Over the past years, we have observed an abundance of approaches for modeling dynamic 3D scenes and their individual elements. Most of them rely on Neural Radiance Fields (NeRF) to store the scene's shape, coloring, and dynamics encoded in the weights of a neural network. Contrary to these approaches, Gaussian Splatting (GS) allows us to represent the scene's structure using Gaussian components, while the neural network solely handles its dynamic aspects. This approach allows for fast rendering and extracting each element of such a dynamic scene. However, modification of such objects over time is challenging. GS enhanced with Deformed Control Points partially solves this issue. However, this approach necessitates selecting elements that need to be kept fixed, as well as centroids that should be adjusted throughout editing. Moreover, this task poses additional difficulties regarding the re-productivity of such editing. To address this, we propose Dynamic Multi-Gaussian Soup (D-MiSo), which allows us to model the mesh-inspired representation of dynamic GS. Additionally, we propose a strategy of linking parameterized Gaussian splats, forming a Triangle Soup with the estimated mesh. Consequently, we can separately construct new trajectories for the 3D objects composing the scene. Thus, we can make the scene's dynamic editable over time or while maintaining partial dynamics.