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008			131202s2014 ne | s |||| 0|eng d
020			_a9789400776067
024	7		_a10.1007/978-94-007-7606-7 _2doi
040			_dES-MaUEC
050		4	_aQA274.7 _bB696 2014
100	1		_aBowman, Gregory R. _eeditor literario _986273 _0Local
245	1	3	_aAn Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation _cedited by Gregory R. Bowman, Vijay S. Pande, Frank Noé.
260			_aDordrecht, Netherlands _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XII, 139 p.) _b65 ilustraciones, 48 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvances in Experimental Medicine and Biology _x0065-2598 _v797
505	0		_aAn 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.
520			_aThe 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 statesâ€{u3974}s of rapidly interconverting conformationsâ€{u1BA4} the rates of transitioning between states.Â{u4A29}s 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.
650		7	_2embne _9668313 _aMarkov, Procesos de
700	1		_aPande, Vijay S _eeditor literario _986274 _0Local
700	1		_aNoé, Frank _eeditor literario _986275 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-94-007-7606-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
907			_a.b12825098 _b10-10-17 _c01-10-14
942			_2lcc _cLE
945			_aQA274.7 B696 2014 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11554307 _z06-04-17
988			_aEBSPRINGER
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