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Stochastic branching-diffusion models for gene expression.

David Cottrell1, Peter S Swain, Paul F Tupper

  • 1Department of Mathematics and Statistics, McGill University, Montreal, Canada.

Proceedings of the National Academy of Sciences of the United States of America
|June 5, 2012
PubMed
Summary
This summary is machine-generated.

Integrating diffusion and stochasticity in biochemical networks is challenging. This study shows protein diffusion length impacts fluctuations, with implications for gene expression models and cellular concentration uniformity.

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Area of Science:

  • Biochemistry
  • Systems Biology
  • Molecular Biology

Background:

  • Biochemical networks involve complex interactions.
  • Modeling these networks requires accounting for diffusion and stochasticity.

Purpose of the Study:

  • To develop analytical expressions for protein numbers in spatial gene expression models.
  • To investigate the influence of diffusion and protein lifetime on fluctuations and covariance.

Main Methods:

  • Utilizing the theory of branching processes.
  • Analyzing a spatial model of gene expression.
  • Estimating protein Kuramoto length using high-throughput data.

Main Results:

  • Protein numbers' mean and variance depend on Kuramoto length.
  • Translational bursting can increase covariance between local protein concentrations.
  • Cytoplasmic proteins in yeast have a large Kuramoto length, suggesting uniform concentration.
  • Local fluctuations deviate from Poisson for long-lived proteins, influenced by mRNA diffusion and translational bursting.

Conclusions:

  • Diffusion significantly impacts the complexity of fluctuations in biochemical networks.
  • Protein diffusion length is a critical parameter in understanding cellular concentration and noise.
  • The findings offer insights into gene expression regulation in different organisms.