The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems.

The Markov state model (MSM) approach aims to address two key challenges of molecular simulation:

1) How to reach long timescales using short simulations of detailed molecular models.

2) How to systematically gain insight from the resulting sea of data.

MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states sets of rapidly interconverting conformations and the rates of transitioning between states.This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways.

This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation."

This text presents an introduction to the field of statistical physics of macromolecules, from the basic concepts to modern achievements. Applications in various fields of polymer physical chemistry and molecular biophysics are also covered, as are: the fundamentals of statistical theory of polymer solutions and melts; classical, sealing and renormalization group approaches; the main ideas of statistical theories of polymer liquid crystals, polymer networks and polyelectrolytes; dynamic viscoelastic behavior of polymer systems; models of house, Zimm and reptation concepts; and specific features of main biopolymers - DNA and proteins. This English edition also includes sections describing the most important recent advances such as: statistical theory of DNA gel-electrophoresis, polymers at interfaces, and dynamics of concentrated solutions of rigid polymers.

The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models. 2) How to systematically gain insight from the resulting sea of data. MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states sets of rapidly interconverting conformations and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation.