Simulation experiments are an essential part of computational science and engineering. The use of simulation models is widely adopted by practitioners from diverse areas of applied sciences. Nevertheless, simulations are rarely replicated due to reuse and maintenance challenges related to models and data. In this respect, we propose that crucial and labor intensive parts of simulation experiments could be supported by model transformations. This work focuses on model-driven engineering practices to enable replicable and reusable simulation experiments. These practices are used to devote researchers' time to analyze the system under investigation rather than dealing with low level details to create a working environment. The results of our framework development work are presented.