Engineered internal noise stochastic resonator in gene network: a model study.

Area of Science:

• Systems biology
• Synthetic biology
• Biophysics

Background:

• Synthetic gene networks often involve coupled genetic circuits.
• Understanding the impact of internal noise on network dynamics is crucial for predictable function.
• Gene expression systems can exhibit complex behaviors like oscillations and bistability.

Purpose of the Study:

• To develop a mesoscopic stochastic model for coupled synthetic gene networks.
• To investigate the influence of internal noise on the oscillatory behavior of these networks.
• To explore the phenomenon of stochastic resonance (SR) in synthetic gene expression systems.

Main Methods:

• Coupling a genetic bistable switch model with a gene oscillator model.
• Constructing a mesoscopic stochastic model for the synthetic gene network.
• Analyzing the effect of varying internal noise levels on system dynamics and oscillation performance.

Main Results:

• Internal noise can induce state-to-state transitions in the coupled gene network.
• Oscillation performance reaches a maximum at a specific internal noise level.
• This optimal noise level indicates the occurrence of internal noise stochastic resonance (SR).
• The coupled gene network functions as a stochastic resonator.

Conclusions:

• Internal noise plays a significant role in the dynamics and performance of coupled synthetic gene networks.
• Stochastic resonance (SR) can be harnessed to optimize oscillatory behavior in gene expression systems.
• The findings suggest potential applications for noise-enhanced gene expression control.