Threshold-dominated regulation hides genetic variation in gene expression networks

Area of Science:

• Systems biology
• Computational biology
• Genetics

Background:

• Dynamical models with feedback and sigmoidal responses feature thresholds for self-regulation.
• Mathematical analysis indicates variables near thresholds exhibit robustness to parameter perturbations in homeostatic states.
• This phenomenon is termed threshold robustness.

Purpose of the Study:

• Investigate the empirical relevance of threshold robustness in gene regulatory networks.
• Examine robustness across varying response function steepnesses, from near on/off to Michaelis-Menten conditions.
• Analyze threshold robustness in a three-gene system with negative feedback loops.

Main Methods:

• Performed simulation studies on a three-gene system with one downstream gene.
• Utilized various logical input gates and sigmoidal or binary dose-response functions.
• Varied parameter values to represent functional genetic variation and analyzed coefficient of variation (CV) of gene product concentrations.

Main Results:

• Threshold robustness increases with response steepness; regulating genes show significantly smaller CVs than unregulating genes, especially for steep responses.
• Robustness diminishes as steepness approaches Michaelis-Menten conditions.
• Loss of robustness occurs if parameter perturbations move equilibrium values far from the threshold.

Conclusions:

• Threshold robustness is the ability to maintain active regulation near a threshold in a homeostatic state despite perturbations.
• Negative feedback loops inherent in homeostatic states maintain this robustness by regulating the variable.
• Threshold regulation is a general phenomenon in feedback networks with sigmoidal responses (without positive feedback), explaining hidden genetic variation via genotype-phenotype mapping.