Viscoelastic characterization of the human osteosarcoma cancer cell line MG-63 using a fractional-order zener model through automated algorithm design and configuration

Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acq...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 31436 - 25
Hauptverfasser: Duque-Gimenez, Grecia C., Zambrano-Gutierrez, Daniel F., Rodriguez-Nieto, Maricela, Menchaca, Jorge Luis, Cruz-Duarte, Jorge M., Zárate-Triviño, Diana G., Avina-Cervantes, Juan Gabriel, Ortiz-Bayliss, José Carlos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 26.08.2025
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Algorithms
Atomic force microscopy
Automation
Biomechanical Phenomena
Biomechanics
Bone cancer
Calculus
Cell Line, Tumor
Cells
Coefficient of variation
Design
Elasticity
Fractional-order zener model
Heuristic
Humanities and Social Sciences
Humans
Hyper-heuristic
Laplace transforms
Mechanical properties
Metaheuristic
Microscopy, Atomic Force
multidisciplinary
Optimization
Osteosarcoma
Osteosarcoma - pathology
Parameter estimation
Physiology
Science
Science (multidisciplinary)
Statistical analysis
Viscoelasticity
Viscosity
Viscoelasticity
Atomic force microscopy
Metaheuristic
Fractional-order zener model
Automated algorithm design
Cells
Hyper-heuristic
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Understanding the viscoelastic properties of cells is essential for studying their mechanical behavior and identifying disease-related biomechanical markers. This paper proposes an integrated framework that combines fractional modeling with automated algorithm design to fit force-relaxation data acquired through atomic force microscopy. We employ the fractional-order zener model to describe cell relaxation curves and formulate the parameter estimation as a black-box optimization problem. To solve it, we implement a Randomized Constructive Hyper-Heuristic with Local Search (RCHH-LS) that automatically generates tailored metaheuristics (MHs) by exploring combinations of search operators. Our results show that the best-performing MH, composed of two swarm-based dynamics and a local random-walk operator ( ), achieves a performance of , representing a 75% improvement over the mean of all candidate configurations. Subsequent hyperparameter tuning with Optuna reduces this value to , a further 4.7% gain relative to the untuned version while preserving high stability and repeatability. In an evaluation of 21 instances (force-relaxation curves), the tuned provided the best result in 19 cases, achieving an average of , about 12% better than the best two-operator alternative and a coefficient of variation below 0.01%, underscoring its generalization capability. The FOZ model fitted using this solver generalizes well to independent datasets and captures critical viscoelastic parameters. We also confirm that , , and are sensitive to the applied force via a statistical analysis, while remains stable, reinforcing its association with intrinsic cell properties. These results highlight the effectiveness of combining fractional viscoelastic modeling with automated MH design for robust and interpretable mechanical characterization of cells. The proposed approach reduces manual intervention, ensures generalizability, and offers a scalable solution for computational biomechanics.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-16174-3