A Versatile Stochastic Model for Stem Cell Growth

Stem cell research promises to revolutionize regenerative medicine and to open new avenues for fighting cancer. Since once differentiated stem cells are incapable of mitosis, a crucial component of stem cell research is modeling the proliferative capacity of the undifferentiated stem cell phenotype. Therefore, it is an important research topic to identify mathematical growth models for stem cells that are inherently heterogeneous and account in a unified way for mitosis, quiescence, senescence and death. Somewhat surprisingly, the overwhelming majority of stem cell growth models proposed in the literature, including the well known Sherley and hyperbolastic models, are deterministic. While simple and tractable, deterministic models are too rigid lacking the flexibility and versatility needed to accurately describe stem cell population dynamics that have high variability and heterogeneity. We believe only stochastic growth model have a chance of making real progress. With this in mind, we propose a simple and versatile stochastic model that can be used to model and predict the proliferation of stem cells. Our model is a modified three-parameter birth-and-death model where the parameters can be fine-tuned to accommodate the proliferative heterogeneity of stem cell populations. We have performed a preliminary validation of our model using NIH-published stem cell growth data. Our model can accurately represent the dynamics of stem cell proliferation for both embryonic and adult mesenchymal stem cells.