Evidence map›Paper›PMID 10798598›Full record

ArticleJournal of computational neuroscience

A simple mathematical model of second-messenger mediated slow excitatory postsynaptic potentials.

P P Bertrand, E A Thomas, W A Kunze, J C Bornstein

Open access · greenAbstract read
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In one paragraph

Article in Journal of computational neuroscience. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 43% 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

5 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. 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

4 authors at 1 institution in 1 country.

P P BertrandDepartment of Physiology, University of Melbourne, Parkville, Australia. p.bertrand@physiology.unimelb.edu.au
E A Thomas
W A Kunze
J C Bornstein
University of Melbourne · AU

Funding

SENSORY NEURONS IN THE ENTERIC NERVOUS SYSTEMF32DK009162 · NIDDK · UNIVERSITY OF MELBOURNE · PI BERTRAND, PAUL P · 1994 to 1997
–
NIDDK NIH HHS DK 09162
6 · The paper itself

Abstract

We have developed a novel and simple mathematical model of a slow excitatory postsynaptic potential (EPSP) based on an abstraction of the processes of activation, inactivation, and summation of a cAMP, protein kinase A (PKA)-dependent second-messenger cascade. The model describes the activation of receptors, G-proteins, and production of cAMP as the first stage and uses first-order, non-rate-limited kinetics. The second stage corresponds to the release of active, PKA catalytic subunit and can use first- or higher-order kinetics. The third stage represents simple phosphorylation of ion channels and is limited by the number of channels available. The decay of each stage is based on first-order, mass-action kinetics. These equations and some variations were solved numerically and values of the parameters were determined by fitting to a variety of experimental data from myenteric neurons of the guinea-pig ileum. The model produced a slow EPSP with a nonlinear stimulus-response relationship that resulted from the underlying kinetics of the signaling cascade. This system of equations is suitable for incorporation into a large-scale computer simulation, and the methodology should be generalizable to other pathways.

Indexed as

AnimalsCell MembraneCyclic AMPElectric StimulationElectrophysiologyEnteric Nervous SystemExcitatory Postsynaptic PotentialsGTP-Binding ProteinsGuinea PigsModels, NeurologicalNerve NetNeuronsReaction TimeSecond Messenger SystemsTime FactorsCyclic AMPGTP-Binding Proteins

Identifiers

PMID10798598
OpenAlexW1578984272

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.