Data-driven reduced-order model of microtubule mechanics

Area of Science:

• Biophysics
• Computational Biology
• Materials Science

Background:

• Microtubules are crucial cytoskeletal components with complex mechanical properties.
• Standard beam elements often fail to capture the anisotropic and coupled deformations of microtubules.
• Atomistic simulations provide detailed mechanical data but are computationally expensive for large-scale modeling.

Purpose of the Study:

• To develop a novel beam element for accurately modeling microtubule mechanics.
• To incorporate anisotropic behavior and deformation coupling directly from molecular simulations.
• To create an efficient computational model for cellular structures involving microtubules.

Main Methods:

• Data reduction of atomistic simulations of microtubule carbon backbone.
• Identification of dominant mechanical response modes.
• Construction of a stiffness matrix for a microtubule data-driven beam model (MTDDBM).

Main Results:

• The MTDDBM captures anisotropic behavior and coupled deformations (bending, stretch, shear) inherent to microtubules.
• The model's force-displacement relationship is derived directly from molecular simulation data.
• The MTDDBM demonstrates comparable economy to standard beam elements.

Conclusions:

• The MTDDBM offers an accurate and efficient method for simulating microtubule mechanical behavior.
• This data-driven approach overcomes limitations of traditional beam elements for cytoskeletal modeling.
• The model accurately reconstructs the mechanics of cellular structures, such as mitotic spindle components.