Human embryonic stem cells (hESC) have been a major cell source for research in regenerative medicine due to the demonstration of properties of self-renewal and efficient lineage specific differentiation, both on additions of external cues. Self-renewal provides the potential to extract large quantities of naïve cells that can then be differentiated to clinically relevant mature lineages. While there exists significant proof-of-concept to transform stem cells to the desired lineage, generating fully functional cell types is still an unmet challenge. A major reason for this is our limited understanding of the complexity of the transformation process. The overarching goal of this PhD research was to provide strategies to bring mathematical modeling into the realm of stem cell research, particularly to analyze the complex regulatory network of signaling events controlling cell fate. This work focused on the signaling pathways that in concert control the balance of self-renewal and endoderm differentiation of hESCs.
We proposed a framework for developing mechanistic understanding from disparate signaling pathways using combinations of data-driven and equation based models. As a first step, we analyzed growth factor mediated PI3K/AKT pathway that must remain highly active to inhibit differentiation in self-renewal state. Using an integrated approach of mechanistic modeling, systems analysis and experimental validation we identified the role of a regulatory process (negative feedback) in maintaining signal amplitudes and controlling the propagation of parameter uncertainty down the pathway in the self-renewal state. To analyze endoderm differentiation, biclustering with bootstrapping formulation was used to identify co-regulated transcription factor patterns under a combinatorial modulation of endoderm inducing signaling pathways. In the final step, a detailed mechanistic analysis was done to characterize the dynamic features of TGF-β/SMAD pathway for inducing endoderm. Utilizing a dynamic Bayesian network formulism, AKT mediated crosstalk connections were inferred from the detailed time series data. Modeling of competing AKT-SMAD interactions followed by parametric ensemble analysis enabled identification of plausible hypotheses that could explain experimental observations. Using our integrated approach, we can now begin to rationally optimize for desirable fate of hESCs with reduced variability and accelerate the path towards therapeutic applications of hESCs
