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An introduction to Markov State Models and their application to long timescale molecular simulation [electronic resource] / Gregory R. Bowman, Vijay S. Pande, Frank Noé, editors.

Bowman, Gregory R., editor of compilation.; Pande, Vijay S., editor of compilation.; Noé, Frank, editor of compilation.
2014
QA274.7
Available Online
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Title
An introduction to Markov State Models and their application to long timescale molecular simulation [electronic resource] / Gregory R. Bowman, Vijay S. Pande, Frank Noé, editors.
ISBN
9789400776067 electronic book
9400776063 electronic book
9789400776050
DOI
https://doi.org/10.1007/978-94-007-7606-7
Published
Dordrecht : Springer, 2014.
Language
English
Description
1 online resource (xii, 139 pages) : illustrations (some color).
Item Number
10.1007/978-94-007-7606-7 doi
Call Number
QA274.7
Dewey Decimal Classification
519.2/33
Summary
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.
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Access limited to authorized users.
Source of Description
Description based on online resource; title from PDF title page (SpringerLink, viewed December 10, 2013).
Added Author
Bowman, Gregory R., editor of compilation.
Pande, Vijay S., editor of compilation.
Noé, Frank, editor of compilation.
Series
Advances in experimental medicine and biology ; v.797. 0065-2598
Linked Resources
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All Resources
An overview and practical guide to building Markov state models
Markov model theory
Estimation and Validation of Markov models
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Analysis of Markov models
Transition Path Theory
Understanding Protein Folding using Markov state models
Understanding Molecular Recognition by Kinetic Network Models Constructed from Molecular Dynamics Simulations
Markov State and Diffusive Stochastic Models in Electron Spin Resonance
Software for building Markov state models.

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