Stem cells play a central role in the development of organisms; hence, studying their gene regulatory networks (GRNs) is of great importance. The Reasoning Engine for Interaction Networks (RE:IN) is a toolset that supports modeling of GRNs to investigate their dynamics systematically and efficiently and make new predictions. Here we constructed a RE:IN model of the GRN which describes the regulation of gene expression in purple sea urchin stem cells. It consists of a constrained abstract Boolean network - a collection of Boolean networks, each corresponding to a possible structure and logic of the GRN consistent with experiments. We examined the model’s compatibility with observed behavior and explored its robustness. To this end, we developed several new methods for modeling GRNs in RE:IN. These include methods for handling cases where models don’t behave in accordance with observed behavior, tools for fast and efficient RE:IN modeling, and tools for synthesizing complex conditions in which models can be tested. Our results show that the current model cannot behave according to the entirety of the expected behavior. Moreover, we show that the model is robust to perturbations in a subset of key genes in the network. These results suggest that there is still work to be done to better capture the intricacies of this GRN in RE:IN. Furthermore, the tools we developed proved to be useful and may serve future research on GRNs within the RE:IN framework.