Evidence map›Paper›PMID 25589574›Full record

ArticleJournal of the Royal Society, Interface2015

Using argument notation to engineer biological simulations with increased confidence.

Kieran Alden, Paul S Andrews, Fiona A C Polack, Henrique Veiga-Fernandes, Mark C Coles, Jon Timmis

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Agent-Based Modeling in Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2015
    Article
  8. Review
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

6 authors.

Kieran AldenYork Computational Immunology Laboratory, University of York, York, UK Centre for Immunology and Infection, University of York, York, UK Department of Electronics, University of York, York, UK kieran.alden@york.ac.uk.
Paul S AndrewsYork Computational Immunology Laboratory, University of York, York, UK Department of Computer Science, University of York, York, UK York Centre for Complex Systems Analysis, University of York, York, UK.
Fiona A C PolackYork Computational Immunology Laboratory, University of York, York, UK Department of Computer Science, University of York, York, UK York Centre for Complex Systems Analysis, University of York, York, UK.
Henrique Veiga-FernandesFaculdade de Medicina de Lisboa, Instituto de Medicina Molecular, Lisboa, Portugal.
Mark C ColesYork Computational Immunology Laboratory, University of York, York, UK Centre for Immunology and Infection, University of York, York, UK SimOmics Ltd, The Catalyst, Baird Lane, Heslington, York, UK mark.coles@york.ac.uk.
Jon TimmisYork Computational Immunology Laboratory, University of York, York, UK Department of Electronics, University of York, York, UK SimOmics Ltd, The Catalyst, Baird Lane, Heslington, York, UK jon.timmis@york.ac.uk.

Funding

Medical Research Council G0601156Wellcome Trust 097829
6 · The paper itself

Abstract

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer's Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions.

Indexed as

AlgorithmsAnimalsCell MovementComputer SimulationHumansInternetLymphoid TissueModels, BiologicalPeyer's PatchesSoftwareargumentationArtoocomputational modellingimmune system modellingsimulation

Identifiers

PMID25589574
PMCPMC4345473

What OpenQuestion holds

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LicenceCC BY
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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.