System biology or pattern recognition of the immune response for offering immunotherapeutic cures for human diseases

Abstract

Abstract Our brain interprets observations by computations in the form of imaginations and creating patterns based on distinct networks of interactions. Nevertheless, collective patterns of the immune response as a system are hidden from our brain’s spontaneous computations, and should be discovered through research. Nevertheless, highly reductionist approaches hinder the discovery of immunological patterns because of getting lost in the details and seeking for a cause-effect direction. Such reductionist approaches have not been fruitful in offering a cure beyond alleviating the symptoms for immune-related diseases. Recent advances in system biology using big data, deep sequencing at single cell levels, multi-omics and artificial intelligence offer unique opportunities for pattern discovery research in immunology. Here, we provide a new interpretation for the existing data by suggesting that distinct internetwork interactions of the immune cells without any cause-effect direction create distinct immunological patterns manifesting collective functions independent from their cellular constituents. Such immunological patterns emerge from distinct proportions of the immune cell types transforming one another for producing collective functions. Based on empirical data, we propose that discovery of immunological patterns in immune-related diseases could offer better understanding of the immune response as a system as well as novel immune modulatory therapeutics for human diseases. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Breast Cancer Research Program under Award No. W81XWH2210793. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the U.S. Department of Defense.