Evidence map›Paper›PMID 33983922›Full record

ArticlePLoS computational biology2021

Ten steps to investigate a cellular system with mathematical modeling.

Jasia King, Kerbaï Saïd Eroumé, Roman Truckenmüller, Stefan Giselbrecht, Ann E Cowan, Leslie Loew, Aurélie Carlier

Abstract read
In one paragraph

Article in PLoS computational biology, 2021. 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
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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

7 authors.

Jasia KingMERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, the Netherlands.ORCID 0000-0002-0589-8601
Kerbaï Saïd ErouméMERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, the Netherlands.ORCID 0000-0002-9047-3471
Roman TruckenmüllerMERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, the Netherlands.
Stefan GiselbrechtMERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, the Netherlands.ORCID 0000-0002-1354-0167
Ann E CowanRichard D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America.
Leslie LoewRichard D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America.ORCID 0000-0002-1851-4646
Aurélie CarlierMERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, the Netherlands.ORCID 0000-0002-2305-5667

Funding

Mechanistic Modeling of Cellular SystemsR24GM137787 · NIGMS · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI Pedro Mendes, Ion I. Moraru · 2020 to 2026
$8.9M
NIGMS NIH HHS R24 GM137787
6 · The paper itself

Abstract

Cellular and intracellular processes are inherently complex due to the large number of components and interactions, which are often nonlinear and occur at different spatiotemporal scales. Because of this complexity, mathematical modeling is increasingly used to simulate such systems and perform experiments in silico, many orders of magnitude faster than real experiments and often at a higher spatiotemporal resolution. In this article, we will focus on the generic modeling process and illustrate it with an example model of membrane lipid turnover.

Indexed as

Cell BiologyModels, BiologicalComputational BiologyComputer SimulationMathematical ConceptsMembrane LipidsNonlinear DynamicsSoftwareSpatio-Temporal AnalysisMembrane Lipids

Identifiers

PMID33983922
PMCPMC8118325

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.