Stem cells are vital for the development and homeostasis of organisms. Consequently, understanding their functions and population dynamics is key to unraveling the mechanisms that control homeostasis and development. To gain such insights into stem cell differentiation and dynamics, we decided to model the Endomesoderm Gene Regulatory Network (GRN) of the Purple Sea Urchin (Strongylocentrotus purpuratus) using the Reasoning Engine for Interaction Networks (RE:IN) programming language. This was to account for the multiple possible mechanisms in line with experimental results, as RE:IN allows modeling over abstract network topologies. Because no RE:IN models of the GRN are currently unavailable, we aimed to analyze whether RE:IN models could satisfy experimental constraints, even alongside perturbations in network genes. To accomplish this, we created RE:IN models of the GRN using tools and programs we developed to efficiently generate models and experimental constraints and evaluate them. Through evaluating experimental constraints, we found that RE:IN modeling is likely not yet able to fully capture the intricacies of the GRN. While complete experimental constraints could not be satisfied, we found transitions between consecutive constraints to be satisfiable. Introducing temporal flexibility also allowed some constraint sets of 3 hours to become satisfiable. Perturbing the most restrictive satisfiable constraints did not affect their satisfiability, but this likely stems from them not being restrictive enough rather than RE:IN models being robust. This work is an important first step into RE:IN modeling of the Endomesoderm GRN and lays important foundations for future research.