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Controlling Cell Migratory Patterns Under an Electric Field Regulated by a Neural Network-Based Feedback Controller.

Giovanny Marquez1, Mohammad Jafari2, Manasa Kesapragada1

  • 1Department of Applied Mathematics, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA.

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This study introduces a novel neural network controller to precisely regulate cell migration using electric fields (EFs). The enhanced controller ensures accurate trajectory tracking and outperforms standard methods in directing cell movement for tissue repair.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Control Systems Engineering

Background:

  • Electric fields (EFs) are utilized for tissue regeneration and wound healing.
  • Cellular responses to EFs, particularly cell migration, are complex and not fully understood.
  • Precise control of cell migration is crucial for development, immune response, and repair.

Purpose of the Study:

  • To develop a closed-loop control system for precise regulation of population-level cell migration.
  • To adapt a neural network (NN) feedback controller for guiding cell migration under safety constraints.
  • To address challenges posed by nonlinear dynamics and EF magnitude limitations in cell migration control.

Main Methods:

  • Reformulation of a previously developed NN feedback controller for single-cell membrane-potential regulation.
  • Adaptation of the NN controller for population-level cell migration guidance.
  • Embedding a projection operator into the NN weight-update law to prevent saturation-induced maladaptive learning.
  • Numerical simulations to validate controller performance under saturation.
  • In vitro proof-of-concept implementation using a unidirectional EF to direct macrophage electrotaxis in 2D culture.
  • Comparison of the novel controller with a standard proportional-integral-derivative (PID) controller.

Main Results:

  • The modified NN controller demonstrated accurate trajectory tracking even when the control signal saturated at the EF limit.
  • The adapted NN controller outperformed the original NN design in simulations.
  • In vitro experiments showed successful direction of naïve macrophage migration using the developed controller.
  • Performance comparison indicated advantages over the standard PID controller in specific scenarios.

Conclusions:

  • The developed NN-based feedback controller offers precise, closed-loop regulation of population-level cell migration.
  • The embedded projection operator effectively mitigates issues arising from EF saturation.
  • This approach provides a robust method for controlling electrotactic cell migration, with potential applications in regenerative medicine and tissue engineering.