Evidence map›Paper›PMID 23296778›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2013

Modeling spatial aspects of intracellular dopamine signaling.

Kim T Blackwell, Lane J Wallace, BoHung Kim, Rodrigo F Oliveira, Wonryull Koh

Open access · greenAbstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
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

3 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Visualization of cyclic nucleotide dynamics in neurons.Frontiers in cellular neuroscience · 2014
    Review
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

5 authors at 3 institutions in 1 country.

Kim T BlackwellThe Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA. kblackw1@gmu.edu
Lane J Wallace
BoHung Kim
Rodrigo F Oliveira
Wonryull Koh
Institute for Advanced Study · USGeorge Mason University · USThe Ohio State University · US

Funding

CRCNS: Spatio-temporal Dynamics of Dopamine Activated 2nd Messenger PathwaysR01AA016022 · NIAAA · GEORGE MASON UNIVERSITY · PI BLACKWELL, KIM L, LOVINGER, DAVID · 2005 to 2017
$2.6M
CRCNS: Spatial & Temporal Aspects of cAMP/PKA Signaling Underlying InformationR01AA018060 · NIAAA · GEORGE MASON UNIVERSITY · PI ABEL, EDWIN TED G., BLACKWELL, KIM L · 2008 to 2012
$1.8M
NIAAA NIH HHS R01 AA016022NIAAA NIH HHS R01 AA018060NIAAA NIH HHS R01 AA16022NIAAA NIH HHS R01 AA18060
6 · The paper itself

Abstract

Dopamine binding to various dopamine receptors activates multiple intracellular signaling molecules, some of which interact with calcium activated signaling pathways. Many experiments measure agonist-stimulated elevations in signaling molecules using prolonged, diffuse application, whereas the response of neurons to transient and spatially localized stimuli is more important. Computational modeling is an approach for investigating the spatial extent, time course, and interaction of postsynaptic signaling molecules activated by dopamine and other transmembrane receptors. NeuroRD is a simulation algorithm which can simulate large numbers of pathways and molecules in multiple spines attached to a dendrite. We explain how to gather the information needed to develop computational models, to implement such models in NeuroRD, to perform simulations, and to analyze the simulated data from these models.

Indexed as

Models, BiologicalSignal TransductionDopamineIntracellular SpaceNeuronsSoftwareSynapsesDopamine

Identifiers

PMID23296778
PMCPMC4076845
OpenAlexW2418819043

What OpenQuestion holds

Textmetadata
LicenceTDM
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.