 Hello, and welcome to this Biotechnology and Bioengineering Video Abstract. We would like to introduce our soon-to-be-published article, entitled Improving Efficiency of Human Pluripotent Stem Cell Differentiation Platforms, using an Integrated Experimental and Computational Approach. Human pluripotent stem cells have a great potential to be used for various tissue engineering applications. To meet industrial and clinical demands for specific somatic cell types that we can derive from human pluripotent stem cells, it will be essential to improve efficiency of current culture systems designed to differentiate human pluripotent stem cells to these somatic cell types of interest, and eventually translate these lab-scale differentiation systems to large-scale processes. To this end, we have designed a novel strategy for looking towards improving the efficiency of human pluripotent stem cell differentiation platforms. We have done this by identifying self-fake decisions that are potentially limiting to a differentiation process. To demonstrate how to use this approach, we used two previously reported epithelial differentiation systems from our lab as model systems. First, it is important to consider that throughout a culture where human pluripotent stem cell populations are induced to differentiate towards a particular lineage, that the culture is not a homogeneous entity, but rather a heterogeneous mixture of various cell subtypes or subpopulations. Using marker proteins to distinguish these various subpopulations in our epithelial differentiation systems, we used multi-parameter flow cytometry over a time course throughout differentiation to map out how the relative fractions of these subpopulations change with time. Combining this information with the total number of cells in culture as a function of time, we can generate data of each cell subpopulation's growth dynamics in culture. We then generated a set of ordinary differential equations derived from mass or material balances for each subpopulation to represent their kinetics in culture and fit this ODE-based model to our cell subpopulation dynamics data. This model fit was performed using a least squares regression by quantitatively estimating rate constants of each cell subpopulation's cell fate decisions, including cell renewal, differentiation, and cell death. After obtaining quantitative estimates of these rates, we performed sensitivity analyses on predicted rate constants to indicate which cell fate decisions, when changed, had the greatest impact on overall epithelial cell yield. Our results indicated that the final cell yield was limited primarily by either the self-renewal rate of an early progenitor state, or the self-renewal of the final differentiated state depending on which of the two differentiation protocols was employed. For scalability considerations, we also determined the relative impact of these cell fate decision rates was highly dependent on the size of the cell culture system. In addition, we demonstrated that our model accurately predicted a change in our overall epithelial cell yield, given a change in certain cell fate decision rates. Overall, we have outlined a novel approach for the quantitative analysis of any established laboratory scale, human pluripotent stem cell differentiation system, and this approach may ultimately help ease development to produce large quantities of cells for tissue engineering applications. Please contact us if you would like more information.