T cell therapy, systems approaches, vaccines, databases, omics, machine learning. Immunology as a research field has a complicated history. It has emerged quite recently as a separate discipline in biomedical science and clinical application. The view on the utility of insights gained from immunology has dramatically changed during the last ten years. From the viewpoint of a physician, there is a positive shift in the timeline: from little or no basic/clinical training on the subject to new generations of physicians that have assimilated detailed concepts and applied them in the clinic. Then again from the viewpoint of accessibility, immunology (especially in clinical medicine) is burdened by the profligate use of acronyms and jargon, e.g. things like the CD (cluster of differentiation) system that was meant to classify cell surface molecules, the interleukin system of cytokine nomenclature, the HLA classification, and many other acronyms.
With massively expanded scientific research programs, the reality is that immunology can no longer be neglected. All relevant aspects of life from embryology to senescence, and from bacterial or viral infections to cancer have shown that immunology has a central role. This is also reflected in the Nobel prizes for medicine or physiology awarded for key immunological advances: from R. Zinkernagel and P. Doherty for their work on the specificity of cell-mediated immune defense to the more recent prize to J. P. Allison and T. Honjo for their work on the regulation of immune responses via the PD-1/PD-L1 axis. The impact of immunology on biological processes of all sorts as well as the impact on clinical treatment options is very profound. For example, the advent of checkpoint-inhibition for malignant melanoma and lung cancers, or the newest generation of chimeric antigen receptor T cells using CRISPR/Cas9-based genome editing is showing the enormous breadth and depth of the immunological “toolbox”.
All these developments have a relevant key theme: the analysis of large datasets both in scientific research and in clinical care. These large datasets require efficient and innovative computational approaches, essentially enabling researchers and physicians to leverage the potential of immunology. The basis for understanding and developing novel computational approaches is the mathematical investigation of the underlying models, concepts, and frameworks. In the recent years, examples of ideas and results from the fields of mathematics and computing have been exported to other fields like biology and medicine resulting in improved interconnectivity among different scientific communities, and in cross-fertilization of viewpoints as scientists from different disciplines exchange ideas. Most notable example is the wide application of deep learning in biomedicine. Furthermore, technology moved in giant leaps as well: from terabyte hard drives (in 2014) to (claims of) quantum supremacy in 2019.
One thing is clear: whether it is a differential equation or a complex multiagent model, deterministic or stochastic, utility and application are dependent on well-chosen strategies. Finding and optimizing these strategies in various contexts from basic mathematical/computational developments to applications within a clinical trial requires the interplay between different disciplines. This kind of diversity in specialty and expertise is reflected in our editorial board members. The higher complexity of research work that sits at the interface between immunological and computational research requires an interdisciplinary team of scientists that can assess relevance and impact. ImmunoInformatics is the journal that aims to bridge this wet- and dry-lab gap by fostering interactions among the different disciplines. In this context, we encourage the publication of research work that does not fit in research journals with single or narrow scopes. In the current scientific practice, this kind of publication outlet is desperately needed. We are confident that we are at the verge of many scientific breakthroughs that can be only achieved by fusing computational and biomedical expertise.
Published online: May 18, 2021
© 2021 Published by Elsevier B.V.