Evidence map›Paper›PMID 19368472›Full record

ArticleThe Journal of chemical physics2009

Using stochastic models calibrated from nanosecond nonequilibrium simulations to approximate mesoscale information.

Christopher P Calderon, Lorant Janosi, Ioan Kosztin

Abstract read
In one paragraph

Article in The Journal of chemical physics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Multiscale modeling of biological functions.Physical chemistry chemical physics : PCCP · 2011
    Review
  4. Article
  5. P-SPLINES USING DERIVATIVE INFORMATION.Multiscale modeling & simulation : a SIAM interdisciplinary journal · 2010
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christopher P CalderonDepartment of Computational and Applied Mathematics, Rice University, Houston, Texas 77005, USA. calderon@rice.edu
Lorant Janosi
Ioan Kosztin

Funding

NANOBIOLOGY INSTITUTIONAL TRAINING GRANT(RMI)T90DK070121 · NIDDK · UNIVERSITY OF HOUSTON · PI PETTITT, BERNARD MONTGOMERY · 2004 to 2008
$1.5M
NIDDK NIH HHS T90 DK070121NIDDK NIH HHS T90 DK070121-04
6 · The paper itself

Abstract

We demonstrate how the surrogate process approximation (SPA) method can be used to compute both the potential of mean force along a reaction coordinate and the associated diffusion coefficient using a relatively small number (10-20) of bidirectional nonequilibrium trajectories coming from a complex system. Our method provides confidence bands which take the variability of the initial configuration of the high-dimensional system, continuous nature of the work paths, and thermal fluctuations into account. Maximum-likelihood-type methods are used to estimate a stochastic differential equation (SDE) approximating the dynamics. For each observed time series, we estimate a new SDE resulting in a collection of SPA models. The physical significance of the collection of SPA models is discussed and methods for exploiting information in the population of estimated SPA models are demonstrated and suggested. Molecular dynamics simulations of potassium ion dynamics inside a gramicidin A channel are used to demonstrate the methodology, although SPA-type modeling has also proven useful in analyzing single-molecule experimental time series [J. Phys. Chem. B 113, 118 (2009)].

Indexed as

DiffusionBacillusComputational BiologyComputer SimulationGramicidinIon TransportModels, ChemicalPotassiumStochastic ProcessesThermodynamicsGramicidinPotassium

Identifiers

PMID19368472
PMCPMC2832035

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.