ArticleThe Journal of chemical physics2012
Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty.
Article in The Journal of chemical physics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Article
- Markov State Models: To Optimize or Not to Optimize.Journal of chemical theory and computation · 2024Article
- Folding@home: Achievements from over 20 years of citizen science herald the exascale era.Biophysical journal · 2023Review
- Analysing ill-conditioned Markov chains.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Folding@home: achievements from over twenty years of citizen science herald the exascale era.ArXiv · 2023Article
- Emerging methods to model cardiac ion channel and myocyte electrophysiology.Biophysics reviews · 2023Review
- Bottom-up Coarse-Graining: Principles and Perspectives.Journal of chemical theory and computation · 2022Review
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Directed Inter-domain Motions Enable the IsdH Staphylococcus aureus Receptor to Rapidly Extract Heme from Human Hemoglobin.Journal of molecular biology · 2022Article
- Bottlebrush polymers with flexible enantiomeric side chains display differential biological properties.Nature chemistry · 2022Article
- The Two-Step Clustering Approach for Metastable States Learning.International journal of molecular sciences · 2021Review
- Simulation Study of the Plasticity of k-Turn Motif in Different Environments.Biophysical journal · 2020Article
- Hierarchical Markov State Model Building to Describe Molecular Processes.Journal of chemical theory and computation · 2020Article
- A new class of enhanced kinetic sampling methods for building Markov state models.The Journal of chemical physics · 2017Article
- Article
- Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking.Scientific reports · 2016Article
- Elucidation of the Dynamics of Transcription Elongation by RNA Polymerase II using Kinetic Network Models.Accounts of chemical research · 2016Article
- Dynamic Modelling Reveals 'Hotspots' on the Pathway to Enzyme-Substrate Complex Formation.PLoS computational biology · 2016Article
- Using Kinetic Network Models To Probe Non-Native Salt-Bridge Effects on α-Helix Folding.The journal of physical chemistry. B · 2016Article
- Percolation-like phase transitions in network models of protein dynamics.The Journal of chemical physics · 2015Article
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1 author.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.