Optimization-based synthesis with directed cell migration.

Area of Science:

• Systems Biology
• Biophysics
• Robotics

Background:

• Collective behavior is fundamental in biological systems, from cells to organisms.
• Collective cell migration (CCM) demonstrates how individual cell movement is influenced by neighbors.

Purpose of the Study:

• To uncover mathematical rules governing CCM using experimental data and dynamic mode decomposition (DMD).
• To validate predictive models of CCM under various boundary conditions.
• To design optimal trajectories for microrobots navigating obstacles for therapeutic applications.

Main Methods:

• Dynamic Mode Decomposition (DMD) applied to experimental data for CCM.
• Testing CCM models with Dirichlet, Neumann, and mixed boundary conditions.
• Synthesizing discovered CCM dynamics with collocation for microrobot trajectory optimization.

Main Results:

• Mathematical rules for CCM were identified from experimental data.
• Predictive models for CCM were validated across different boundary conditions.
• Optimal microrobot trajectories were computationally generated for obstacle courses.

Conclusions:

• DMD successfully elucidated the mathematical principles of CCM.
• The synthesized dynamics offer a framework for advanced microrobot path planning.
• In silico results show potential for therapeutic microrobot applications like targeted drug delivery.