Control of amino acid starvation-induced apoptosis in CHO cell cultures

This research was dedicated to the study of amino acid starvation-induced apoptosis in CHO cell cultures. The two mechanisms of cell death reported in the literature are necrosis and apoptosis. Necrosis is a passive process that occurs when the cells are exposed to extreme environmental or physiologic stresses. In apoptosis or programmed cell death (PCD), the cells are induced to commit suicide under normal physiological conditions. The study of apoptosis is very important to biotechnology due to the extensive use of animal cell cultures for the production of useful proteins. However, the productivity of recombinant mammalian cells is limited by the rate at which cells die right after reaching a maximum cell density. For mammalian cells, PCD is a way of life and can be induced in response to several stress related phenomena including starvation-induced conditions during the declining phase of a batch culture. In order to control starvation-induced apoptosis in a fed-batch reactor, a mathematical model of mammalian cell culture kinetics is paramount. However, classical kinetic models do not distinguish between death by necrosis or apoptosis. In this work, we extended classical kinetic models to account for apoptotic cell death in real-time. Neural network based sensitivity analysis identified glutamine and asparagine as two major amino acids that play a key role in the suppression of apoptosis.; Advanced process control concepts are usually tested using a simulated system. This works shows that a non-classical control algorithm can be implemented in a discrete fashion on real processes with satisfactory results. Kalman filters and model predictive controllers were implemented to delay starvation-induced apoptosis in CHO cell cultures. The off-line measured concentrations of viable and total cells, lactate, and glucose were used to update the state estimates. A NN was then used to approximate the concentration of apoptotic cells in the bioreactor based on the concentrations of viable cells, glutamine and asparagine. This information was then fed to a model predictive controller that was activated when the apoptotic cells reached a concentration of 1.0 × 105 cells/ml.