A Novel Method to Calibrate Spring-Network Cell Model in Hydrodynamic Flow.

Area of Science:

• Biomedical Engineering
• Cell Mechanics
• Bioprinting Technology

Background:

• Extrusion bioprinting faces challenges with mechanical stresses impacting cell viability.
• Current statistical models for cell damage are empirical and lack single-cell predictive power.
• Accurate simulation of cell deformability is crucial for developing validated computational models.

Purpose of the Study:

• To develop an efficient computational model for simulating human dermal fibroblast deformability in extrusion bioprinting.
• To address the challenge of accurately calibrating spring-network model coefficients for eukaryotic cells.
• To establish a manual calibration method using experimental data for improved model accuracy.

Main Methods:

• Utilized experimental data of human dermal fibroblasts in microfluidic constrictions.
• Employed a spring-network model for simulating cellular deformation.
• Initiated calibration with red blood cell coefficients and refined using experimental fibroblast data.
• Adjusted elastic coefficients to match experimental entry times within a 5% margin.

Main Results:

• Successfully calibrated elastic coefficients to closely match experimental entry times.
• Observed persistent differences in cell deformation behavior between simulation and experimental data.
• Incorporating membrane viscosity showed minimal reduction (<10%) in transient cell deformation.

Conclusions:

• The developed manual calibration method offers a pathway for improving computational models of cell deformability in bioprinting.
• Further refinement is needed to fully capture the complex deformation behavior of human dermal fibroblasts.
• This work contributes to the development of more accurate predictive models for cell health during bioprinting processes.