Simulating biological systems in-silico has become a common method to provide deeper insights into the regarding systems than in-vitro investigations. Classic approaches like differential equations, Boolean networks or Markov chains are performant, but usually cannot express often desired spatial features. For that reason particle based simulators like Smoldyn, GFRD or ReaDDy came up that are able to examine a reaction network in space, but usually not on a large timescale. Due to methodological and instrumental restrictions it still is elusive to simulate complex systems in full detail over a large period of time. Coarse-graining methods allow the reduction of a highdetailed system into a little-detail system, whereby the qualitative behavior of the simulated systems is conserved. The aim of this thesis is to derive techniques that allow a detailed simulation on a large timescale. For that reason, firstly, a pathway is developed that translates a reaction network between a set of species into space using properties from literature, like mass and diffusion coefficient of all species. Secondly, coarse-graining methods are developed that are automatized applicable to real biological systems. These methods allow the simulation of the generated particle based model in a feasible amount of time, whereby the focus lies on the reduction of the simulation complexity rather than the models complexity. Thirdly, a novel simulation tool is established that allows a simplified study of self assembling processes by coarse-graining the diffusion. To study these methods at work, two models of biological systems emerged in the scope of this thesis, namely the spindle assembly checkpoint and PML nuclear bodies. The spindle assembly checkpoint (SAC) guards proper cell division by prolonging the metaphase until all 92 sister chromatids are aligned properly. Even a single unattached kinetochore keeps the SAC active, which is rapidly inactivated after the last attachment.