ArticleJournal of chemical theory and computation2014
Inferring Microscopic Kinetic Rates from Stationary State Distributions.
Article in Journal of chemical theory and computation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Article
- The Maximum Caliber Variational Principle for Nonequilibria.Annual review of physical chemistry · 2020Review
- An efficient strategy to estimate thermodynamics and kinetics of G protein-coupled receptor activation using metadynamics and maximum caliber.The Journal of chemical physics · 2018Article
- LinkingeLife · 2018Article
- Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber.Biophysical journal · 2017Article
- Stratified UWHAM and Its Stochastic Approximation for Multicanonical Simulations Which Are Far from Equilibrium.Journal of chemical theory and computation · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We present a principled approach for estimating the matrix of microscopic transition probabilities among states of a Markov process, given only its stationary state population distribution and a single average global kinetic observable. We adapt Maximum Caliber, a variational principle in which the path entropy is maximized over the distribution of all possible trajectories, subject to basic kinetic constraints and some average dynamical observables. We illustrate the method by computing the solvation dynamics of water molecules from molecular dynamics trajectories.
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Registered trials
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.