Data-intensive immune network modelling for One Health

1. Brief Bioinform. 2026 Jul 3;27(4):bbag420. doi: 10.1093/bib/bbag420. Data-intensive immune network modelling for One Health. Hillis R(1)(2), Johari NBA(1), Shirali M(2)(3), Overton IM(1). Author information: (1)Johnston Cancer Research Centre, Queen's University Belfast, Belfast, UK. (2)Agri-Food and Biosciences Institute, Hillsborough, UK. (3)School of Biological Sciences, Queen's University Belfast, Belfast, UK. The immune system plays a critical role in morbidity and mortality. For example, infectious disease, cancer, and autoimmune disorders impose substantial health burdens upon both humans and animals. Immune systems share fundamental organisation, and the transmission of pathogens across species boundaries means that immune health in one population shapes risk in others. The One Health approach recognises this interdependence as the basis for understanding human, animal, and environmental health together. Immune systems are formed from multiscale networks of biomolecular interactions spanning specialised cell types, dynamic states, and differentiation trajectories. Data-intensive computational methods are required to model these processes accurately in specific biological contexts. Accordingly, bioinformatics is essential for understanding how the immune system functions under various conditions that arise from infections, in chronic diseases, and through environmental exposures. This article reviews cutting-edge techniques for studying immune function in human and animal health; with emphasis upon genetics, transcriptomics, single-cell approaches, network biology, and machine learning. We consider bioinformatics applications that inform our understanding of immune function to improve health and food systems. Examples are discussed from the rapidly developing cross-disciplinary landscape of computational and physical techniques. We illustrate data-intensive approaches in understanding context-specific immune biology, applied to illuminate the relationship between genetic variation and disease phenotypes. © The Author(s) 2026. Published by Oxford University Press. DOI: 10.1093/bib/bbag420 PMID: 42561151 [Indexed for MEDLINE]

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