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Enhanced modeling via network theory: Adaptive sampling of Markov state models.

ABSTRACT: Computer simulations can complement experiments by providing insight into molecular kinetics with atomic resolution. Unfortunately, even the most powerful supercomputers can only simulate small systems for short timescales, leaving modeling of most biologically relevant systems and timescales intractable. In this work, however, we show that molecular simulations driven by adaptive sampling of networks called Markov State Models (MSMs) can yield tremendous time and resource savings, allowing previously intractable calculations to be performed on a routine basis on existing hardware. We also introduce a distance metric (based on the relative entropy) for comparing MSMs. We primarily employ this metric to judge the convergence of various sampling schemes but it could also be employed to assess the effects of perturbations to a system (e.g. determining how changing the temperature or making a mutation changes a system's dynamics).

SUBMITTER: Bowman GR

PROVIDER: S-EPMC3637129 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Enhanced modeling via network theory: Adaptive sampling of Markov state models.

Bowman Gregory R GR Ensign Daniel L DL Pande Vijay S VS

Journal of chemical theory and computation 20100101 3

Computer simulations can complement experiments by providing insight into molecular kinetics with atomic resolution. Unfortunately, even the most powerful supercomputers can only simulate small systems for short timescales, leaving modeling of most biologically relevant systems and timescales intractable. In this work, however, we show that molecular simulations driven by adaptive sampling of networks called Markov State Models (MSMs) can yield tremendous time and resource savings, allowing prev ...[more]

PMID: 23626502

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