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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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A primer on model selection using the Akaike Information Criterion.

Stéphanie Portet1

  • 1Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, R3T 2N2, Canada.

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|January 21, 2020
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Summary
This summary is machine-generated.

This study introduces a workflow for using multiple mathematical models to investigate biological problems. It details model calibration and selection methods, including the Akaike Information Criterion, for identifying the best-fitting model based on experimental data.

Keywords:
Akaike information criterionCollection of modelsModel calibrationModel selection

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Area of Science:

  • Computational Biology
  • Mathematical Modeling in Biology
  • Systems Biology

Background:

  • Investigating complex biological systems often requires mathematical models.
  • A single model may not capture all biological complexities or hypotheses.
  • A collection of models allows for exploring diverse working hypotheses.

Purpose of the Study:

  • To describe a workflow for using a collection of mathematical models in biological research.
  • To motivate the use of multiple models for hypothesis testing and selection.
  • To detail essential steps of model calibration and selection.

Main Methods:

  • Description of the standard workflow for mathematical modeling in biology.
  • Explanation of parameter estimation using observational data.
  • Introduction to model calibration procedures and the Akaike Information Criterion (AIC) for model selection.

Main Results:

  • The study details the practical computation and application of the Akaike Information Criterion.
  • It provides a framework for selecting the most appropriate model from a collection based on experimental data.
  • The intrinsic link between model calibration and model selection is highlighted.

Conclusions:

  • Using a collection of models is a powerful approach for biological investigation.
  • Model calibration and selection are critical, linked steps in the modeling workflow.
  • The Akaike Information Criterion offers a data-driven method for choosing the best model.