Advanced Mathematical Platform for the Control and Manipulation of Magnetized Living Cells.

Area of Science:

• Biomedical Engineering
• Cellular Manipulation
• Artificial Intelligence

Background:

• Superparamagnetic iron oxide nanoparticles (SPIONs) magnetize living cells for remote magnetic manipulation.
• This technique is promising for tissue engineering, enabling the assembly of tissue precursors for bone and organ reconstruction.
• Current progress is hindered by a lack of robust mathematical tools to control magnetic nano- and micro-objects, especially under heterogeneous conditions.

Purpose of the Study:

• To develop a hybrid modeling framework integrating physics-based simulations and AI-driven image analysis.
• To enable accurate control of ensembles of magnetic cells despite statistical heterogeneity.
• To provide a practical tool for the 3D magnetic assembly of living cells for tissue engineering applications.

Main Methods:

• Developed a hybrid modeling framework combining physics-based simulations with AI-driven image analysis.
• Extracted dynamic parameters from video recordings of magnetized cells in microfluidic devices under controlled magnetic fields.
• Utilized AI analysis for quantitative characterization of ensemble behavior under heterogeneous conditions.

Main Results:

• The hybrid framework successfully captured the collective dynamics of magnetized cell ensembles.
• Accurate control of spatial organization of cell ensembles was achieved using external magnetic actuation.
• Robust parameter identification was accomplished despite statistical heterogeneity within the cell system.

Conclusions:

• The integrated modeling approach offers a practical and effective method for controlling the 3D magnetic assembly of living cells.
• This framework addresses the limitations of traditional equation-based modeling for heterogeneous systems.
• The study shows strong potential for advancing tissue engineering and regenerative medicine through precise cellular control.