rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: An
    title: Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation
  name:
    name part: Bowman, Gregory R.
    role:
      role term: editor.
  name:
    name part: Pande, Vijay S.
    role:
      role term: editor.
  name:
    name part: Noé, Frank.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: ne
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XII, 139 p. 65 illus., 48 illus. in color. online resource.
  abstract: 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.
  table of 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.
  note: edited by Gregory R. Bowman, Vijay S. Pande, Frank Noé.
  subject:
    topic: Medicine
  subject:
    topic: Molecular biology
  subject:
    topic: Physical chemistry
  subject:
    topic: Bioinformatics
  subject:
    topic: Computational biology
  subject:
    topic: Mathematics
  subject:
    topic: Physics
  subject:
    topic: Biomedicine
  subject:
    topic: Molecular Medicine
  subject:
    topic: Theoretical, Mathematical and Computational Physics
  subject:
    topic: Computer Appl. in Life Sciences
  subject:
    topic: Physical Chemistry
  subject:
    topic: Mathematics, general
  classification: QH506
  classification: 611.01816
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Experimental Medicine and Biology, 797
  identifier: 9789400776067
  identifier: http://dx.doi.org/10.1007/978-94-007-7606-7
  record info:
    record creation date: 131202
    record change date: 20151030211121.0
    record identifier: 978-94-007-7606-7