Computational and AI-Driven Advances in Systems Immunology

The rapid growth of high-dimensional single-cell omics, spatial profiling, and gene-editing technologies has generated unprecedented volumes of immunological data. Understanding the complex, multi-scale dynamics of the immune system requires a transition from qualitative observations to quantitative, predictive models.This Research Topic, inspired by the 4th Workshop on Advances in Systems Immunology (ASI 2026), aims to bring together computational and experimental researchers to address major bottlenecks in systems immunology. We focus on bridging computer science, artificial intelligence, statistics, immunology, and clinical medicine to translate basic discoveries into therapeutic applications. By integrating multi-scale data, the collection seeks to provide a comprehensive framework for understanding immune responses in health and disease.We welcome submissions that develop, apply, or evaluate computational, statistical, and machine learning methods to analyze immunological systems. Submissions can address molecular, cellular, tissue, or organism-level immune responses, with a particular interest in translational applications such as immunotherapy, vaccine optimization, and biomarker discovery.Key themes of interest include, but are not limited to:• AI and Machine Learning in Immunology: Application of machine learning and deep learning to immune cell type or state inference, cell differentiation, lineage inference, and AI-driven drug discovery for immunological targets.• Antibody and Repertoire Engineering: Computational analysis of BCR and TCR repertoires, AI-assisted antibody design, and modeling of antigen processing, presentation, and recognition.• Single-Cell & Spatial Omics: Methods for integrating multi-omics single-cell data, analyzing spatial omics within the tissue microenvironment (including the tumor microenvironment), and quantitative microscopy.• Immune Networks & Modeling: Simulation of tumor-immune system interactions, inference of intra- and inter-cellular immune regulatory networks, and modeling microbiome-immune system interactions.• Translational Systems Immunology: Optimization of molecular and cellular immunotherapies, computational vaccine design, and systems-level analyses of cancer, viral immunology, and host-pathogen interactions.• High-Dimensional Data Analysis: Novel statistical and computational pipelines for analyzing high-dimensional flow/mass cytometry data and pooled CRISPR genetic screens in immune cells.Please note that purely bibliometric or scientometric studies, such as citation analyses, publication trend studies, or literature network mapping, fall outside the scope of this Research Topic. Submissions should present computational, statistical, or experimental advances in the biology of the immune system itself.We welcome Original Research, Methods, Reviews, Mini-Reviews, Perspectives, and Brief Research Reports that present substantial computational advances, open-source software tools, or novel biological insights in systems immunology. Topic editors: Marmar Moussa, Ion Mandoiu, Wei Chen Source: https://www.frontiersin.org/research-topics/83315/computational-and-ai-driven-advances-in-systems-immunology