Landscape inferred from gene expression data governs pluripotency in embryonic stem cells.

Embryonic stem cells (ESCs) can differentiate into diverse cell types and have the ability of self-renewal. Therefore, the study of cell fate decisions on embryonic stem cells has far-reaching significance for regenerative medicine and other biomedical fields. Mathematical models have been used to study emryonic stem cell differentiation. However, the underlying mechanisms of cell differentiation and lineage reprogramming remain to be elucidated. Especially, how to integrate the computational models with quantitative experimental data is still challenging. In this work, we developed a data-constrained modelling approach, and established a model of mouse embryonic stem cells. We used the truncated moment equations (TME) method to quantify the potential landscape of the ESC network. We identified two attractors on the landscape, which represent the embryonic stem cell (ESC) state and differentiated cell (DC) state, respectively, and quantified high dimensional biological paths for differentiation and reprogramming process. Through identifying the optimal combinations of gene targets based on a landscape control strategy, we offered some predictions about the key regulatory factors that govern the differentiation and reprogramming in ESCs.