Controlling E. coli Gene Expression Noise.

Area of Science:

• Systems Biology
• Synthetic Biology
• Biophysics

Background:

• Cellular processes exhibit inherent variability in protein production, leading to significant cell-to-cell differences in protein copy numbers.
• Understanding and controlling this biological noise is crucial for predictable synthetic gene circuit design and fundamental biological research.

Purpose of the Study:

• To investigate methods for controlling intracellular protein copy number variability in synthetic gene expression systems.
• To develop and apply a novel framework, stochastic control analysis (SCA), for studying noise propagation and control in biochemical networks.

Main Methods:

• Development of stochastic control analysis (SCA), a sensitivity-based framework for noise control.
• Application of SCA to synthetic gene expression systems in Escherichia coli.
• Analysis of protein copy number distributions and noise scaling relationships.

Main Results:

• Dual control of transcription and translation efficiencies offers the most effective noise-versus-mean control.
• Protein expression distributions were found to follow the gamma distribution, consistent with chromosomal proteins.
• Bursty translation was identified as a major contributor to cell-to-cell variability in protein numbers.
• Accounting for autofluorescence fluctuations recovered correct noise-mean scaling for weak fluorescence signals.

Conclusions:

• Stochastic control analysis provides a powerful tool for understanding and manipulating noise in biological systems.
• Targeting both transcriptional and translational efficiencies is key for precise control of protein expression variability.
• Identifying and mitigating sources of noise, such as bursty translation, is essential for robust synthetic biology applications.