EAGER: Systems Analysis of Signaling Pathway towards Robust Differentiation

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1455800 Banerjee, Ipsita Pluripotent stem cells have the unique capability of giving rise to any tissue-specific cell type at an unlimited quantity. These outstanding properties make them a promising candidate as a renewable source of human tissue for research, pharmaceutical testing and cell-based therapies. The feasibility of obtaining organ-specific cell types by modulation of associated signaling pathways has already been demonstrated. However, the differentiated cell types at present remain limited in their yield and mature functionality. The next challenge in realizing the potential of stem cells is to ensure functional and homogenous maturation of these cells to the desired lineage. This goal can be achieved through a quantitative understanding of signal transduction and noise propagation through the signaling pathway. The topic of this EAGER proposal is to develop a quantitative and predictive representation of a critical signal transduction pathway driving stem cell differentiation. Once it is known how signals are transduced and how uncertainty is propagated during differentiation, it will be possible to design targeted interventions to achieve homogenous and efficient differentiation. The predictive platform to be developed in this project will inform the design of targeted interventions for efficient and homogenous differentiation of stem cells.In this proposal, the Transforming Growth Factor beta (TGF beta) signaling pathway will be analyzed for differentiating pluripotent stem cells by integrating quantitative experiments with predictive modeling and systems analysis. The TGF beta pathway plays a central role in inducing definitive endoderm differentiation in human pluripotent stem cell. Key components of the experimental platform will be the development of techniques for quantitative single cell level analysis of stem cell dynamics, in order to ensure accurate representation of a cell¡¦s dynamic response, unbiased by heterogeneity in the population. Specifically, the dynamics of TGF beta effectors molecules will be quantified in individual cell compartment using Green Fluorescent Protein fused effector proteins. The nucleo-cytoplasmic shuttling of these proteins will be quantified using Fluorescence Recovery after Photobleaching (FRAP) assay. Experimentally obtained single cell dynamics will be used to train the mathematical model of the pathway. Ensemble modeling will be used to integrate the single cell information to represent heterogeneity in cell population. Computational efficiency of this integration will be enhanced by a meta-modeling technique. Global sensitivity analysis of the ensemble model will allow identification of key mechanisms governing signal transduction. The model predictions will be experimentally validated at every step of the model development. The validated model will be further analyzed for propagation of uncertainty through the system by (i) experimental measurement of input variability and (ii) in-silico simulation of propagation of input variability to output molecules. The final outcome of the proposed project will be an experimentally validated predictive platform which will allow design of targeted perturbations to enhance differentiation efficiency while reducing heterogeneity in the differentiated population.