Statistical physics of immune-viral co-evolution
The immune system within each individual host destroys viruses, which manage to escape immunity on the global scale. Recent experiments show population-level responses of both immune repertoires and viruses, and a history dependence of their functional phenotypes. This constrained long-term co-evolution of immune receptor and viral populations is a stochastic many-body problem occurring at many scales, in which the response emerges based on the past states of both the repertoire and viral populations. STRUGGLE infers the details of viral-immune receptor interactions from functional datasets to obtain a predictive statistical model of co-evolution between immune repertoires and viruses.

STRUGGLE covers the many scales of immune-virus interactions: from the molecular level, analyzing high-throughput mutational screens of libraries of antibodies binding a given antigen, through the population-level response of immune repertoires, analyzing next-generation sequencing of vaccine-stimulated whole repertoires, to the population level, modeling the long term co-evolution of both repertoires and viruses.

STRUGGLE combines a statistical data analysis approach with cross-scale many-body physics to: 
- build a molecular model for antigen-receptor binding;
- learn statistical models for repertoire-level response to viral antigen stimulation;
- validate dynamical models of interactions between antigen and immune receptors;
- theoretically evaluate the predictive power of the immune system and viruses;
- and predict virus strains and immune responses based on past infections.

The outcomes of STRUGGLE include the quantitative characterization of the human T-cell response to flu vaccines, with implications for vaccination strategies, and the trout B-cell response to life-threatening rhabdoviruses, which aids vaccine design for fish, with wide use in agriculture. The statistical properties of the co-evolutionary process are needed for informed development of immunotherapies.