A predictive systems vaccinology framework enables rational optimization of MVA-based vaccines

The poxvirus Modified Vaccinia virus Ankara (MVA) is a safe and versatile licensed vaccine and viral vector, yet its immunogenicity remains improvable, as it often requires multiple doses for optimal protection and also induces waning of antibody responses. To enable more rational optimization of MVA-based vaccines, we developed a mechanistic and executable systems vaccinology framework based on Boolean modeling to capture the dynamics of vaccine-induced immune responses. We constructed and calibrated a Boolean network of the MVA‑induced immune response by integrating literature‑derived mechanisms with longitudinal in vivo experimental data. The model accurately reproduced immune dynamics with high fidelity and, importantly, was validated against independent datasets of genetically modified MVA vaccines, demonstrating strong predictive capacity. Using this framework, we performed in silico perturbations to evaluate novel genetically modified MVA mutants derived from expert knowledge. To further guide rational design, we built two other vaccine-induced response Boolean networks: one describing the MVA response in a broader fashion, the other modeling the YF‑17D yellow fever vaccine response that represents a reference for durable protection after single‑dose immunization. Comparative analysis of network topology and dynamics revealed shared and divergent features that informed strategies to enhance MVA-induced responses by reorienting them toward YF-17D-like immune signatures, and allowed us to design and test virtually two new genetically modified MVA deletion mutants. Throughout this work, we exploited the executable nature of the models of response to MVA to simulate perturbations, identifying potential targets to boost immunogenicity. Together, this work establishes executable Boolean modeling as a valuable predictive tool for systems vaccinology and provides a generalizable framework for the rational design and optimization of next‑generation MVA‑based vaccines.