Modeling Active Non-Markovian Oscillations.

Area of Science:

• Physics
• Biophysics
• Nonlinear Dynamics

Background:

• Modeling noisy oscillations in active systems presents a significant challenge due to complex underlying physical mechanisms.
• Understanding these oscillations is crucial for advancements in physics and biology.

Purpose of the Study:

• To propose a novel linear stochastic model capable of generating self-sustained periodic oscillations.
• To analytically derive key dynamical and thermodynamic properties of the proposed model.
• To validate the model's accuracy using experimental data from biological systems.

Main Methods:

• Development of a linear stochastic model driven by non-Markovian bistable noise.
• Analytical derivation of dynamical and thermodynamic properties.
• Comparison of model predictions with experimental data on bullfrog sacculus hair bundle oscillations.

Main Results:

• The model successfully generates self-sustained periodic oscillations.
• Analytical predictions for the model's properties were derived.
• The model accurately describes bistable-like oscillatory motion observed in bullfrog hair bundles.

Conclusions:

• A minimal linear stochastic model with non-Markovian bistable noise effectively captures noisy oscillations in active systems.
• The model provides an accurate description of biological oscillatory phenomena, specifically bullfrog hair bundle motion.
• The estimated power required for these active oscillations is approximately 100 kBT per cycle.