Modeling and interpreting molecular kinetics from simulation data
- Brooke E. Husic.
- [Stanford, California] : [Stanford University], 2019.
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- Atomistic simulations provide detailed information about a molecular system's dynamics at finer time and length scales than experiments can access. However, the modeling and interpretation of simulation datasets in an unbiased and statistically sound way require dedicated algorithms. One analysis method is the so-called variational approach to conformational dynamics, which introduces an objective framework for the ranking of models. One such class of models are Markov state models (MSMs), which separate the configuration space explored by a simulated molecule, such as a protein, into discrete, disjoint states between which the transitions can be modeled as Markovian. In Chapter 1, I summarize the essentials of MSM construction and the variational approach. In Chapters 2 and 3, I describe systematic studies in varying MSM parameters, in pursuit of general trends for variationally optimal models of proteins, with a focus on clustering into states. In Chapters 4 and 5, I present algorithmic advances in comparing multiple related models and in coarse-graining MSMs, also using clustering. In Chapter 6, I summarize the current state of the art in MSM methods and identify frontiers in their application and methods development. Finally, in Chapters 7 and 8, I apply clustering to different problems, such as in fluid dynamics. I discuss the possibility of extending the methods motivated here to a broader class of dynamical systems in Chapter 9. Overall, the methods presented in this work focus on interpretability and statistical robustness.
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- Submitted to the Department of Chemistry.
- Thesis Ph.D. Stanford University 2019.