New Computational Model Maps Immune System's Response to BCG Vaccine
Researchers at MIT have developed a new computational framework that provides insights into these systems by uncovering key interactions. This innovative model, which uses probabilistic graphical networks, was used to analyze immune responses to the BCG tuberculosis vaccine, revealing critical mechanisms that drive immunity.
Unlike traditional machine learning models that focus on predicting outcomes, this approach maps the intermediate steps between inputs (e.g., vaccine administration) and outputs (e.g., immune response). By employing a technique called graphical lasso, the researchers filtered out indirect relationships, isolating the essential interactions within a highly complex network of variables.
Applying the model to BCG vaccine studies, the team identified specific pathways involving T cells and cytokines that activate B cells, which are crucial for generating effective immunity. They also validated the model’s ability to predict the effects of immune cell disruptions, offering a valuable tool for vaccine developers to optimize immunization strategies.
Beyond vaccines, this approach is now being used to study malaria immunization and cancer treatment responses. The findings, published in Cell Systems, demonstrate the potential of this computational method to advance both immunology and therapeutic development.
