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Biophysical Characterization of Flagellar Motor Functions
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Parameter identification problems in the modelling of cell motility.

Wayne Croft1, Charles M Elliott, Graham Ladds

  • 1School of Life Sciences, Queens Medical Centre, University of Nottingham, Nottingham, NG7 2UH, UK.

Journal of Mathematical Biology
|September 2, 2014
PubMed
Summary
This summary is machine-generated.

A new algorithm estimates cell motility model parameters from imaging data. This robust method handles complex deformations and noise, applicable to 3D models and experimental yeast cell data.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Mathematical Modeling

Background:

  • Accurate parameter estimation is crucial for understanding cell motility.
  • Existing methods may struggle with complex cellular behaviors and noisy imaging data.

Purpose of the Study:

  • To develop and validate a novel algorithm for parameter identification in cell motility models.
  • To assess the algorithm's robustness and applicability to various scenarios, including 3D models and experimental data.

Main Methods:

  • Formulation of the parameter identification problem as a nonlinear least squares minimization.
  • Application of a Levenberg-Marquardt optimization algorithm.
  • Development of two alternative objective functional formulations for data comparison.

Main Results:

  • The algorithm demonstrates robustness in parameter identification despite large deformations and noisy imaging data.
  • Successful application to 3D cell motility models and experimental data of fission yeast cell growth.
  • Numerical tests show agreement between the two objective functional formulations.

Conclusions:

  • The presented algorithm provides a reliable tool for parameter estimation in cell motility models.
  • The method is versatile, applicable to both simulated and real-world biological data.
  • Both objective functional formulations yield comparable and accurate results.