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Inferring scale-dependent non-equilibrium activity using carbon nanotubes.

Alexandru Bacanu1,2,3, James F Pelletier1,4,5, Yoon Jung1

  • 1Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nature Nanotechnology
|May 8, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to quantify non-equilibrium activity in biological systems. This technique analyzes molecular dynamics to understand how microscopic actions create large-scale functions in living structures.

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Area of Science:

  • Biophysics
  • Cell Biology
  • Soft Matter Physics

Background:

  • Living systems exhibit complex multiscale structures and functions driven by irreversible, stochastic molecular interactions.
  • Quantifying the dynamics of non-equilibrium activity in these systems is challenging due to a lack of suitable methods.

Purpose of the Study:

  • To develop and apply a novel method for characterizing the multiscale dynamics of non-equilibrium activity in biological networks.
  • To dissect the functional coupling between microscopic dynamics and emergent large-scale activities.

Main Methods:

  • Utilized single-walled carbon nanotubes as probes within Xenopus egg extract actomyosin networks.
  • Measured time-reversal asymmetry in the conformational dynamics of filaments to quantify non-equilibrium activity.
  • Analyzed bending-mode amplitudes to characterize spatiotemporal dynamics.

Main Results:

  • The developed method successfully quantified non-equilibrium activity in the actomyosin network.
  • The technique demonstrated sensitivity to perturbations in the actomyosin network and adenosine triphosphate/adenosine diphosphate ratios.
  • Established a relationship between spatiotemporal scales of non-equilibrium activity and physical parameters of embedded filaments.

Conclusions:

  • The study presents a generalizable tool for characterizing steady-state non-equilibrium activity in complex, high-dimensional biological systems.
  • This method provides insights into the structure-function relationship in living systems by quantifying underlying dynamics.
  • Advances the understanding of biophysical processes governing cellular functions like motility and division.