Abstract
The regulation of immunological tolerance is considered from the perspective of contextual discrimination, rather than self-nonself discrimination. According to the adaptive lymphocyte hypothesis, the scale of immune aggression versus tolerance can be regulated at the cell population level, but individual cells also tune and update their responsiveness under the influence of recurrent signals. The generation of a sizeable conventional immune response, which is transient and aggressive, depends critically on the perturbation to the system, which is related to the rate of appearance of the immunizing agent. These characteristics are explained in quantitative terms by the "balance of growth and differentiation model". Strong perturbations are typically associated, physiologically, with acute infections. Full activation of individual lymphocytes also requires strong metabolic perturbations, where the perturbation is defined as a measure of variation in the intensity of stimulation. Cells that fail to be activated in this way may be driven into a state which formally conforms to the operational definition of anergy. This state is characterized by a variable degree of resistance to the stereotypic mode of activation for which the cell has been programmed before. While in this state, the cell interacts with its environment: these interactions promote its viability, update its activation thresholds and its excitability, and may reprogram the cell for a different mode of response when activated later. In addition, cells engaged in such interactions may mediate context-dependent immunological functions. The characteristics of the interactions involving such anergic cells are discussed in semi-quantitative terms with the help of the "tunable activation-thresholds model". Several aspects of immunological tolerance are interpreted in a unifying way based on this conceptual framework. It is suggested that progress in our ability to evaluate and manipulate the regulation of immunological tolerance would require a methodology to conjoin many pieces of data together and to look for patterns.
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