL) learning, which combines data sets from different cell lines and trains the individual cell‐line models jointly to improve model accuracy. The computational results demonstrate the important role of growth signaling and transcript variability in metabolic models as well as the virtue of the MCL approach for constructing cell‐line models with a limited amount of data. The resulting models exhibit a high level of accuracy in predicting distinct metabolic behaviors in the different cell lines; these models can potentially be used to accelerate the process and cell‐line development for the biomanufacturing of new protein therapeutics.