Real-Time Cell Growth Control Using a Lactate-Based Model Predictive Controller

Abstract

Providing a cost-efficient feeding strategy for cell expansion processes remains a challenging task due to, among other factors, donor variability. The current method to use a fixed medium replacement strategy for all cell batches results often in either over- or underfeeding these cells. In order to take into account the individual needs of the cells, a model predictive controller was developed in this work. Reference experiments were performed by expanding human periosteum derived progenitor cells (hPDCs) in tissue flasks to acquire reference data. With these data, a time-variant prediction model was identified to describe the relation between the accumulated medium replaced as the control input and the accumulated lactate produced as the process output. Several forecast methods to predict the cell growth process were designed using multiple collected datasets by applying transfer function models or machine learning. The first controller experiment was performed using the accumulated lactate values from the reference experiment as a static target function over time, resulting in over- or underfeeding the cells. The second controller experiment used a time-adaptive target function by combining reference data as well as current measured real-time data, without over- or underfeeding the cells.