Transcription and noise in negative feedback loops

Area of Science:

• Molecular Biology
• Systems Biology
• Biophysics

Background:

• Genetic regulatory networks control gene expression through complex interactions.
• Transcriptional noise, or random fluctuations in gene expression, is a key factor in cellular dynamics.
• Negative feedback loops (FBL) are common motifs in genetic networks, often involved in regulating gene expression levels.

Purpose of the Study:

• To investigate the dynamics and impact of negative feedback loops on transcriptional noise using a stochastic approach.
• To quantify the noise reduction and protein abundance variations influenced by FBL strength.
• To explore the role of intrinsic and external noise in protein induction within FBL systems.

Main Methods:

• Development of a stochastic model to analyze gene transcription dynamics.
• Mathematical analysis of feedback loop strength (self-repression coupling D).
• Computation of variance and coefficient of variation to assess transcriptional noise and protein abundance.

Main Results:

• Identification of bimodal activity in transcriptional regulation based on FBL coupling strength (D).
• Demonstration of a 28% greater reduction in transcriptional noise variance in the strong coupling region (D>>1) compared to previous findings.
• Observation that the coefficient of variation for protein abundance is parameter-independent in the strong coupling region, aligning with experimental data.
• Significant induction of regulating proteins by both intrinsic and external noise in the strong coupling regime.

Conclusions:

• Strong self-repression in FBL significantly reduces transcriptional noise.
• The coefficient of variation provides insights into noise effects on protein abundance.
• Inherent noise in FBL systems facilitates basal protein production even under high repression, suggesting a functional role for noise.