TY - JOUR
T1 - Dynamic optimization of hybridoma growth in a fed-batch bioreactor
AU - Dhir, Sanjeev
AU - Morrow, K. John
AU - Rhinehart, R. Russell
AU - Wiesner, Theodore
PY - 2000/1/20
Y1 - 2000/1/20
N2 - This study addressed the problem of maximizing cell mass and monoclonal antibody production from a fed-batch hybridoma cell culture. We hypothesized that inaccuracies in the process model limited the mathematical optimization. On the basis of shaker flask data, we established a simple phenomenological model with cell mass and lactate production as the controlled variables. We then formulated an optimal control algorithm, which calculated the process- model mismatch at each sampling time, updated the model parameters, and reoptimized the substrate concentrations dynamically throughout the time course of the batch. Manipulated variables were feed rates of glucose and glutamine. Dynamic parameter adjustment was done using a fuzzy logic technique, while a heuristic random optimizer (HRO) optimized the feed rates. The parameters selected for updating were specific growth rate and the yield coefficient of lactate from glucose. These were chosen by a sensitivity analysis. The cell mass produced using dynamic optimization was compared to the cell mass produced for an unoptimized case, and for a one-time optimization at the beginning of the batch. Substantial improvements in reactor productivity resulted from dynamic re-optimization and parameter adjustment. We demonstrated first that a single off-line optimization of substrate concentration at the start of the batch significantly increased the yield of cell mass by 27% over an unoptimized fermentation. Periodic optimization on-line increased yield of cell mass per batch by 44% over the single off-line optimization. Concomitantly, the yield of monoclonal antibody increased by 31% over the off-line optimization case. For batch and fed-batch processes, this appears to be a suitable arrangement to account for inaccuracies in process models. This suggests that implementation of advanced yet inexpensive techniques can improve performance of fed-batch reactors employed in hybridoma cell culture.
AB - This study addressed the problem of maximizing cell mass and monoclonal antibody production from a fed-batch hybridoma cell culture. We hypothesized that inaccuracies in the process model limited the mathematical optimization. On the basis of shaker flask data, we established a simple phenomenological model with cell mass and lactate production as the controlled variables. We then formulated an optimal control algorithm, which calculated the process- model mismatch at each sampling time, updated the model parameters, and reoptimized the substrate concentrations dynamically throughout the time course of the batch. Manipulated variables were feed rates of glucose and glutamine. Dynamic parameter adjustment was done using a fuzzy logic technique, while a heuristic random optimizer (HRO) optimized the feed rates. The parameters selected for updating were specific growth rate and the yield coefficient of lactate from glucose. These were chosen by a sensitivity analysis. The cell mass produced using dynamic optimization was compared to the cell mass produced for an unoptimized case, and for a one-time optimization at the beginning of the batch. Substantial improvements in reactor productivity resulted from dynamic re-optimization and parameter adjustment. We demonstrated first that a single off-line optimization of substrate concentration at the start of the batch significantly increased the yield of cell mass by 27% over an unoptimized fermentation. Periodic optimization on-line increased yield of cell mass per batch by 44% over the single off-line optimization. Concomitantly, the yield of monoclonal antibody increased by 31% over the off-line optimization case. For batch and fed-batch processes, this appears to be a suitable arrangement to account for inaccuracies in process models. This suggests that implementation of advanced yet inexpensive techniques can improve performance of fed-batch reactors employed in hybridoma cell culture.
KW - Dynamic optimization
KW - Fed-batch bioreactor
KW - Fuzzy logic
KW - Hybridoma
KW - Model reparametrization
KW - Monoclonal antibody
UR - http://www.scopus.com/inward/record.url?scp=0034688179&partnerID=8YFLogxK
U2 - 10.1002/(SICI)1097-0290(20000120)67:2<197::AID-BIT9>3.0.CO;2-W
DO - 10.1002/(SICI)1097-0290(20000120)67:2<197::AID-BIT9>3.0.CO;2-W
M3 - Article
C2 - 10592517
AN - SCOPUS:0034688179
SN - 0006-3592
VL - 67
SP - 197
EP - 205
JO - Biotechnology and Bioengineering
JF - Biotechnology and Bioengineering
IS - 2
ER -