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An Introduction to Markov S...
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
Monografía
monografia Rebiun36042033 https://catalogo.rebiun.org/rebiun/record/Rebiun36042033 m o d | cr nn 008mamaa 131202s2014 ne s 00 0 eng d 9789400776067 9789400776074 9789400776050 9789402407624 10.1007/978-94-007-7606-7 doi UMA.RE eng MBGR bicssc PSD bicssc SCI049000 bisacsh MED067000 bisacsh 611.01816 23 An Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation Recurso electrónico] edited by Gregory R. Bowman, Vijay S. Pande, Frank Noé Dordrecht Springer Netherlands 2014 Dordrecht Dordrecht Springer Netherlands Dordrecht Springer Netherlands Imprint: Springer 2014 Dordrecht Dordrecht Springer Netherlands Imprint: Springer XII, 139 p. 65 il., 48 il. col XII, 139 p. 65 il., 48 il. col Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Advances in Experimental Medicine and Biology 797 Bibliographic Level Mode of Issuance: Monograph 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 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 English Medicine Biology- Data processing Chemistry, Physical organic Mathematics Molecular Medicine. Theoretical, Mathematical and Computational Physics. Computer Appl. in Life Sciences. Physical Chemistry. Mathematics, general. Medicine Biology- Data processing Chemistry, Physical organic Mathematics Molecular Medicine Theoretical, Mathematical and Computational Physics Computer Appl. in Life Sciences Physical Chemistry Mathematics, general Bowman, Gregory R ed. lit Pande, Vijay S ed. lit Noé, Frank ed. lit 94-007-7605-5 Advances in Experimental Medicine and Biology 797