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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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Multi-method global sensitivity analysis of mathematical models.

An Dela1, Blerta Shtylla2, Lisette de Pillis3

  • 1Institute of Mathematical Sciences, Claremont Graduate University, Claremont, CA 91711, USA.

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|May 16, 2022
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Summary
This summary is machine-generated.

This study introduces a multi-method framework for parameter-sensitivity analysis, comparing Sobol's method, DGSM, and a new MeFAST implementation. The framework aids in selecting appropriate sensitivity analysis techniques for various models, enhancing decision-making.

Keywords:
Derivative-based global sensitivity measuresGlobal parameter sensitivity analysisHIV modelModelingSobol’s methodTumor growth modeleFAST

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Area of Science:

  • Computational modeling
  • Mathematical biology
  • Sensitivity analysis

Background:

  • Parameter-sensitivity analysis is crucial for model understanding but selecting methods is challenging.
  • Existing methods have limitations, necessitating a comparative approach.

Purpose of the Study:

  • To present a novel multi-method framework for global sensitivity analysis.
  • To compare DGSM, Sobol's method, and a new MeFAST implementation.
  • To provide a computational tool for informed method selection.

Main Methods:

  • Incorporation of three global sensitivity analysis methods: DGSM, Sobol's method, and MeFAST (an enhanced eFAST implementation).
  • Development of MATLAB codes and a hyper-parameter tuning guide.
  • Demonstration using mathematical models of HIV disease progression and tumor growth.

Main Results:

  • A computational framework generating comparative visualizations of DGSM, Sobol, and MeFAST outputs.
  • Demonstration of the workflow on two distinct mathematical models.
  • Facilitation of informed decisions regarding parameter influence on model outcomes.

Conclusions:

  • The presented framework enables a comprehensive comparison of different sensitivity analysis techniques.
  • This facilitates a more informed selection of methods based on model characteristics and analysis goals.
  • The tool aids researchers in identifying key model parameters more effectively.