A Graph-Algorithmic Approach for the Study of Metastability in Markov   Chains
  Large continuous-time Markov chains with exponentially small transition rates arise in modeling complex systems in physics, chemistry and biology. We propose a constructive graph-algorithmic approach to determine the sequence of critical timescales at which the qualitative behavior of a given Markov chain changes, and give an effective description of the dynamics on each of them. This approach is valid for both time-reversible and time-irreversible Markov processes, with or without symmetry. Central to this approach are two graph algorithms, Algorithm 1 and Algorithm 2, for obtaining the sequences of the critical timescales and the hierarchies of Typical Transition Graphs or T-graphs indicating the most likely transitions in the system {without and with} symmetry respectively. The sequence of {critical} timescales includes the subsequence of the reciprocals { of the real parts } of eigenvalues. Under a certain assumption, we prove sharp asymptotic estimates for eigenvalues (including prefactors) and show how one can extract them from the output of Algorithm 1. We discuss the relationship between Algorithms 1 and 2, and explain how one needs to interpret the output of Algorithm 1 if it is applied in the case with symmetry instead of Algorithm 2. Finally, we analyze an example motivated by R. D. Astumian's model of the dynamics of kinesin, a molecular motor, by means of Algorithm 2. 
Tingyue Gan and Maria Cameron
math.PR