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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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A software package for immunologists to learn simulation modeling.

Andreas Handel1

  • 1Department of Epidemiology and Biostatistics and Health Informatics Institute and Center for the Ecology of Infectious Diseases, The University of Georgia, Athens, GA, USA. ahandel@uga.edu.

BMC Immunology
|January 4, 2020
PubMed
Summary
This summary is machine-generated.

A new R package, Dynamical Systems Approach to Immune Response Modeling (DSAIRM), teaches mechanistic simulation models for immunology without coding. This tool aids immunologists in understanding and applying computational models in research.

Keywords:
Mechanistic simulation modelsR packageTeaching software

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Area of Science:

  • Immunology
  • Computational Biology
  • Infectious Disease Dynamics

Background:

  • Quantitative immunology increasingly relies on sophisticated computational tools like simulation models.
  • A significant barrier to using these models is the requirement for prior computer coding experience.
  • This limits accessibility for many immunologists and researchers.

Purpose of the Study:

  • To introduce a software package that facilitates learning mechanistic simulation models for immunology.
  • To enable researchers without coding experience to study infection and immune response dynamics.
  • To lower the barrier to entry for computational modeling in immunology.

Main Methods:

  • Development of the Dynamical Systems Approach to Immune Response Modeling (DSAIRM) R package.
  • Implementation as a freely available R package.
  • Provision of hands-on introduction and examples for users.

Main Results:

  • DSAIRM teaches the use and basics of mechanistic simulation models without requiring users to write code.
  • The software is accessible to immunologists and scientists with limited or no prior coding experience.
  • The package provides introductory examples and explanations of model applications.

Conclusions:

  • DSAIRM equips users to critically evaluate simulation-based studies in immunology literature.
  • It helps researchers understand the applicability of modeling approaches to their own research questions.
  • The package serves as a starting point for developing and utilizing simulation models in immunological research.