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Bistability, probability transition rate and first-passage time in an autoactivating positive-feedback loop.
Xiu-Deng Zheng1, Xiao-Qian Yang, Yi Tao
1Key Laboratory of Animal Ecology and Conservational Biology, Centre for Computational and Evolutionary Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, People's Republic of China.
Plos One
|March 30, 2011
Summary
Stochastic gene expression in bistable systems transitions between states. Noise intensity and correlation influence transition rates and cellular state probabilities.
Area of Science:
- Systems Biology
- Molecular Biology
- Biophysics
Background:
- Bistability in positive-feedback gene regulation leads to distinct cellular states.
- Stochasticity causes random transitions between these states, resulting in bimodal distributions.
- Fokker-Planck equations describe systems with two potential wells due to bistability.
Purpose of the Study:
- Investigate probability transition rates and first-passage times in an autoactivating positive-feedback loop.
- Analyze the impact of maximum transcription rate, additive/multiplicative noise intensities, and their correlation.
- Understand how these factors influence stochastic gene expression and cellular state dynamics.
Main Methods:
- Theoretical investigation of probability transition rate and first-passage time.
- Modeling gene expression with additive and multiplicative external noises.
- Analysis using Fokker-Planck equations with two potential wells.
Main Results:
- Increased maximum transcription rate maintains high gene expression.
- Higher additive noise intensity increases the probability transition rate between states.
- Increased multiplicative noise strength favors the low expression state (left potential well).
- Noise correlation influences state occupancy: positive correlation favors the left well, negative correlation favors the right well.
Conclusions:
- Maximum transcription rate is crucial for maintaining high expression states.
- Additive and multiplicative noise intensities and their correlation significantly modulate stochastic gene expression dynamics.
- Understanding these noise effects is key to controlling cellular states in bistable systems.
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