Presentation Information
[POS-03]Mathematical modeling of hair follicle stem cell dynamics
*Yu Sasagawa1, Hiroshi Haeno1 (1. Tokyo University of Science, Research Institute for Biomedical Sciences (Japan))
Keywords:
mathematical model,alopecia,stem cells
Hair loss such as AGA (Androgenetic Alopecia) and alopecia areata is a serious concern for many individuals. The incidence of AGA in Japanese men is 30%. Since hair loss significantly impacts the appearance of individuals, the quality of life can be decreased.
To address these problems and clarify the mechanisms of hair loss, we focused on hair follicle stem cells which play a crucial role in hair growth and maintenance and constructed a stochastic model to investigate the dynamics of hair follicle stem cells. In the model, each cell is stochastically chosen to divide and die and maintain the stem cell population over time. We performed simulations to record the probability of hair follicle stem cell extinction and investigate the parameter dependence. We found that the number and turnover rate of hair follicle stem cells were important to prevent the extinction of the population. This finding provides a biological basis for the common observation that hair loss increases with age. Using these results, we next constructed a mathematical model of hair tissue to evaluate how the stem cell dynamics affected the hair tissue. We fitted our model to public hair loss data obtained from over 1000 people to estimate realistic model parameters. The fitting process involved adjusting model parameters such as the stem cell differentiation rate "u" and relative fitness of stem cells "r" to best match observed patterns of hair follicle density over time. This analysis allowed us to estimate key biological parameters associated with hair loss progression. In the future, we plan to extend the model to assess the effectiveness of medications for treating hair loss.
To address these problems and clarify the mechanisms of hair loss, we focused on hair follicle stem cells which play a crucial role in hair growth and maintenance and constructed a stochastic model to investigate the dynamics of hair follicle stem cells. In the model, each cell is stochastically chosen to divide and die and maintain the stem cell population over time. We performed simulations to record the probability of hair follicle stem cell extinction and investigate the parameter dependence. We found that the number and turnover rate of hair follicle stem cells were important to prevent the extinction of the population. This finding provides a biological basis for the common observation that hair loss increases with age. Using these results, we next constructed a mathematical model of hair tissue to evaluate how the stem cell dynamics affected the hair tissue. We fitted our model to public hair loss data obtained from over 1000 people to estimate realistic model parameters. The fitting process involved adjusting model parameters such as the stem cell differentiation rate "u" and relative fitness of stem cells "r" to best match observed patterns of hair follicle density over time. This analysis allowed us to estimate key biological parameters associated with hair loss progression. In the future, we plan to extend the model to assess the effectiveness of medications for treating hair loss.