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Stochastic gene expression conditioned on large deviations.

Jordan M Horowitz1, Rahul V Kulkarni

  • 1Department of Physics, Physics of Living Systems Group, Massachusetts Institute of Technology, 400 Technology Square, Cambridge, MA 02139, United States of America.

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This summary is machine-generated.

Stochastic gene expression leads to rare events driving cell variation. This study uses advanced statistical mechanics and queueing theory to analyze these large deviations in gene expression models, providing new insights into cellular dynamics.

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Area of Science:

  • * Biophysics
  • * Statistical Mechanics
  • * Systems Biology

Background:

  • * Intrinsic stochasticity in gene expression causes significant phenotypic variation in cell populations.
  • * Rare events in gene expression are crucial drivers of this variation.
  • * Analyzing large deviations in stochastic models is key to understanding these rare events.

Purpose of the Study:

  • * To analyze large deviations in general stochastic models of gene expression.
  • * To apply a framework from non-equilibrium statistical mechanics for conditioned Markovian processes.
  • * To quantify fluctuations and rare events in gene expression.

Main Methods:

  • * Employed a framework for analyzing Markovian processes conditioned on rare events.
  • * Integrated queueing theory approaches.
  • * Modeled gene expression using Batch Markovian Arrival Processes (BMAP) to derive analytical results.

Main Results:

  • * Derived exact analytical results for large deviations in gene expression models.
  • * Showed that conditioning-free driven processes can be represented by a BMAP with renormalized parameters.
  • * Developed methods to quantify the likelihood of large deviations and characterize system fluctuations.

Conclusions:

  • * The study provides a quantitative framework for understanding rare events in gene expression.
  • * Results can identify model parameters leading to dynamical phase transitions.
  • * Offers tools to characterize cellular fluctuations conditional on rare events.