Evidence map›Paper›PMID 33023471›Full record

ArticleBMC bioinformatics2020

Cells in Silico - introducing a high-performance framework for large-scale tissue modeling.

Marco Berghoff, Jakob Rosenbauer, Felix Hoffmann, Alexander Schug

Abstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  3. FromFrontiers in molecular biosciences · 2024
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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

4 authors.

Marco BerghoffSteinbuch Centre for Computing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, 76344, Germany.
Jakob RosenbauerJohn von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, Jülich, 52428, Germany.
Felix HoffmannSteinbuch Centre for Computing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, 76344, Germany.
Alexander SchugJohn von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, Jülich, 52428, Germany. al.schug@fz-juelich.de.ORCID http://orcid.org/0000-0002-0534-502X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiscoveries in cellular dynamics and tissue development constantly reshape our understanding of fundamental biological processes such as embryogenesis, wound-healing, and tumorigenesis. High-quality microscopy data and ever-improving understanding of single-cell effects rapidly accelerate new discoveries. Still, many computational models either describe few cells highly detailed or larger cell ensembles and tissues more coarsely. Here, we connect these two scales in a joint theoretical model.

resultsWe developed a highly parallel version of the cellular Potts model that can be flexibly applied and provides an agent-based model driving cellular events. The model can be modular extended to a multi-model simulation on both scales. Based on the NAStJA framework, a scaling implementation running efficiently on high-performance computing systems was realized. We demonstrate independence of bias in our approach as well as excellent scaling behavior.

conclusionsOur model scales approximately linear beyond 10,000 cores and thus enables the simulation of large-scale three-dimensional tissues only confined by available computational resources. The strict modular design allows arbitrary models to be configured flexibly and enables applications in a wide range of research questions. Cells in Silico (CiS) can be easily molded to different model assumptions and help push computational scientists to expand their simulations to a new area in tissue simulations. As an example we highlight a 1000

Indexed as

Computer SimulationOrgan SpecificityBiological TransportCell DeathCellsDiffusionModels, TheoreticalMutationUser-Computer InterfaceCellular Potts modelMassively parallelTissue growth

Identifiers

PMID33023471
PMCPMC7542106

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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.