This work highlights the role of process system engineering (PSE) principles (especially simulation and optimization) in the development of a COVID-19 vaccine and other kinds of vaccines. As a result of a unique multi-national collaboration of PSE-minded chemical engineers, immunologists, and pathologists, we have created computer models of how the human body's immune system responds to vaccine injections of various kinds. The STochastic Omentum REsponse model (STORE) is stochastic, agent-based, and dynamic, and tracks how T-cells and antigen presenting cells interact, change, divide, and respond after an immune event such as a vaccine injection or an infection. Using model parameters related to dosage, injection schedule, genetic traits of the patient, and various vaccine or immune system properties, the STORE model can be used to predict how the human body responds in the days and weeks after a vaccine injection.

The STORE model consists of a collection of small, agent-based models where each of potentially millions of agents (a cell of various types) is individually tracked, and each small model accounts for certain interactions between agents based on known phenomena, with only a few fitting parameters. The collection of these smaller models forms a complex system model that captures much of the nuance of the human immune system. The outputs of the models are trajectories of the counts of T-cells at different stages of maturation or division over time, from which immunologists and pathologists can use to determine important information about a patient's chances at protective immunity. This is very useful for rapid vaccine development. For example, as candidate vaccines are developed (including coronavirus vaccines), the model can be used during animal and human trials to predict the effectiveness of a vaccine many months in the future from data collected during the first few days. In addition, it can be used for dosing studies by significantly reducing the guesswork in choosing dosages, thereby reducing the risk to human trial participants. This allows for much more rapid screening of and development candidate vaccines which is crucial to overcoming our current pandemic. It can also be used to predict, much ahead of time, whether booster shots will be needed and when, and utilize the stochastic properties of the model to determine probability distributions of effectiveness in vaccine recipients which is crucial for public health applications such as determining herd immunity.

This talk will be geared toward chemical process systems engineers and will not require knowledge in medicine or immunology. Instead, it will focus on how PSE principles apply to this biological system. For example, classic chemical process design, model development, optimization, and control principles and methods can be used analogously.