Unstructured modeling and RNN Surrogate development for optimizing vaccine production in Baculovirus Expression Vector System

Process optimization and scale up for biomolecules/vaccine production remain challenging due to the adaptation of experiment-based route which needs a large number of expensive experiments making it more challenging in translating the compound for industrial production. In this context, we propose a framework amalgamating systems biology and artificial intelligence for control and optimization of the protein/vaccine production in a Baculovirus expression system [BEVs]. Experimental investigation is conducted to study the growth of insect cells (Sf-9) when infected with the wild type Baculovirus (AcMNPV). Optimal unstructured model replicating the experimental data on cell and virus growth has been identified using a computation strategy consisting of a hybrid optimization technique. The selected model is then used for large scale data generation with an objective to build AI based RNN model that can be proved extremely helpful to handle numerical stability related issues while performing optimal control of the biological system. This work shows a proof of concept and represents the first instance, where an experimental study, mathematical modeling and AI based techniques have been applied for optimal protein production in recombinant expression system at industry setting.