Toward single-cell control: noise-robust perfect adaptation in biomolecular systems

Area of Science:

• Systems Biology
• Synthetic Biology
• Biophysics

Background:

• Robust perfect adaptation (RPA) ensures stable biological output despite disturbances, crucial for cellular function.
• Antithetic integral feedback (AIF) achieves population-level RPA but amplifies noise, hindering single-cell regulation.
• Existing controllers struggle with noise amplification, limiting precise single-cell output control.

Purpose of the Study:

• To develop a novel regulatory motif for achieving RPA at the single-cell level.
• To overcome the noise amplification issue associated with traditional AIF controllers.
• To enhance the precision and stability of biological control systems.

Main Methods:

• Introduced a 'noise controller' inspired by AIF, utilizing output species dimerization for sensing.
• Combined the noise controller with AIF to achieve noise RPA.
• Validated the controller's efficacy in the DNA repair system of Escherichia coli.

Main Results:

• The combined noise controller and AIF maintained both mean and noise levels post-perturbation, achieving noise RPA.
• The noise controller successfully reduced output noise to a Fano factor of 1, the intrinsic noise lower bound.
• Demonstrated reduced failure rate in DNA damage response initiation in E. coli.

Conclusions:

• The novel noise controller enables robust perfect adaptation at the single-cell level by managing output noise.
• This approach enhances the precision of biological systems and is broadly applicable to ergodic networks.
• The findings represent a significant advancement toward achieving precise single-cell level regulation in biological engineering.