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The Resource An introduction to Markov State Models and their application to long timescale molecular simulation, Gregory R. Bowman, Vijay S. Pande, Frank Noé, editors, (electronic book)

An introduction to Markov State Models and their application to long timescale molecular simulation, Gregory R. Bowman, Vijay S. Pande, Frank Noé, editors, (electronic book)

Label
An introduction to Markov State Models and their application to long timescale molecular simulation
Title
An introduction to Markov State Models and their application to long timescale molecular simulation
Statement of responsibility
Gregory R. Bowman, Vijay S. Pande, Frank Noé, editors
Contributor
Editor of compilation
Subject
Language
eng
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
Member of
Cataloging source
GW5XE
Dewey number
519.2/33
Illustrations
illustrations
Index
no index present
LC call number
QA274.7
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Bowman, Gregory R.
  • Pande, Vijay S.
  • Noé, Frank
Series statement
Advances in Experimental Medicine and Biology,
Series volume
volume 797
http://library.link/vocab/subjectName
  • Markov processes
  • Biology
Label
An introduction to Markov State Models and their application to long timescale molecular simulation, Gregory R. Bowman, Vijay S. Pande, Frank Noé, editors, (electronic book)
Instantiates
Publication
Antecedent source
unknown
Color
multicolored
Contents
An overview and practical guide to building Markov state models -- Markov model theory -- Estimation and Validation of Markov models -- Uncertainty estimation -- 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
Control code
SPR870680524
Dimensions
unknown
Extent
1 online resource (xii, 139 pages)
File format
unknown
Form of item
online
Isbn
9789400776050
Level of compression
unknown
Other control number
10.1007/978-94-007-7606-7
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
Label
An introduction to Markov State Models and their application to long timescale molecular simulation, Gregory R. Bowman, Vijay S. Pande, Frank Noé, editors, (electronic book)
Publication
Antecedent source
unknown
Color
multicolored
Contents
An overview and practical guide to building Markov state models -- Markov model theory -- Estimation and Validation of Markov models -- Uncertainty estimation -- 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
Control code
SPR870680524
Dimensions
unknown
Extent
1 online resource (xii, 139 pages)
File format
unknown
Form of item
online
Isbn
9789400776050
Level of compression
unknown
Other control number
10.1007/978-94-007-7606-7
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote

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