We present a description of transition rate matrices of models for Stochastic Automata Networks (SAN) in their representation as operators in Tensor Train (TT) format. The collection contains models from real-life applications both taken from references directly or adaptations that still have a realistic interpretation. A classification is given according to the properties of interest of each model. Identifiers based on such properties are provided. A brief description of each model is given. This document both provides an illustration of the big variety of applications associated with Stochastic Automata Network as it also allows testing and tuning algorithms for the solution of relevant associated problems as, for instance, the determination of their steady-state.