Scalable modeling of complex immune behavior across biological scales: Toward precision immunotherapy
ABSTRACT
Immunotherapies have transformed the treatment of immune-related diseases, including cancer, autoimmune disorders, and infectious diseases. Despite the rapid accumulation of multi-omics, pharmacological, and clinical data related to the immune system, predicting patient-specific immune responses remains a fundamental challenge. The immune system consists of a vast number of interacting molecular and cellular components whose collective behavior emerges across multiple biological scales, from intracellular signaling and intercellular interactions to tissues, organs, and the whole organism. Capturing such complexity requires computational models that can systematically expand across biological entities and organizational scales. In this talk, I will introduce the concept of scalable modeling of complex immune behavior across biological scales. Scalable modeling aims to incorporate increasingly rich molecular, cellular, and physiological details into a unified computational framework without sacrificing tractability, interpretability, or predictive power. Building upon basic and clinical immunology, this framework integrates omics-driven systems immunology, mechanistic dynamical systems modeling, pharmacometrics, and AI to bridge detailed immunological mechanisms with patient-level immune responses. I will present our efforts toward constructing computational models capable of representing the immune system as an integrated multiscale dynamical system and discuss how such scalable models can accelerate mechanistic understanding of immune-related diseases and provide a computational foundation for predictive and personalized precision immunotherapy.
