Evidence map›Paper›PMID 29926381›Full record

ArticleBulletin of mathematical biology2019

Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach.

Stefan Engblom

Abstract read
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In one paragraph

Article in Bulletin of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Cancer informatics · 2022
    Article
  4. 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

1 author.

Stefan EngblomDivision of Scientific Computing, Department of Information Technology, Uppsala University, 751 05, Uppsala, Sweden. stefane@it.uu.se.ORCID 0000-0002-3614-1732

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie's celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single-cell computational modeling, here taken to be spatial stochastic reaction-diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch-Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites and via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods.

Indexed as

Body PatterningModels, BiologicalAlgorithmsCell CommunicationCell Physiological PhenomenaComputer SimulationIntracellular Signaling Peptides and ProteinsMathematical ConceptsMembrane ProteinsMorphogenesisReceptors, NotchSignal TransductionStochastic ProcessesSystems Biologydelta proteinIntracellular Signaling Peptides and ProteinsMembrane ProteinsReceptors, NotchCell population modelDiscrete Laplacian cell mechanicsNotch signaling pathwayReaction–diffusion master equationSingle-cell model

Identifiers

PMID29926381
PMCPMC6677715

What OpenQuestion holds

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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.