With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for their conduction. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art by means for knowledge sharing across domains, increased productivity and quality of complex simulation experiments, as well as reusability and automation. We demonstrate the practicality of our approach using case studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields), and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments, and therefore can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning following activities depending on the context of the simulation study, could be developed.

This is a preprint of an article published in Scientific Reports. The final authenticated version is available online at: https://doi.org/10.1038/s41598-022-08279-w . Electrical stimulation of biological samples such as tissues and cell cultures attracts growing attention due to its capability of enhancing cell activity, proliferation and differentiation. Eventually, profound knowledge of the underlying mechanisms paves the way for innovative therapeutic devices. Capacitive coupling is one option of delivering electric fields to biological samples and has advantages with regard to biocompatibility. However, the mechanism of interaction is not well understood. Experimental findings could be related to voltage-gated channels, which are triggered by changes of the transmembrane potential (TMP). Numerical simulations by the Finite Element method (FEM) provide a possibility to estimate the TMP. For realistic simulations of in vitro electric stimulation experiments, a bridge from the mesoscopic level down to the cellular level has to be found. A special challenge poses the ratio between the cell membrane (a few nm) and the general setup (some cm). Hence, a full discretization of the cell membrane becomes prohibitively expensive for 3D simulations. We suggest using an approximate FE method that makes 3D multi-scale simulations possible. Starting from an established 2D model, the chosen method is characterized and applied to realistic in vitro situations. A to date not investigated parameter dependency is included and tackled by means of Uncertainty Quantification (UQ) techniques. It reveals a strong, frequency-dependent influence of uncertain parameters on the modeling result.