Assisting Identifiability Analysis of Large-Scale Dynamical Models with Decision Trees: DecTrees and Interactive Menus

Parameter Identifiability Analysis (IA) of large scale dynamical models is a technically tedious process. The realization of this task can benefit from a platform assisting the easier task of determining small identifiable parameter subsets. Then, these subsets get enlarged on an iterative basis until a maximal identifiable parameter set is obtained. Moreover, the employment of mutual combinations of many candidates of mathematical tools and algorithmic variants for the underlying interrelated computational subtasks within Parameter Estimation (PE) needs to be examined. This work demonstrates the developer-oriented software DecTrees employed for establishing such a platform. It provides a compact generic implementation of Decision Trees (DTs). The main elements are represented via context-free C++ components describing nodes and conditioned edges. By extending these white-boxes with a context, meaningful applicative decision systems are established. This is the case with the software InteractiveMenus useful for simplifying the configuration of complicated computational tasks. The advantages are emphasized with a realistic IA application. The software is open-source and is available under https://github.com/AtiyahElsheikh/DecTrees.