Stochastic model of transcription factor-regulated gene expression

Area of Science:

• Systems Biology
• Molecular Biology
• Genetics

Background:

• Gene expression is regulated by transcription factors (TFs).
• Stochastic fluctuations in TF levels can influence protein output.
• Understanding these dynamics is crucial for deciphering gene regulation and genetic disorders.

Purpose of the Study:

• To investigate the impact of transcription factor (TF) fluctuations on gene expression using a stochastic model.
• To analyze how changes in gene copy number affect TF fluctuations and target gene protein levels.
• To explore the relevance of these findings to haploinsufficiency disorders.

Main Methods:

• Development of a stochastic model for TF-regulated gene expression involving two genes.
• Analytical calculations to determine the effect of TF fluctuations on protein level distributions.
• Investigation of reduced gene copy number effects (from two to one).
• Calculation of probabilities for target gene protein levels exceeding a threshold.
• Comparison of analytical results with numerical simulations of a detailed stochastic model.

Main Results:

• TF fluctuations significantly affect target gene protein levels when TF concentration is in a highly sensitive range.
• Reducing the copy number of the TF-producing gene enhances fluctuations, leading to different outcomes compared to deterministic models.
• The probability of exceeding a protein threshold was accurately calculated using probability density functions.
• Numerical simulations validated the analytical findings.

Conclusions:

• Stochastic effects in TF regulation are critical, particularly in sensitive gene expression regimes.
• The model provides insights into how reduced gene dosage (haploinsufficiency) arises from enhanced TF noise.
• The study successfully explains experimental observations of haploinsufficiency in genes like Nkx 3.1.