Multi-objective Optimization and control under Uncertainty for performance improvement of a Baculovirus Expression Vector System

Bioprocess optimization and control for large scale production of vaccine/protein remain challenging due to the adaptation of experiment-based route which needs numerous expensive and time intensive experiments. The presence of model uncertainties in such a nonlinear system further makes the optimization and scale -up challenging. In this context, we propose a robust framework amalgamating the paradigms of systems biology and dynamic optimization under uncertainty for improving the performance of one of the most widely used vaccine/protein production platform, the Baculovirus expression system [BEVs]. Here, the multi-objective optimal control problem is formulated with an objective of maximizing the productivity and minimizing raw material consumption in a semi-batch baculovirus system considering parametric uncertainty. A comprehensive comparison shows that a multifold increase in the productivity can be obtained using this computational framework considering controlled addition of feed material. This study provides a generic methodology for improving the performance of a bioprocess and represents the first instance where robust optimal control has been applied for optimizing the productivity of a baculovirus-insect cell system.