Overview What follows is a brief overview of the theory of Markov state models (MSMs), for a more detailed picture see some of the many good references. MSMs describe the first-order conformational kinetics of a system by specifying the conditional probability of transitioning from a state i at a time t to a state j at a time t + τ later. This information is summarized in the transition matrix T ij (τ ) = P (j, t + τ |i, t). Each state, i, is a collection of conformations which have similar kinetic properties. The transition matrix is a finite and discrete representation of the underlying Markovian transfer operator, T (τ ), which describes the dynamics of the system. The first left eigenvector φ 1 (in descending eigenvalue order λ i , with λ 1 = 1) corresponds to the stationary or equilibrium distribution, which we also label π; the second left eigenvector, φ 2 corresponds to the slowest conformational relaxation process; the third is the next slowest relaxation process and so on. The corresponding right eigenvectors, ψ i are normalized by π (so ψ 2 = 1 for all states). The eigenvalues are related to the timescales of these relaxation processes by: t i = -τ / log λ i . The transition matrix is said to be reversible if it obeys detailed balance π i T ij = π j T ji .