|
|
|||
|
||||
OverviewThe 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. Full Product DetailsAuthor: Gregory R. Bowman , Vijay S. Pande , Frank NoéPublisher: Springer Imprint: Springer Edition: 2014 ed. Volume: 797 Dimensions: Width: 17.80cm , Height: 1.50cm , Length: 25.40cm Weight: 4.734kg ISBN: 9789400776050ISBN 10: 9400776055 Pages: 139 Publication Date: 18 December 2013 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand We will order this item for you from a manufactured on demand supplier. Table of ContentsAn 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.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |