Evidence map›Paper›PMID 37074576›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

A Practical Guide to Reproducible Modeling for Biochemical Networks.

Veronica L Porubsky, Herbert M Sauro

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. 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

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

2 authors.

Veronica L PorubskyUniversity of Washington, Department of Bioengineering, Seattle, WA, USA. verosky@uw.edu.
Herbert M SauroUniversity of Washington, Department of Bioengineering, Seattle, WA, USA.

Funding

TR&D Project 4. The Imaging Stage: Multiscale Spatiotemporal Modeling of Macromolecular Systems in Cellular NeighborhoodsP41GM109824 · NIGMS · ROCKEFELLER UNIVERSITY · PI ROUT, MICHAEL P · 2014 to 2023
$18.8M
NIGMS NIH HHS P41 GM109824
6 · The paper itself

Abstract

While scientific disciplines revere reproducibility, many studies - experimental and computational alike - fall short of this ideal and cannot be reproduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to support reproducibility. This chapter points the reader to useful software tools and standardized formats that support reproducible modeling of biochemical networks and provides suggestions on how to implement reproducible methods in practice. Many of the suggestions encourage readers to use best practices from the software development community in order to automate, test, and version control their model components. A Jupyter Notebook demonstrating several of the key steps in building a reproducible biochemical network model is included to supplement the recommendations in the text.

Indexed as

SoftwareComputer SimulationReproducibility of ResultsBiochemical networksCOMBINE archiveComputational modelingFAIRModeling softwareReproducibilitySBMLSED-ML

Identifiers

What OpenQuestion holds

Textmetadata
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.