Evidence map›Paper›PMID 41343683›Full record

ArticlePLoS computational biology2025

Verification and reproducible curation of the BioModels repository.

Lucian P Smith, Rahuman S Malik-Sheriff, Tung V N Nguyen, Henning Hermjakob, Jonathan Karr, Bilal Shaikh, Logan Drescher, Ion I Moraru, James C Schaff, Eran Agmon and 7 more

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Lucian P SmithDepartment of Bioengineering, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0001-7002-6386
Rahuman S Malik-SheriffEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom.
Tung V N NguyenEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom.
Henning HermjakobEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom.ORCID 0000-0001-8479-0262
Jonathan KarrIcahn School of Medicine at Mount Sinai, New York, New York, United States of America.ORCID 0000-0002-2605-5080
Bilal ShaikhIcahn School of Medicine at Mount Sinai, New York, New York, United States of America.ORCID 0000-0001-5801-5510
Logan DrescherUniversity of Connecticut School of Medicine, Farmington, Connecticut, United States of America.
Ion I MoraruUniversity of Connecticut School of Medicine, Farmington, Connecticut, United States of America.
James C SchaffUniversity of Connecticut School of Medicine, Farmington, Connecticut, United States of America.
Eran AgmonUniversity of Connecticut School of Medicine, Farmington, Connecticut, United States of America.
Alexander A PatrieUniversity of Connecticut School of Medicine, Farmington, Connecticut, United States of America.
Michael L BlinovUniversity of Connecticut School of Medicine, Farmington, Connecticut, United States of America.
Joseph L HellersteineScience Institute, University of Washington, Seattle, Washington, United States of America.
Elebeoba E MayDepartment of Medical Microbiology and Wisconsin Institute of Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
David P NickersonAuckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.ORCID 0000-0003-4667-9779
John H GennariDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America.
Herbert M SauroDepartment of Bioengineering, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-3659-6817

Funding

TR&D3: Standards and Tools for Simulator Composition and Credibility portalP41EB023912 · NIBIB · UNIVERSITY OF WASHINGTON · PI HERBERT M. SAURO · 2018 to 2026
$11.4M
Mechanistic Modeling of Cellular SystemsR24GM137787 · NIGMS · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI Pedro Mendes, Ion I. Moraru · 2020 to 2026
$8.9M
NIBIB NIH HHS P41 EB023912NIGMS NIH HHS R24 GM137787
6 · The paper itself

Abstract

The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation and collect data. Therefore, it can be challenging to reproduce any figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run an experiment corresponding to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them impossible to run. To make these models and their results more reproducible, we report here on our work updating, correcting and generating new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. We tested, corrected, and improved over 450 existing SED-ML files in the BioModels database, and created basic files for the rest of the entries. Then, we used a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines, after further improving the models, the wrappers, and the engines themselves. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible. Improved reproducibility means these models are now even more fit for re-use, such as in new investigations and as components of multiscale models.

Indexed as

Databases, FactualData CurationModels, BiologicalProgramming LanguagesSystems BiologyComputational BiologyComputer SimulationHumansReproducibility of Results

Identifiers

PMID41343683
PMCPMC12677764

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.