Immune network operations in COVID-19

Abstract

The immune system, whose nature lies in being a complex network of interactions, lends itself well to being represented and studied using graph theory. However, it should be noted that although the formalization of models of the immune system is relatively recent, the medical use of its signaling network structure has been carried out empirically for centuries in vaccinology, immunopathology, and clinical immunology, as evidenced by the development of effective vaccines, the management of transplant rejection, the management of allergies, and the treatment of certain types of cancer and autoimmune diseases. A network optimization analogy is proposed through the employment of the system dynamic formalism of causal loop diagrams (CLDs), where current network operations (also known as NetOps) in information technology (IT), are interpreted as immune NetOps in coronavirus disease 2019 (COVID-19) treatment. Traffic shaping corresponds to signaling pathway modulation by immunosuppressors. Data caching corresponds to the activation of innate immunity by application of Bacillus Calmette-Guerin (BCG) and other vaccines. Data compression corresponds with the activation of adaptative immune response by vaccination with the actual approved COVID-19 vaccines. Buffer tuning corresponds with concurrent activation of innate and adaptative or specialized immune cells and antibodies that attack and destroy foreign invaders by trained immunity-based vaccines to develop. The present study delineates some experimental extensions and future developments. Given the complex communication architecture of signal transduction in the immune system, it is apparent that multiple parallel pathways influencing and regulating each other are not the exception but the norm. Thus, the transition from empirical immune NetOps to analytical immune NetOps is a goal for the near future in biomedicine.