Shaping protein distributions in stochastic self-regulated gene expression networks

Area of Science:

• Systems Biology
• Molecular Biology
• Biophysics

Background:

• Self-regulatory networks exhibit complex dynamics influenced by system parameters.
• Understanding protein distribution patterns is key to deciphering cellular stochasticity.
• Previous deterministic models can misrepresent dynamic behaviors in biological networks.

Purpose of the Study:

• To develop a method for identifying parameter regions that support bimodal or binary protein distributions.
• To investigate the conditions under which stochastic dynamics, such as protein level switching, occur.
• To analyze the relationship between network parameters and the manifestation of bimodal protein distributions.

Main Methods:

• Utilized a continuous approximation of the chemical master equation to model gene expression dynamics.
• Developed a computational method to map parameter space regions associated with specific protein distribution types.
• Contrasted the continuous approximation approach with traditional deterministic models.

Main Results:

• Bimodal protein distributions are a widespread phenomenon in cooperative gene expression networks with positive feedback.
• A critical leakage threshold was identified, below which bimodality is prevalent across various transcription and translation rates.
• The critical threshold for bimodality is independent of expression rates but dependent on the degree of cooperativity.

Conclusions:

• The developed method accurately identifies parameter regions for stochastic dynamics, offering insights into gene expression control.
• Bimodal dynamics are robust in cooperative networks, particularly under low leakage conditions.
• This approach provides a framework for predicting and designing biological systems with specific switching properties.