An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation

  • Gregory R. Bowman
  • Vijay S. Pande
  • Frank Noé

Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 797)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Gregory R. Bowman, Vijay S. Pande, Frank Noé
    Pages 1-6
  3. Marco Sarich, Jan-Hendrik Prinz, Christof Schütte
    Pages 23-44
  4. Jan-Hendrik Prinz, John D. Chodera, Frank Noé
    Pages 45-60
  5. Frank Noé, John D. Chodera
    Pages 61-74
  6. Frank Noé, Jan-Hendrik Prinz
    Pages 75-90
  7. Eric Vanden-Eijnden
    Pages 91-100
  8. Gregory R. Bowman, Frank Noé
    Pages 139-139

About this book

Introduction

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.

Keywords

Master equation models Molecular dynamics

Editors and affiliations

  • Gregory R. Bowman
    • 1
  • Vijay S. Pande
    • 2
  • Frank Noé
    • 3
  1. 1.University of CaliforniaBerkeleyUSA
  2. 2.Department of ChemistryStanford UniversityStanfordUSA
  3. 3.Freie Universität BerlinBerlinGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-007-7606-7
  • Copyright Information Springer Science+Business Media Dordrecht 2014
  • Publisher Name Springer, Dordrecht
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-94-007-7605-0
  • Online ISBN 978-94-007-7606-7
  • Series Print ISSN 0065-2598
  • Series Online ISSN 2214-8019
  • About this book