Digital Seed Train Twins and Statistical Methods.

摘要: Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development process steps. One these steps is time- cost-intensive cell proliferation (also called seed train) increase number from thawing up scale. Challenges like complex metabolism, batch-to-batch variation, variabilities behavior, influences changes cultivation conditions necessitate adequate solutions provide information about current near future state derive correct decisions.For this purpose train twins have proved be efficient, digitally display time-dependent behavior important variables based on mathematical models, strategies, adaption procedures.This chapter will outline needs for digitalization trains, construction twin, role parameter estimation, different statistical methods within context, are applicable problems field bioprocessing. The results case study presented illustrate Bayesian approach estimation prediction an industrial culture digitalization. This outlines processes (seed trains), twin well seed, example It has been shown way prior knowledge input uncertainty can considered propagated predictive uncertainty.