Electro-chemo-mechanical model to investigate multi-pulse electric-field-driven integrin clustering.

Area of Science:

• Biophysics
• Cellular Biophysics
• Computational Biology

Background:

• The precise mechanisms by which transmembrane proteins sense and respond to electric fields, particularly pulsed electric fields (PEFs), remain incompletely understood.
• Chemo-mechanical cues within the cellular microenvironment are hypothesized to play a crucial role in this electric field sensing process.
• Integrin proteins, critical for cell adhesion and signaling, are key targets for investigating these electro-mechanical interactions.

Purpose of the Study:

• To develop and utilize a computational model that elucidates how chemo-mechanical cues mediate electric field sensing by integrin proteins.
• To investigate the effects of both steady-state electric fields and transient PEF pulse trains on integrin clustering.
• To explore the influence of PEF pulse-train parameters on integrin clustering dynamics.

Main Methods:

• Development of a kinetic Monte Carlo statistical model for integrin proteins.
• Integration of chemical, mechanical, and electrical cues within the model, including cell membrane stiffness, ligand density, glycocalyx stiffness, Brownian motion, and electric field-induced diffusion.
• Simulation of integrin clustering under steady-state and pulsed electric field conditions.

Main Results:

• Electric-field-driven integrin clustering is significantly mediated by cell membrane stiffness and extracellular ligand density.
• The parameters of PEF pulse trains, including amplitude, polarity, and pulse width, demonstrably affect integrin clustering.
• The computational model successfully simulates integrin clustering for time scales relevant to experimental observations (seconds to minutes).

Conclusions:

• A computational methodology is presented for simulating integrin clustering in response to PEFs, integrating experimental data.
• The study provides a framework for understanding the complex interplay of electric fields and chemo-mechanical cues in protein-mediated cellular signaling.
• Key challenges in integrating experimental data into computational models like kinetic Monte Carlo for biophysical simulations were highlighted.