(595d) Modeling Heterogeneity In Populations of Self-Renewing Human Embryonic Stem Cells

Despite the great potential of human pluripotent stem cells as a source of therapeutics for regenerative medicine, their efficient expansion and differentiation remains challenging. To that end, cell population modeling can contribute to the optimization and standardization of conditions for the efficient maintenance and differentiation of stem cells. In this work, we developed a population balance equation (PBE) model to describe and predict the dynamics of human embryonic stem cell (hESC) self-renewal. Intrinsic and extrinsic sources of stem cell population heterogeneity were considered. The model is based on experimental data obtained in our laboratory.

A PBE model was constructed for the heterogeneous self-renewal of hESCs with a physiological state vector comprising elements pertinent to hESC proliferation and the expression level of pluripotency markers. These variables were measured experimentally for a variety of hESC maintenance conditions and their rates of change were represented by ordinary and stochastic differential equations. Solutions to the model were obtained primarily by Monte Carlo methods. 

Model parameters pertinent to cell growth, division and marker expression were calculated by comparing simulation results with experimental data. Sensitivity analysis was also performed to gauge the effect of parameter perturbation on hESC heterogeneity. Additional experiments for the self-renewal of hESCs under different conditions were carried out to validate the model. The results correlate well with the flow cytometry data for hESC populations. The contribution of intrinsic sources considered here to hESC heterogeneity was found to be almost 40%. Finally, the PBE model successfully depicts both the proliferation of hESCs and maintenance of their pluripotency in a heterogeneous population. Current efforts concentrate on expanding the model to include additional sources of heterogeneity stemming from hESC differentiation.

The quantitative framework developed can complement and accelerate investigations utilizing stem cell populations by reducing costly and labor-intensive experimental procedures. Moreover, the model's predictive power can be useful in the design and operation of processes for the large-scale expansion and differentiation of human pluripotent stem cells to therapeutically useful cells.