Physics Informed Neural Networks for Baculovirus-Insect Cell System

The Baculovirus expression vector system (BEVS) is one of the widely utilized platforms for the development of recombinant proteins, virus-like particles (VLPs), and vaccines. A mathematical model tuned with data generated from such systems is critical for its optimization and control. In this work, a mathematical model driven by physics depicting such a cell behavior has been proposed and validated with experiments conducted and subsequently used to study the physics informed neural networks (PINNs). Since the governing equations are found as a set of stiff ordinary differential equations (ODEs), Stiff-PINN, a variant of PINN that is utilized to solve stiff ODEs, is implemented here. Assuming a quasi-steady state for the oxygen concentration, the equation responsible for stiffness, the results are found to be more accurate compared to regular PINN. Such Stiff-PINN models, once developed, can act as a replacement of regular PINN models having trouble to handle stiff equations of BEVS.