About this Research Topic
The immune system is a highly complex system that has evolved to defend the organism against an extremely diverse array of pathogens, toxins, and allergenic structures. Furthermore, its malfunctioning can result in a wide number of disorders, including cancer, auto-immune diseases, and diabetes. While the design of effective vaccines and treatments requires a detailed understanding of how healthy responses of the immune systems are mounted against external pathogens, traditional approaches to understanding the inner workings of the immune system still heavily rely on animal models. However, recently, several computational models have shown to be useful in understanding the immune dynamics and, possibly, could guide more resource-efficient wet-lab experimentation.
Multi-scale hybrid models: The immune response integrates processes that act at very different spatial and time scales, such as molecular interactions, gene regulatory events, cellular communication events, and systemic responses implicating multiple organs across time. This complexity warrants the use of models based on the combination of diverse and complementary modeling approaches to accurately capture the dynamics associated with the different spatial and temporal scales. Such multi-scale approaches include mechanistic, probabilistic, statistical, and stochastic models, which might be combined with machine learning and deep learning techniques. For instance, differential equations have been used to model developmental processes associated with the differentiation of immune cells, while data-driven molecular docking approaches can be repurposed to predict the antigenic portion of the pathogen that is likely to be targeted by the immune cells or to infer the logical relationships among genes that regulate cell behavior.
For this Research Topic, we invite submissions of papers describing new computational models that exploit different modeling techniques to unravel the immune system. Especially welcome are papers tackling the spatial and temporal complexity of the innate and adaptive immune responses using a combination of mechanistic and AI approaches, as well as articles discussing progress and challenges in multi-scale hybrid models trained on big immunological datasets.
A non-exhaustive list of possible topics includes: multi-clonal dynamical models of B and T cells describing the emergence of immune memory; optimization of cancer immunotherapies including CAR-T cell-based therapies; optimization of vaccines development for infectious diseases; systemic COVID-related models; models for the generation of repertoire diversity; models of lymphocyte receptor affinity to foreign molecules accounting for structural and chemical properties; interactions between the microbiome and the immune system; quantitative reconstruction of gene regulatory network dynamics and multi-scale integration.
Keywords: Immune System, Multi-scale models, Computational Models, AI, Immunological Datasets
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.