Robustness and Fragility in Immunosenescence

We construct a model to study tradeoffs associated with aging in the adaptive immune system, focusing on cumulative effects of replacing naive cells with memory cells. Binding affinities are characterized by a stochastic shape space model. System loss arising from an individual infection is associated with disease severity, as measured by the total antigen population over the course of an infection. We monitor evolution of cell populations on the shape space over a string of infections, and find that the distribution of losses becomes increasingly heavy-tailed with time. Initially this lowers the average loss: the memory cell population becomes tuned to the history of past exposures, reducing the loss of the system when subjected to a second, similar infection. This is accompanied by a corresponding increase in vulnerability to novel infections, which ultimately causes the expected loss to increase due to overspecialization, leading to increasing fragility with age (i.e., immunosenescence). In our model, immunosenescence is not the result of a performance degradation of some specific lymphocyte, but rather a natural consequence of the built-in mechanisms for system adaptation. This “robust, yet fragile” behavior is a key signature of Highly Optimized Tolerance.Synopsis: The immune system can be viewed as a complex system, which adapts, over time, to reflect the history of infections experienced by the organism. This paper describes a model that captures this adaptation and corresponding robust, yet fragile behavior. To model immunological processes that rely on binding specificity, researchers typically utilize abstract shape space models. These models describe the binding characteristics of a receptor or antigen as points in a high dimensional vector space. Stromberg and Carlson have incorporated the concept of shape space into a dynamical model of immune response. They use this model to examine the development of the system over a series of infections and monitor the severity of disease for each infection. The diseases are drawn at random from a distribution having a few frequently reoccurring and many rare. The system is observed to adapt over a series of infections, becoming robust to the frequent diseases while developing fragility to the rare diseases. This age-correlated weakness arises from the underlying dynamics of system adaptation rather than from an accumulation of defects. This robust, yet fragile behavior is a signature of Highly Optimized Tolerance, a mechanism for complexity based on robustness tradeoffs.