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Description:
The enormous time lag between fast atomic motion and complex protein folding events makes it almost impossible to compute molecular dynamics on a high resolution. A common way to tackle this problem is to model the system dynamics as a Markov process. Yet for large molecular systems the resulting Markov chains can hardly be handled due to the curse of dimensionality. Coarse graining methods can be used to reduce the dimension of a Markov chain, but it is still unclear how far the coarse grained Markov chain resembles the original system. In order to answer this question, two different coarse-graining methods were analysed and compared: a classical set-based reduction method and an alternative subspace-based approach, which is based on membership vectors instead of sets. On the basis of a small toy system, it could be shown, that in contrast to the subset-based approach, the subspace-based reduction method preserves the Markov property as well as the essential dynamics of the original system.