Associative conditioning in gene regulatory network models increases integrative causal emergence

Area of Science:

• Systems biology
• Computational neuroscience
• Evolutionary biology

Background:

• Gene regulatory networks (GRNs) integrate stimuli, forming complex systems.
• Causal emergence quantifies how a system's integrated properties exceed the sum of its components.

Purpose of the Study:

• To investigate how learning affects causal emergence in biological networks.
• To understand the relationship between training, network structure, and emergent properties.

Main Methods:

• Analysis of 29 experimentally derived gene regulatory networks.
• Application of causal emergence measures before, during, and after training.
• Clustering analysis to categorize network responses to training.

Main Results:

• Biological networks demonstrated increased causal emergence following training.
• Five distinct patterns of emergence response to training were identified.
• These patterns did not align with traditional network characterizations but correlated with biological categories.

Conclusions:

• Learning actively enhances the integration and emergence of biological agents from their components.
• Increased causal emergence appears to be an evolutionarily favored property.
• Findings offer insights into scaling intelligence and potential biomedical applications for network-based diseases.