Adaptive discrimination between harmful and harmless antigens in the immune system by predictive coding

なぜ免疫系はウイルスを排除して食べ物を排除しないのか？ --予測符号化に基づく免疫記憶のアップデート--. 京都大学プレスリリース. 2023-01-12.The immune system discriminates between harmful and harmless antigens based on past experiences; however, the underlying mechanism is largely unknown. From the viewpoint of machine learning, the learning system predicts the observation and updates the prediction based on prediction error, a process known as “predictive coding.” Here, we modeled the population dynamics of T cells by adopting the concept of predictive coding; conventional and regulatory T cells predict the antigen concentration and excessive immune response, respectively. Their prediction error signals, possibly via cytokines, induce their differentiation to memory T cells. Through numerical simulations, we found that the immune system identifies antigen risks depending on the concentration and input rapidness of the antigen. Further, our model reproduced history-dependent discrimination, as in allergy onset and subsequent therapy. Taken together, this study provided a novel framework to improve our understanding of how the immune system adaptively learns the risks of diverse antigens

Stem cell homeostasis regulated by hierarchy and neutral competition

幹細胞の栄枯盛衰のメカニズムを提唱 --多細胞組織における階層性と競争原理が織り成す幹細胞ダイナミクス--. 京都大学プレスリリース. 2022-12-15.Tissue stem cells maintain themselves through self-renewal while constantly supplying differentiating cells. Two distinct models have been proposed as mechanisms of stem cell homeostasis. According to the classical model, there is hierarchy among stem cells, and master stem cells produce stem cells by asymmetric division; whereas, according to the recent model, stem cells are equipotent and neutrally compete. However, the mechanism remains controversial in several tissues and species. Here, we developed a mathematical model linking the two models, named the hierarchical neutral competition (hNC) model. Our theoretical analysis showed that the combination of the hierarchy and neutral competition exhibited bursts in clonal expansion, which was consistent with experimental data of rhesus macaque hematopoiesis. Furthermore, the scaling law in clone size distribution, considered a unique characteristic of the recent model, was satisfied even in the hNC model. Based on the findings above, we proposed the criterion for distinguishing the three models based on experiments

Adaptive discrimination between harmful and harmless antigens in the immune system by predictive coding

Summary: The immune system discriminates between harmful and harmless antigens based on past experiences; however, the underlying mechanism is largely unknown. From the viewpoint of machine learning, the learning system predicts the observation and updates the prediction based on prediction error, a process known as “predictive coding.” Here, we modeled the population dynamics of T cells by adopting the concept of predictive coding; conventional and regulatory T cells predict the antigen concentration and excessive immune response, respectively. Their prediction error signals, possibly via cytokines, induce their differentiation to memory T cells. Through numerical simulations, we found that the immune system identifies antigen risks depending on the concentration and input rapidness of the antigen. Further, our model reproduced history-dependent discrimination, as in allergy onset and subsequent therapy. Taken together, this study provided a novel framework to improve our understanding of how the immune system adaptively learns the risks of diverse antigens