Document Detail


Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty.
MedLine Citation:
PMID:  23039589     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Markov state models (MSMs)--or discrete-time master equation models--are a powerful way of modeling the structure and function of molecular systems like proteins. Unfortunately, MSMs with sufficiently many states to make a quantitative connection with experiments (often tens of thousands of states even for small systems) are generally too complicated to understand. Here, I present a bayesian agglomerative clustering engine (BACE) for coarse-graining such Markov models, thereby reducing their complexity and making them more comprehensible. An important feature of this algorithm is its ability to explicitly account for statistical uncertainty in model parameters that arises from finite sampling. This advance builds on a number of recent works highlighting the importance of accounting for uncertainty in the analysis of MSMs and provides significant advantages over existing methods for coarse-graining Markov state models. The closed-form expression I derive here for determining which states to merge is equivalent to the generalized Jensen-Shannon divergence, an important measure from information theory that is related to the relative entropy. Therefore, the method has an appealing information theoretic interpretation in terms of minimizing information loss. The bottom-up nature of the algorithm likely makes it particularly well suited for constructing mesoscale models. I also present an extremely efficient expression for bayesian model comparison that can be used to identify the most meaningful levels of the hierarchy of models from BACE.
Authors:
Gregory R Bowman
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  The Journal of chemical physics     Volume:  137     ISSN:  1089-7690     ISO Abbreviation:  J Chem Phys     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-10-08     Completed Date:  2013-02-20     Revised Date:  2013-10-17    
Medline Journal Info:
Nlm Unique ID:  0375360     Medline TA:  J Chem Phys     Country:  United States    
Other Details:
Languages:  eng     Pagination:  134111     Citation Subset:  IM    
Affiliation:
Department of Chemistry, University of California, Berkeley, California 94720, USA. gregoryrbowman@gmail.com
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Kinetics
Markov Chains*
Proteins / chemistry*
Thermodynamics
Uncertainty
Grant Support
ID/Acronym/Agency:
R01-GM050945/GM/NIGMS NIH HHS
Chemical
Reg. No./Substance:
0/Proteins
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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