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Updated: May 8, 2026
06:21
An Ecdysone Receptor-based Singular Gene Switch for Deliberate Expression of Transgene with Robustness, Reversibility, and Negligible Leakiness
Published on: May 7, 2018
Extrinsic noise driven phenotype switching in a self-regulating gene.
Michael Assaf1, Elijah Roberts, Zaida Luthey-Schulten
1Racah Institute of Physics, Hebrew University of Jerusalem, Jerusalem 91904, Israel.
Physical Review Letters
|August 20, 2013
Summary
Extrinsic noise, not just intrinsic fluctuations, drives cell state transitions in gene networks. This study reveals how external factors alter cell phenotype stability and escape mechanisms, with broad applications for biological decision-making systems.
Area of Science:
- Systems biology
- Cellular heterogeneity
- Gene regulatory networks
Background:
- Cellular phenotypes arise from complex gene regulation networks exhibiting metastable states.
- Population heterogeneity is often attributed to noise-induced transitions between these states.
- Previous research primarily focused on intrinsic noise, neglecting extrinsic factors.
Purpose of the Study:
- To investigate the impact of extrinsic noise on cellular phenotypic state transitions.
- To develop a theoretical framework for analyzing combined intrinsic and extrinsic noise effects.
- To explore how extrinsic noise influences the stability and dynamics of gene regulatory networks.
Main Methods:
- Development of an analytical framework for noise analysis.
- Application to a Boolean genetic switch model.
- Extension to a biologically relevant model with Hill-like regulatory functions.
- Utilizing Monte Carlo simulations for validation.
Main Results:
- Extrinsic noise significantly affects the lifetimes of metastable phenotypic states.
- External fluctuations can fundamentally alter the mechanisms by which cells transition between states.
- The developed theory accurately models the interplay of intrinsic and extrinsic noise.
Conclusions:
- Extrinsic noise plays a critical role in cellular heterogeneity and phenotypic switching.
- The analytical framework provides insights into noise-driven dynamics in gene regulatory networks.
- The theory is generalizable to complex biological decision-making networks.
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