ArticleThe journal of physical chemistry. B2013
Extracting conformational memory from single-molecule kinetic data.
Article in The journal of physical chemistry. B, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Single-molecule reaction-diffusion.Biophysical journal · 2025Article
- Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.The Journal of chemical physics · 2024Article
- Integrative generalized master equation: A method to study long-timescale biomolecular dynamics via the integrals of memory kernels.The Journal of chemical physics · 2023Article
- Single-Molecule Reaction-Diffusion.bioRxiv : the preprint server for biology · 2023Article
- Transcending Markov: non-Markovian rate processes of thermosensitive TRP ion channels.Royal Society open science · 2023Article
- Single-photon smFRET: II. Application to continuous illumination.Biophysical reports · 2023Article
- Single-photon smFRET. I: Theory and conceptual basis.Biophysical reports · 2023Article
- Gene expression model inference from snapshot RNA data using Bayesian non-parametrics.Nature computational science · 2023Article
- Learning continuous potentials from smFRET.Biophysical journal · 2023Article
- Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap.iScience · 2022Article
- Extraction of rapid kinetics from smFRET measurements using integrative detectors.Cell reports. Physical science · 2021Article
- Inferring effective forces for Langevin dynamics using Gaussian processes.The Journal of chemical physics · 2020Article
- Quantitative Kinetic Models from Intravital Microscopy: A Case Study Using Hepatic Transport.The journal of physical chemistry. B · 2019Article
- Single-Molecule Test for Markovianity of the Dynamics along a Reaction Coordinate.The journal of physical chemistry letters · 2018Article
- Directly measuring single-molecule heterogeneity using force spectroscopy.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Error-based extraction of states and energy landscapes from experimental single-molecule time-series.Scientific reports · 2015Article
- Article
- Data-driven techniques for detecting dynamical state changes in noisily measured 3D single-molecule trajectories.Molecules (Basel, Switzerland) · 2014Article
- Single molecule conformational memory extraction: p5ab RNA hairpin.The journal of physical chemistry. B · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Single-molecule data often come in the form of stochastic time trajectories. A key question is how to extract an underlying kinetic model from the data. A traditional approach is to assume some discrete state model, that is, a model topology, and to assume that transitions between states are Markovian. The transition rates are then selected according to which ones best fit the data. However, in experiments, each apparent state can be a broad ensemble of states or can be hiding multiple interconverting states. Here, we describe a more general approach called the non-Markov memory kernel (NMMK) method. The idea is to begin with a very broad class of non-Markov models and to let the data directly select for the best possible model. To do so, we adapt an image reconstruction approach that is grounded in maximum entropy. The NMMK method is not limited to discrete state models for the data; it yields a unique model given the data, it gives error bars for the model, and it does not assume Markov dynamics. Furthermore, NMMK is less wasteful of data by letting the entire data set determine the model. When the data warrants, the NMMK gives a memory kernel that is Markovian. We highlight, by numerical example, how conformational memory extracted using this method can be translated into useful mechanistic insight.
Indexed as
Identifiers
What OpenQuestion holds
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.