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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Exploiting fluctuations in gene expression to detect causal interactions between genes.

Euan Joly-Smith1,2, Mir Mikdad Talpur1, Paige Allard3

  • 1Department of Physics, University of Toronto, Toronto, Canada.

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|December 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to identify gene interactions by analyzing natural cell-to-cell variability, avoiding cell perturbation. The approach uses a mathematical relation to interpret gene expression noise, enabling causal interaction detection.

Keywords:
E. colicausal effectscomputational biologygene regulationnetwork inferencephysics of living systemsstochastic fluctuationssystems biology

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Area of Science:

  • Systems Biology
  • Molecular Biology
  • Genetics

Background:

  • Understanding cellular behavior necessitates deciphering causal interactions within cellular components.
  • Current methods often require perturbing cellular states, limiting mechanistic insights.

Purpose of the Study:

  • To develop a method for detecting causal gene interactions without altering cellular physiology.
  • To leverage naturally occurring cell-to-cell variability for inferring gene regulatory networks.

Main Methods:

  • Utilizing a mathematical relation to constrain gene expression noise propagation in biochemical networks.
  • Analyzing static population snapshots of genetically identical cells.
  • Developing a passive reporter of gene expression, analogous to dual reporter assays.

Main Results:

  • Demonstrated a theoretical framework for inferring causal interactions from gene expression fluctuations.
  • Provided an experimental proof-of-principle in synthetic gene regulatory circuits in Escherichia coli.
  • Confirmed the method's ability to detect causal interactions in controlled settings.

Conclusions:

  • The presented approach offers a non-perturbational strategy for identifying gene interactions.
  • Further research is required to validate large-scale applicability in natural networks and diverse organisms.