Computational approach for understanding and improving GS-NS0 antibody production under hyperosmotic conditions.

A systematic computational framework is proposed for studying the underlying mechanisms of hyperosmotic conditions on GS-NS0 antibody production and to predict the optimal hyperosmotic induction time. Both IgG mRNA and polypeptide chain concentrations were positively related to the specific antibody productivity (q(Ab)) for normal and hyperosmotic conditions throughout. Hyperosmotic conditions resulted in 100% increase in specific IgG mRNA transcription rates; however, mRNA half-lives were 25% lower at both the mid-exponential and stationary phases. The IgG specific translation rates were higher (24%) at the mid-exponential phase for hyperosmotic cultures but were comparable in later phases. The main mechanism through which hyperosmotic conditions improve q(Ab) was concluded to be the heightened specific transcription rates. The predictive capability of the model was experimentally verified by identifying the optimal hyperosmotic induction time for biphasic GS-NS0 cultures at 72 h. The systematic approach that seamlessly combined experimentation and mathematical modelling, allowed both for the model based design of experiments that yielded valuable biological insight and for the prediction of the optimal hyperosmotic induction time. This framework enables "closing-the-loop" in mammalian cell bioprocess modelling by guiding experimentation through modelling.