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Updated: May 31, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

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Published on: April 20, 2016

OPTIMIZATION USING SENSITIVITY FUNCTIONS FOR FITTING MODELS TO DATA.

J B Bassingthwaighte1, M Chaloupka, A A Goldstein

  • 1Center for Bioengineering WD-12, University of Washington, Seattle, WA 98195, U.S.A.

Mathematics and Computers in Simulation
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

Accurate modeling of myocardial cell transport requires sufficient experimental data for fitting complex exchange models. Sensitivity functions aid in parameter evaluation and model refinement for reliable ion and substrate transport analysis.

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Published on: August 19, 2021

Area of Science:

  • Biophysics
  • Physiology
  • Biochemistry

Background:

  • Estimating myocardial cell transport rates necessitates models of blood-interstitial fluid-cell exchanges.
  • Multiple indicator dilution curves are used for analyzing ion and substrate transport across the sarcolemma.

Purpose of the Study:

  • To outline the requirements for formulating and fitting exchange models to experimental data.
  • To highlight the importance of data quantity and quality in parameter estimation.

Main Methods:

  • Formulation of blood-ISF-cell exchange models.
  • Analysis of multiple indicator dilution curves.
  • Utilizing sensitivity functions for model fitting and parameter evaluation.

Main Results:

  • Sufficient data, with redundancy and overdetermination, is crucial for accurate model fitting.
  • Sensitivity functions effectively link data segments to unknown model parameters.
  • Sensitivity functions assist in adjusting parameters to achieve optimal model-data fit.

Conclusions:

  • Robust modeling of myocardial transport depends on adequate, well-determined experimental datasets.
  • Sensitivity analysis is a key tool for validating and refining transport models.
  • Effective parameter evaluation is essential for understanding sarcolemmal transport dynamics.