Biology subjects

Yoshido, K.

Publications and source records attributed to Yoshido, K..

2 recordsLinked to original sources

bioRxiv2022

Stem cell homeostasis regulated by hierarchy and neutral competition

Tissue stem cells maintain themselves through self-renewal while constantly supplying differentiating cells. As a mechanism of stem cell homeostasis, two distinct models were proposed: the classical model states that there are hierarchy among stem cells and master stem cells provide stem cells by asymmetric divisions, whereas the recent model states that stem cells are equipotent and neutrally competing each other. However, its mechanism is still controversial in several tissues and species. Here, we developed a mathematical model linking these two models. Our theoretical analysis showed that the model with the hierarchy and neutral competition, called the hierarchical neutral competition (hNC) model, exhibited bursts in clonal expansion, unlike existing models. Furthermore, the scaling law in clone size distribution, thought to be a unique characteristic of the recent model, was satisfied in the hNC model. Based on these findings, we proposed a criterion for distinguishing the three models by experiments.

developmental biology↗

bioRxiv2021

Adaptive discrimination of antigen risk by predictive coding in immune system

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; helper and regulatory T cells predict the antigen amount 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. Together, this study provided a novel framework to improve our understanding of how the immune system adaptively learns the risks of diverse antigens.

immunology↗