# A State Model for the Analysis of Stem Cell Proliferation and Differentiation

## TL;DR

This paper introduces a dynamic state model for analyzing stem cell proliferation and differentiation, allowing variable division probabilities over time, with an estimation method validated through simulation.

## Contribution

It presents a novel time-varying probabilistic model for stem cell division dynamics and an estimation approach validated by simulation studies.

## Key findings

- The model accurately estimates division probabilities with small sample sizes.
- Variable probabilities over time improve the understanding of stem cell behavior.
- Simulation results demonstrate robustness of the estimation method.

## Abstract

Stem cells are characterized by their ability to self-renew, as well as to differentiate and give rise to new populations of cells. Stem cell divisions are crucial for generative processes that occur during early development, and later in adulthood to support tissue regenerative capabilities. This property of stemness, the ability of self-renewal or tissue-specific differentiation, is also observed in cancer cells facilitating the sustenance of tumor growth, and in bipotent megakaryocytic-erythroid progenitors (MEPs) to produce blood cells. We are interested in modeling the size of the stem cell population required to adequately generate tissues or colonies of cells. We develop a state model that characterizes stem cell divisions and the dynamic changes of the stem cell and differentiated cell populations. In our model, the probabilities of self-renewal and differentiation events that stem cells undergo can vary over time instead of remaining constant throughout the process. We provide an estimation method for the division probabilities and using a simulation study, we show that our method provides good estimates even with a small sample size.