Parameter identification problems in the modelling of cell motility.

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.