Enhanced modeling via network theory: Adaptive sampling of markov state models

Gregory R. Bowman, Daniel L. Ensign, Vijay S. Pande

Research output: Contribution to journalArticle

118 Scopus citations

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 time scales, leaving modeling of most biologically relevant systems and time scales 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 systems dynamics).

Original languageEnglish
Pages (from-to)787-794
Number of pages8
JournalJournal of Chemical Theory and Computation
Volume6
Issue number3
DOIs
StatePublished - Mar 9 2010

Fingerprint Dive into the research topics of 'Enhanced modeling via network theory: Adaptive sampling of markov state models'. Together they form a unique fingerprint.

  • Cite this