Analysis and Modelling of Pluripotent Stem Cells Dynamics

This thesis explores the dynamics of mouse embryonic stem cells (ESCs) during differentiation, using a combination of experimental data analysis and stochastic modeling. We begin by analyzing data from the experiment conducted by Strawbridge et al., which provides key insights into cell population behavior in vitro. Essential parameters are inferred from these data to construct different phenomenological stochastic models of the ESC population. The stochastic models are then implemented using the Gillespie algorithm to simulate cell behavior. Initially, a simple model is constructed to validate the alignment between the simulated lifetime distribution and its analytical solution. This model also reveals that the ”no change” rate, where the system remains in its previous state, has minimal impact on overall system behavior. Building on these findings, we construct both constant rate and time-dependent rate models to describe the system’s dynamics. While both models effectively replicate key metrics, such as cell count distribution and first passage times, the time-dependent model excels in capturing the non-monotonic behavior of the mean cell count over time, a characteristic observed in the experimental data. This study provides a foundational framework for developing more sophisticated models of stem cell behavior, which could eventually guide the creation of targeted therapies for conditions such as degenerative diseases and tissue repair.