Editorial: Modeling the immune system: from PK/PD to systems immunology

Understanding and ultimately controlling immune function takes much more than listing cell types, cytokines, and receptors. It requires quantitative ways of thinking and modeling that can capture the immune system as it really is: a highly complex, specialized, and dynamic network that operates across multiple scales -molecules, cells, tissues, and time. At the same time, these models need to remain understandable to immunologists so they can guide experiments, identify biomarkers, and support therapeutic decision-making.This Research Topic was designed to help bring together two traditions that are increasingly overlapping: pharmacometrics and quantitative systems pharmacology (PK/PD, semimechanistic and mechanistic models), and systems immunology (data-driven and network-based approaches that integrate multi-omics and prior knowledge into coherent, testable frameworks).The collection brings together conceptual overviews, methodological advances, and applied case studies. It includes nine articles: eight original research papers and one review. Among the original research contributions, seven present computational models focused on specific aspects of the immune system, and one focuses on instrumentation and biomarker discovery. The review article examines how artificial intelligence and systems biology are reshaping modern immunology. Collectively, these contributions illustrate a central message: progress in immunology will accelerate when we treat immune responses as dynamic, multilevel systems that can be quantified with models, tested against data, iteratively refined, and ultimately used to predict how the system will respond to perturbations such as drugs, infections, or combination therapies.Alfonso-González et al. offer a concise roadmap to the methodological "pillars" of systems immunology, highlighting how network pharmacology, artificial intelligence, and quantitative systems pharmacology can be combined to identify biomarkers, optimize therapies, and support drug discovery in inflammatory, autoimmune, and infectious diseases. The review does not ignore real-world constraints. It emphasizes challenges such as data quality, validation, interpretability, and regulatory requirements that must be addressed before these methods can have a genuine clinical impact.Hong and Park propose "multi-physiology modeling" as a unifying paradigm for precision immunotherapy. They argue for integrating omics-driven systems immunology with dynamical modeling and pharmacometrics to represent cross-scale interactions and patient-to-patient heterogeneity. Their perspective underscores the growing need for modular, interoperable modeling frameworks that integrate multiple interacting agents and phenotypically diverse cell Taken together, these contributions showcase the transformative potential of mathematical and computational modeling in immunology. By simulating and analyzing the complex interactions among biological components, researchers are advancing our understanding of immune mechanisms and host-pathogen interactions while opening new avenues for therapy. Deployed in this integrated way, these methods are already driving high-impact discoveries and equipping immunology with powerful tools to tackle some of its most urgent challenges.