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LEADER 02286nam a22003137i 4500
001 a11614108
003 SIRSI
005 20200516050002.0
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008 160310s2016 xx sm 000 0 eng d
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a| CSt c| CSt d| UtOrBLW
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a| McGibbon, Robert T. ?| UNAUTHORIZED
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a| Statistical models of protein conformational dynamics h| [electronic resource] / c| Robert T. McGibbon.
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c| 2016.
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a| 1 online resource.
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a| Submitted to the Department of Chemistry.
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a| Thesis (Ph.D.)--Stanford University, 2016.
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a| Understanding the conformational dynamics of biological macromolecules at atomic resolution remains a grand challenge at the intersection of biology, chemistry, and physics. Molecular dynamics (MD) --- which refers to computational simulations of the atomic-level interactions and equations of motions that give rise to these dynamics --- is a powerful approach that now produces immense quantities of time series data on the dynamics of these systems. Here, I describe a variety of new methodologies for analyzing the rare events in these MD data sets in an automatic, statically-sound manner, and constructing the appropriate simplified models of these processes. These techniques are rooted in the theory of reversible Markov chains. They include new classes of Markov state models, hidden Markov models, and reaction coordinate finding algorithms, with applications to protein folding and conformational change. A particular focus herein is on methods for model selection and model comparison, and computationally efficient algorithms.
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a| Pande, Vijay e| primary advisor. 4| ths =| ^A2467733
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a| Markland, Thomas E., e| advisor. 4| ths =| ^A2683416
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a| Martinez, Todd J. q| (Todd Joseph), d| 1968- e| advisor. 4| ths =| ^A2683417
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a| Stanford University. b| Department of Chemistry. =| ^A1328265
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a| (OCoLC-M)944735172
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a| 21 22
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u| http://purl.stanford.edu/sc364qr4692 x| SDR-PURL x| item
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a| DATE CATALOGED b| 20160314
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a| 3781 2016 M w| ALPHANUM c| 1 i| 36105223699070 l| UARCH-30 m| SPEC-COLL r| Y s| Y t| NONCIRC u| 3/11/2016
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a| INTERNET RESOURCE w| ASIS c| 1 i| 11614108-2001 l| INTERNET m| SUL r| Y s| Y t| SUL u| 3/11/2016 x| E-THESIS