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Inferring the burst dynamics of coupled self-feedback gene expression circuits based on single-cell data.

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Summary

This study introduces a single-cell stochastic burst model to analyze gene expression feedback. Positive feedback restores bimodal gene expression, aiding understanding of cell differentiation and fate determination.

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Genomics

Background:

  • Gene expression regulation is complex, with self-feedback playing a crucial role.
  • Self-feedback burst dynamics in single-cell sequencing data remain underexplored.

Purpose of the Study:

  • To develop and analyze a single-cell stochastic burst model for coupled positive- and negative-feedback gene expression circuits.
  • To investigate the impact of feedback regulation on gene expression dynamics and patterns.

Main Methods:

  • Proposed a single-cell stochastic burst model incorporating coupled positive- and negative-feedback loops.
  • Analyzed dynamical behavior using mouse fibroblast single-cell RNA sequencing (scRNA-seq) data.
  • Inferred genomewide burst dynamics parameters and identified self-feedback regulation patterns.

Main Results:

  • Positive feedback restored bimodal gene expression distribution, unlike negative or no feedback which led to unimodal distributions.
  • Analyzed effects of feedback on burst size, frequency, and noise.
  • Found increasing trends in burst frequency and size with gene mean, while noise decreased; negative feedback had higher mean burst frequency, while positive feedback had higher mean burst size and noise.

Conclusions:

  • Self-feedback regulation significantly influences gene expression patterns, including distribution shape.
  • Feedback type differentially impacts burst dynamics (size, frequency, noise).
  • Findings enhance understanding of gene expression, cell differentiation, and fate determination.