(420c) Determining Appropriate Input Perturbation for an Improved Intensified Design of Experiments Approach for System Identification of a Continuous Bioprocess

This work addresses the problem of determining appropriate input perturbation for cell culture bioprocesses in general, and for an monoclonal antibodies industrial bioreactor in particular. The key objective is to demonstrate the feasibility of using far more perturbations than typically done in bio process identification, although significantly less than standard chemical process system identification, to yield data rich enough for the purpose of data driven modeling (and subsequently, control) and yet not having disturbed the cell culture too much.

A proprietary mechanistic model developed by Sartorius for their Cellca cell line is first introduced to serve as a test bed. Subsequently, this test bed is used to address the question of determining the frequency of input perturbation sufficient to identify a data driven dynamic model. To this end, the test bed is used to generate data at various frequencies and a linear time invariant model is identified. The predictive capability of the identified model using limited data from only one batch is used to ascertain the frequency of changes in data generation such that the changes are acceptable from a biological standpoint, and yet generate sufficiently rich data. In particular, a frequency of perturbations at once every three days is found to balance these tradeoffs for the monoclonal antibody process under consideration. The results from the manuscript are meaningful both from a specific results standpoint (as illustrated by subsequent adoption by Sartorius), but also by providing a mechanism to ascertain such information for other bioprocesses.