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N-linked glycan distribution plays a significant role in the generation of therapeutic proteins. It is challenging to determine the operating conditions when developing a new biopharmaceutical product with the desired glycan distributions. The glycosylation is a high complex nonlinear system, and it is difficult to develop a reliable first-principle model that heavily relies on experimentation. Our goal is to develop a nonlinear data-driven model and find an appropriate operating space included kinds of input combination from process variables based on this model to ensure the desired product quality. A methodology is proposed based on the inversion of a nonlinear latent-variable model (locality preserving projection to latent structures, LPPLS) to identify a subspace of the knowledge space. The normal operating points of the input variables are designed based on the LPPLS inversion, and the range of operating conditions are expanded around the normal operation points through the prediction uncertainty analysis of forward and inversion model simultaneously. Finally, the designated operation space from LPPLS inversion is applied in an benchmark glycosylation model.

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