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Using argument notation to engineer biological simulations with increased confidence.

Kieran Alden1, Paul S Andrews2, Fiona A C Polack2

  • 1York 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.

Journal of the Royal Society, Interface
|January 16, 2015
PubMed
Summary
This summary is machine-generated.

Structured argumentation enhances biological modeling confidence. This approach, adapted from safety-critical engineering, ensures models accurately represent biological systems, aiding therapeutic development and research publication.

Keywords:
Artooargumentationcomputational modellingimmune system modellingsimulation

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Area of Science:

  • Computational biology
  • Systems biology
  • Biomedical modeling

Background:

  • Computational and mathematical modeling are increasingly used in biological research.
  • Ensuring model accuracy is crucial for impactful biological research and therapeutic development.
  • In silico approaches require robust validation to build confidence in their application.

Purpose of the Study:

  • To propose and demonstrate a structured argumentation approach for biological model development and validation.
  • To improve confidence in computational models by ensuring they adequately represent biological systems.
  • To enhance transparency in model design, analysis, and publication.

Main Methods:

  • Adapted argumentation from safety-critical systems engineering for biological models.
  • Utilized a Web-based tool (Artoo) to capture argumentation structure using Goal Structuring Notation.
  • Applied the approach to a model of lymphoid tissue formation, specifically Peyer's Patches.

Main Results:

  • The structured argumentation approach facilitates examination of biological information underpinning models.
  • Identified model strengths and areas needing further biological experimentation.
  • Provided documentation supporting model publication and transparently captured design reasoning, assumptions, and evidence.

Conclusions:

  • Structured argumentation enhances the rigor and transparency of biological modeling.
  • This method supports the development of reliable in silico models for drug discovery and biological research.
  • The approach aids in validating model fitness-for-purpose and guides future experimental work.