Distribution of population-averaged observables in stochastic gene expression

Area of Science:

• Systems Biology
• Molecular Biology
• Biophysics

Background:

• Phenotypic diversity in genetically identical cells arises from stochasticity in gene regulatory networks.
• Gene expression noise is a fundamental aspect of cellular function and regulation.

Purpose of the Study:

• To analyze how population-averaged gene expression levels reflect underlying stochastic mechanisms.
• To investigate the impact of population size on the accuracy of gene expression measurements.
• To compare different gene regulation models regarding their population-averaged properties.

Main Methods:

• Utilized analytical expressions for probability generating functions.
• Applied large deviation theory to calculate distributions and moments.
• Employed stochastic simulations for validation.
• Derived exact results for asymptotic properties of population averages.

Main Results:

• Calculated distributions and moments of population-averaged mRNA and protein levels.
• Demonstrated that population-averaged quantities reflect underlying mechanisms to varying extents across different models.
• Identified qualitative differences in asymptotic properties among models.
• Validated analytical findings with stochastic simulations.

Conclusions:

• The extent to which population-averaged gene expression reflects underlying mechanisms is model-dependent.
• Stochastic gene expression models exhibit distinct behaviors concerning population averaging.
• Accurate interpretation of cellular heterogeneity requires considering the specific gene regulatory network and measurement conditions.