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Published on: September 27, 2019

The input-output relationship approach to structural identifiability analysis.

Daniel J Bearup1, Neil D Evans, Michael J Chappell

  • 1Department of Mathematics, University of Leicester, Leicester LE1 7RH, UK. djb69@le.ac.uk

Computer Methods and Programs in Biomedicine
|December 12, 2012
PubMed
Summary
This summary is machine-generated.

This study presents a novel, computationally tractable method for analyzing model identifiability in complex systems. The approach, utilizing input-output relations, simplifies parameter determination from physical data, crucial for scientific modeling.

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Published on: September 27, 2019

Area of Science:

  • Systems Biology
  • Computational Modeling
  • Biomedical Engineering

Background:

  • Model identifiability is crucial for parameter estimation in systems analysis.
  • Current methods for nonlinear systems are often computationally intractable or limited in scope.
  • Input-output relations offer a potential alternative for model analysis.

Purpose of the Study:

  • To develop a computationally efficient method for assessing model identifiability.
  • To address limitations of existing tools for nonlinear system analysis.
  • To enable robust parameter determination from physical data.

Main Methods:

  • A novel test for linear independence of differential polynomial monomials is presented.
  • Symbolic computation using Maple facilitates derivation and analysis.
  • The method is applied to biomedical and enzyme kinetics models.

Main Results:

  • A simple and effective test for monomial linear independence was derived.
  • The symbolic implementation in Maple proved efficient for complex models.
  • Identifiability analysis was successfully performed on classical biomedical and enzyme models.

Conclusions:

  • The proposed input-output relation method provides a tractable approach to model identifiability analysis.
  • This technique enhances the ability to determine model parameters from physical data.
  • The method is broadly applicable to nonlinear models in various scientific domains.