Evidence map›Paper›PMID 40184419›Full record

ArticlePLoS computational biology2025

Logic-based modeling of biological networks with Netflux.

Alexander P Clark, Mukti Chowkwale, Alexander Paap, Stephen Dang, Jeffrey J Saucerman

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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  13. Striking the balance: Complexity, simplicity, and credibility in mathematical biology.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  15. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Alexander P ClarkDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.ORCID 0000-0002-5200-6573
Mukti ChowkwaleDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Alexander PaapDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Stephen DangDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
Jeffrey J SaucermanDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.ORCID 0000-0001-9464-8374

Funding

BASIC CARDIOVASCULAR RESEARCH TRAINING GRANTT32HL007284 · NHLBI · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI Brant E Isakson, Gary K Owens · 1985 to 2026
$19.6M
Systems Pharmacology Model of Cardiac HypertrophyR01HL162925 · NHLBI · UNIVERSITY OF VIRGINIA · PI SAUCERMAN, JEFFREY J., WOLF, MATTHEW J · 2022 to 2025
$3.1M
Computational and Experimental Modeling of Cardiomyocyte ProliferationR01HL160665 · NHLBI · UNIVERSITY OF VIRGINIA · PI SAUCERMAN, JEFFREY J., VAN BERLO, JOHANNES (JOP) · 2022 to 2025
$2.8M
Systems Pharmacology Model for Spatial Control of Cardiac FibrosisR01HL137755 · NHLBI · UNIVERSITY OF VIRGINIA · PI HOLMES, JEFFREY W, SAUCERMAN, JEFFREY J. · 2017 to 2020
$1.9M
NHLBI NIH HHS R01 HL137755NHLBI NIH HHS R01 HL160665NHLBI NIH HHS R01 HL162925NHLBI NIH HHS T32 HL007284
6 · The paper itself

Abstract

Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools require extensive programming knowledge. In this article, we describe a user-friendly, programming-free network simulation tool called Netflux. Over the last decade, Netflux has been used to construct numerous predictive network models that have deepened our understanding of how complex biological networks make cell decisions. Here, we provide a Netflux tutorial that covers how to construct a network model and then simulate network responses to perturbations. Upon completion of this tutorial, you will be able to construct your own model in Netflux and simulate how perturbations to proteins and genes propagate through signaling and gene-regulatory networks.

Indexed as

Computational BiologyModels, BiologicalSignal TransductionSoftwareComputer SimulationGene Regulatory NetworksHumans

Identifiers

PMID40184419
PMCPMC11970637

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.