{"paper_id":"4e834212-a7bc-4031-a904-b60fd99f2626","body_text":"A predictive systems vaccinology framework enables rational optimization of MVA-based vaccines | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A predictive systems vaccinology framework enables rational optimization of MVA-based vaccines Vincent Deman, Philippe Castera, Juan García-Arriaza, Mariano Esteban, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9265749/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Modified Vaccinia virus Ankara (MVA) rational vaccine design systems vaccinology vaccine optimization in silico modeling Boolean network modeling immune response viral immunomodulatory genes. Full Text Additional Declarations No competing interests reported. Supplementary Files Demanetal300326suppfig.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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 \\u003cem\\u003ein vivo\\u003c/em\\u003e 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 \\u003cem\\u003ein silico\\u003c/em\\u003e perturbations to evaluate novel genetically modified MVA mutants derived from expert knowledge. 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