Quantifying dissipation in actomyosin networks.

Area of Science:

• Physics
• Biophysics
• Computational Biology

Background:

• Active matter systems, like actomyosin networks, are far-from-equilibrium systems where self-organization principles are not fully understood.
• A hypothesis suggests that these systems optimize free energy dissipation, potentially leading to ordered states.
• It remains unclear if biological active matter, such as actomyosin networks, adheres to this dissipation optimization principle.

Purpose of the Study:

• To investigate whether actomyosin networks self-organize based on the principle of dissipation optimization.
• To develop and apply computational methods for quantifying entropy production in active matter systems.
• To explore the relationship between network structure, energy transduction, and dissipation rates.

Main Methods:

• Utilized the MEDYAN simulation platform for detailed molecular-level modeling of active matter networks.
• Extended MEDYAN to quantify dissipation rates from chemical reactions and mechanical stress relaxation.
• Developed a novel formula for Gibbs free energy changes in chemical reactions and calculated instantaneous mechanical energy.

Main Results:

• Validated the computational approach using a mean-field model for filament treadmilling dissipation.
• Found that compact and highly cross-linked actomyosin networks exhibit more efficient transduction of chemical to mechanical energy.
• Observed that spontaneous network reorganizations in simple actomyosin systems lead to a reduced, steady-state dissipation rate.

Conclusions:

• The developed methodology provides a framework for quantifying dissipation in active matter simulations.
• Initial findings in simple actomyosin networks suggest a tendency towards reduced dissipation rates during self-organization.
• Further research is needed to confirm if dissipation-driven adaptation applies to more complex cytoskeletal structures.