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Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.

Oleg Lenive1, Paul D W Kirk2, Michael P H Stumpf3

  • 1ICR, Sutton, SM2 5NG, UK.

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|August 24, 2016
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Summary
This summary is machine-generated.

This study introduces a faster simulation method for gene expression, enabling better inference of reaction rates and noise parameters from cell data. The findings show that extrinsic noise, while often small, is significant for gene expression variability.

Keywords:
Approximate Bayesian computationExtrinsic noiseGene expressionStochastic simulation

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Gene expression exhibits inherent stochasticity (intrinsic noise), often involving low molecule counts.
  • Variability between identical cells also arises from external factors (extrinsic noise), which is challenging to model.
  • Accurate parameter inference for gene expression models is difficult due to computational costs of exact stochastic simulations.

Purpose of the Study:

  • To develop a computationally efficient, model-specific stochastic simulation algorithm for the two-state gene expression model.
  • To enable accurate inference of reaction rates and extrinsic noise parameters from single-cell measurements.
  • To assess the contribution of extrinsic noise to overall gene expression variability.

Main Methods:

  • Developed a specialized, exact stochastic simulation algorithm for the two-state gene expression model.
  • The algorithm bypasses full temporal simulation, directly outputting molecule counts at a specific time point.
  • Integrated the algorithm with approximate Bayesian computation for parameter inference.

Main Results:

  • The new simulation algorithm significantly reduces computational cost, especially for models with high protein output.
  • Applied to published gene expression data, the method successfully inferred model rate and noise parameters.
  • Analysis revealed that extrinsic noise contributions are non-negligible for most genes.

Conclusions:

  • The developed simulation approach makes parameter inference for stochastic gene expression models more tractable.
  • Extrinsic noise plays a significant, though often moderate, role in the variability of gene expression.
  • This work provides a valuable tool for understanding the sources of noise in gene expression.