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Updated: May 24, 2026
07:59
Temporal Quantification of MAPK Induced Expression in Single Yeast Cells
Published on: October 4, 2013
Stochastic signalling rewires the interaction map of a multiple feedback network during yeast evolution.
Chieh Hsu1, Simone Scherrer, Antoine Buetti-Dinh
1Biozentrum, University of Basel, Klingelbergstrasse 50/70, Basel 4056, Switzerland.
Nature Communications
|February 23, 2012
Summary
Genetic networks rewire through interaction changes. Synthetic circuits reveal feedback loops function autonomously via stochastic gene expression, not just interaction strength.
Area of Science:
- Systems biology
- Molecular biology
- Genetics
Background:
- Genetic networks evolve via rewiring of interactions, leading to new regulatory mechanisms.
- The galactose network utilizes the Gal4p transcriptional activator to enhance expression of GAL1/GAL3 and GAL2 genes.
- Feedback loop strength in genetic networks is often determined by the number of transcription factor binding sites.
Purpose of the Study:
- To investigate how wiring strength and stochasticity influence feedback loop function in genetic regulatory circuits.
- To determine the role of nonlinear stochastic transcriptional responses in enabling autonomous feedback loop operation.
Main Methods:
- Construction and analysis of synthetic genetic circuits.
- Experimental manipulation of Gal4p binding sites to alter wiring strength.
- Measurement of gene expression, RNA decay rates, and transcriptional bursting dynamics.
Main Results:
- Increasing Gal4p binding sites enhances direct gene expression but does not feed back into the circuit.
- Feedback loops are activated by genes exhibiting frequent stochastic bursts and rapid RNA decay.
- Rapid adaptation to galactose is achievable even with weakly expressed genes.
Conclusions:
- Nonlinear stochastic transcriptional responses are crucial for autonomous feedback loop function.
- Feedback loop functionality can operate independently of the strength of interactions within the circuit.
- Understanding stochasticity is key to deciphering the evolution and function of genetic regulatory networks.
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