Identification of cell-fate determinants for directing stem cell differentiation 
remains a challenge. Moreover, little is known about how cell-fate determinants 
are regulated in functionally important subnetworks in large gene-regulatory 
networks (i.e., GRN motifs). Here we propose a model of stem cell 
differentiation in which cell-fate determinants work synergistically to 
determine different cellular identities, and reside in a class of GRN motifs 
known as feedback loops. Based on this model, we develop a computational method 
that can systematically predict cell-fate determinants and their GRN motifs. The 
method was able to recapitulate experimentally validated cell-fate determinants, 
and validation of two predicted cell-fate determinants confirmed that 
overexpression of ESR1 and RUNX2 in mouse neural stem cells induces neuronal and 
astrocyte differentiation, respectively. Thus, the presented GRN-based model of 
stem cell differentiation and computational method can guide differentiation 
experiments in stem cell research and regenerative medicine.