What can we learn when fitting a simple telegraph model to a complex gene expression model?

Area of Science:

• Molecular Biology
• Systems Biology
• Biophysics

Background:

• Single-cell gene expression analysis often employs the random telegraph model to describe mRNA or protein number fluctuations.
• This model simplifies gene dynamics by considering synthesis, decay, and gene state switching (active/inactive).
• However, it overlooks complex mechanisms like feedback, non-exponential inactivation, and multiple activation pathways.

Purpose of the Study:

• To investigate the dynamical properties of complex gene expression models.
• To assess the ability of the simple telegraph model to represent complex gene expression distributions.
• To explore how effective parameters from the telegraph model can reveal underlying gene regulation.

Main Methods:

• Fitting steady-state mRNA or protein number distributions from complex models to the simple telegraph model.
• Analyzing conditional distributions within the active gene state.
• Utilizing single-cell data at multiple time points and varying experimental conditions.

Main Results:

• The three-parameter telegraph model accurately captures steady-state and conditional distributions of complex gene expression models.
• Some effective parameters reliably reflect complex model dynamics, while others may deviate.
• Effective parameters can characterize multimodality and reveal underlying gene regulation mechanisms.
• Results are robust against cooperative transcriptional regulation and extrinsic noise.

Conclusions:

• The random telegraph model serves as a powerful, simplified representation of complex gene expression dynamics.
• Effective parameter analysis provides a method to infer gene regulation mechanisms from single-cell data.
• Faster relaxation to steady state improves parameter inference precision, especially under high extrinsic noise.