Molecular distributions in gene regulatory dynamics
Michael C Mackey1, Marta Tyran-Kamińska, Romain Yvinec
1Department of Physiology and Centre for Nonlinear Dynamics, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, Canada H3G 1Y6. mackey@cnd.mcgill.ca
Journal of Theoretical Biology
|January 25, 2011
Summary
This study analyzes molecular noise in gene regulation, finding that bursting and degradation noise are analytically indistinguishable in simple models, offering an alternative to complex simulations.
Area of Science:
- Systems Biology
- Biophysics
- Computational Biology
Background:
- Gene expression involves inherent noise from processes like transcription, translation, and degradation.
- Understanding stochastic effects is crucial for predicting cellular behavior and system stability.
Purpose of the Study:
- To analyze the stationary density and stability of molecular noise in gene regulatory models.
- To compare deterministic and stochastic analyses of inducible or repressible operons.
- To identify parameters governing steady states and density distributions.
Main Methods:
- Mathematical modeling of gene regulatory networks with stochastic elements.
- Analysis of stationary density, global stability, and bifurcation structure.
- Comparison with deterministic (non-stochastic) model analogs.
Main Results:
- Identified key dimensionless parameters controlling unimodal/bimodal densities and stable steady states.
- Demonstrated correspondence between deterministic and stochastic system behaviors.
- Found that bursting and degradation noise are analytically indistinguishable when acting alone.
Conclusions:
- Stochastic analysis can provide insights into gene expression noise, sometimes replacing intensive simulations.
- Noise from bursting transcription/translation or degradation affects molecular distributions.
- The study elucidates the relationship between deterministic and stochastic behaviors in gene regulation.
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