A computational framework for analyzing stochasticity in gene expression.

Area of Science:

• Molecular Biology
• Systems Biology
• Biophysics

Background:

• Stochastic fluctuations in gene expression create varied protein levels within cell populations.
• Existing theoretical models for gene expression stochasticity are numerous, but a robust, assumption-free method for inferring underlying mechanisms is lacking.

Purpose of the Study:

• To develop a robust, efficient, and assumption-free method for inferring molecular mechanisms from protein expression distributions.
• To estimate biochemical rate constants governing chromatin modification, transcription, translation, and RNA/protein degradation.

Main Methods:

• Derived analytical solutions for the first four moments of protein distributions.
• Demonstrated that these four moments fully characterize protein distribution shapes.
• Developed an efficient algorithm to infer gene expression rate constants from these moments.

Main Results:

• Most protein distributions are consistent with multiple sets of biochemical rate constants, indicating degeneracy.
• The inferred rate constant solution space often reveals underlying mechanisms, such as distinguishing transcriptional bursting from constitutive transcription.
• The method achieved accurate rate constant estimation in 91% of tested cases, even without assumptions.

Conclusions:

• The developed framework provides a powerful tool for deducing specific molecular contributions to gene expression patterns.
• The method successfully infers individual or ratio rate constants, offering new insights into gene regulation.
• This approach advances our understanding of how molecular mechanisms shape protein level distributions.