Validation of Markov state models using Shannon’s entropy.

S. Park and V. S. Pande. Journal of Chemical Physics (2006)

SUMMARY: Markov State Models (MSM’s) have become a major part of how Folding@home calculations are performed. In particular, the MSM technique is at the heart of how one can divide complex calculations like protein folding or lipid vesicle dynamics on 10,000 to 100,000 CPU’s — i.e. how distributed computing can tackle complex problems. This paper presents a new way to test the validity of MSM’s generated to make sure that the models are suitable and self-consistent.

ABSTRACT: Markov state models are kinetic models built from the dynamics of molecular simulation trajectories by grouping similar configurations into states and examining the transition probabilities between states. Here we present a procedure for validating the underlying Markov assumption in Markov state models based on information theory using Shannon’s entropy. This entropy method is applied to a simple system and is compared with the previous eigenvalue method. The entropy method also provides a way to identify states that are least Markovian, which can then be divided into finer states to improve the model.