Error analysis and efficient sampling in Markovian state models for molecular dynamics.
In previous work, we described a Markovian state model (MSM) for analyzing molecular-dynamics trajectories, which involved grouping conformations into states and estimating the transition probabilities between states. In this paper, we analyze the errors in this model caused by finite sampling. We give different methods with various approximations to determine the precision of the reported mean first passage times. These approximations are validated on an 87 state toy Markovian system. In addition, we propose an efficient and practical sampling algorithm that uses these error calculations to build a MSM that has the same precision in mean first passage time values but requires an order of magnitude fewer samples. We also show how these methods can be scaled to large systems using sparse matrix methods.
Singhal
Nina
N
Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
Pande
Vijay S
VS
D000465:Algorithms; D002627:Chemistry, Physical / Q000379:methods*; D003198:Computer Simulation; D008390:Markov Chains; D008956:Models, Chemical; D015233:Models, Statistical; D008968:Molecular Conformation; D015999:Multivariate Analysis; D011336:Probability; D015203:Reproducibility of Results; D012107:Research Design; D018401:Sample Size