Phenotype accessibility and noise in random threshold gene regulatory networks

Area of Science:

• Evolutionary biology
• Systems biology
• Developmental biology

Background:

• Phenotypic variation is essential for evolution.
• Gene regulatory networks (GRNs) drive organismal development and phenotypic diversity.
• Organisms may leverage environmental cues and noise to access novel phenotypes and accelerate adaptation.

Purpose of the Study:

• To investigate how noise in gene expression affects access to new phenotypes and overall phenotypic diversity.
• To explore strategies for increasing phenotypic diversity, such as genome augmentation or increasing gene expression states.
• To analyze the impact of different GRN types on noise-induced phenotypic diversity.

Main Methods:

• Utilized a generic model of organismal development based on gene regulatory networks.
• Simulated varying levels of noise on gene expression states.
• Examined different GRN architectures and the effects of increasing gene expression states or genome size.

Main Results:

• In binary gene expression systems, increased noise generally reduces phenotype accessibility.
• With multiple gene expression states (3-4), noise can moderately enhance the rate of discovering new phenotypes.
• Noise's beneficial effect on phenotype discovery is contingent on a sufficient number of distinct regulatory networks in the population.

Conclusions:

• Noise can enhance phenotypic diversity and adaptation speed, but only under specific conditions.
• Sufficiently diverse regulatory networks and multiple gene expression states are crucial for noise to be advantageous.
• When acquiring more genes or fine-tuning expression is costly, noise offers a viable strategy for accessing unique phenotypes.