Speakers
Description
Oncolytic virotherapy has emerged as a promising modality for treating solid tumors by exploiting viruses that selectively infect and lyse cancer cells while stimulating antitumor immune responses. Mathematical modeling plays a pivotal role in understanding these multiscale and nonlinear biological dynamics. This minisymposium will focus on recent advances in predictive mathematical and computational modeling of oncolytic viral infection in tumor tissues. Key topics include the quantitative analysis of immune–virus–tumor interactions, spatial modeling of viral spread in heterogeneous tumor microenvironments, and data-driven approaches for parameter estimation, treatment planning, and optimal dosing strategies. By integrating theoretical analysis with experimental and clinical findings, these models can guide treatment design, improve dosing strategies, and identify key mechanisms influencing therapeutic success. This session aims to foster collaboration across mathematics, oncology, and virology, and to highlight how model-based predictions can accelerate the translation of oncolytic virotherapy into effective clinical applications. The thematic track for this minisymposium is Mathematical Oncology.
