| Many important processes in biology occur at the molecular scale. A detailed understanding of these processes can lead to significant advances in the medical and life sciences---for example, many diseases are caused by protein aggregation or misfolding. One approach to studying these systems is to use physically-based computational simulations to model the interactions and movement of the molecules. While molecular simulations are computationally expensive, it is now possible to simulate many independent molecular dynamics trajectories in a parallel fashion by using distributed computing methods such as Folding Home.; The analysis of these large, high-dimensional, data sets presents new computational challenges. This dissertation presents a novel approach to analyzing large ensembles of molecular dynamics trajectories to generate a compact model of the dynamics. The model groups conformations into discrete states and describes the dynamics as Markovian, or history-independent, transitions between the states. We will discuss why the Markovian state model (MSM) is suitable for macro-molecular dynamics, and how it can be used to answer many interesting and relevant questions about the molecular system. We will also present new approaches for many of the computational and statistical challenges in building such a model, specifically a novel algorithm for defining the states, methods for comparing between different state definitions and determining the optimal number of states, efficient error analysis techniques to determine the statistical reliability, and adaptive algorithms to efficiently design new simulations. |
