In autoimmune disorders, a normal healthy tissue, such as the central nervous system, the pancreas, or the digestive tract, becomes a target for destruction by autoreactive cytotoxic T lymphocytes. This leads to a significant damage to the tissue and a loss of function that can be quite debilitating to the patients. It has been estimated that ~4% of world population suffer from one of more than 80 different autoimmune diseases (including, but not limited to, multiple sclerosis, type 1 diabetes, Crohn's disease, Sjögren's syndrome, rheumatoid arthritis), and that their prevalence is increasing worldwide. Thus understanding the dynamics of these diseases and finding treatments are becoming pressing global health issues.

Nanovaccines, in the form of nanoparticles (NPs) coated with pMHC class I and II complexes, have been recently shown to be effective in blocking spontaneous autoimmune disorders that result from polyclonal immune responses against multiple antigens. Evidence suggests that the efficacy of these nanovaccines in aborting autoimmunity depends on two design parameters, the pMHC-valence on NPs and NP–volume, as well as on the dose and frequency of NP injections. In the past 5-10 years, we have used quantitative/mathematical in silico methods, in parallel with experimental techniques, to shed light into the dynamics of one of these diseases, develop NP-based treatment strategies and guide the design of NPs to increase their efficacy. In this talk, I will provide an overview of how these quantitative/mathematical techniques have been used, and illustrate the remaining challenging questions in the field that still require further investigation.