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Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes.

Mohammad Soltani1, Cesar A Vargas-Garcia1, Duarte Antunes2

  • 1Electrical and Computer Engineering Department, University of Delaware, Newark, Delaware, United States of America.

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Cell division timing and gene expression noise are linked. Random cell division and genome duplication impact protein levels, offering new ways to control cellular phenotypes.

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Area of Science:

  • * Molecular and Cellular Biology
  • * Systems Biology
  • * Biophysics

Background:

  • * Gene expression is inherently stochastic, leading to cell-to-cell protein number variability.
  • * Protein half-lives comparable to cell cycle length and low molecule numbers amplify noise through division timing and partitioning errors.

Purpose of the Study:

  • * To derive analytical formulas for total protein noise considering cell cycle duration distributions.
  • * To decompose total noise into components from stochastic expression, partitioning errors, and random cell division.
  • * To investigate the influence of genome duplication timing on protein noise.

Main Methods:

  • * Derivation of analytical formulas for protein noise.
  • * A novel hybrid approach to decompose total noise.
  • * Mathematical modeling of cell cycle duration and genome duplication.

Main Results:

  • * Random cell division times affect mean protein levels and intrinsic noise.
  • * Protein noise can paradoxically decrease with more stochastic cell division times.
  • * An optimal genome duplication time minimizes noise from stochastic expression.

Conclusions:

  • * Theoretical framework for understanding and quantifying protein noise sources.
  • * Novel insights into the interplay between cell cycle dynamics and gene expression noise.
  • * Potential for new experimental methods to dissect noise components and engineer cellular phenotypes.