Network control principles predict neuron function in the Caenorhabditis elegans connectome.

Area of Science:

• Neuroscience
• Systems Biology
• Computational Biology

Background:

• Complex systems theory provides a framework for understanding structure-function relationships in networks.
• Experimental validation of these control principles in biological systems is lacking.
• The nematode Caenorhabditis elegans offers a well-characterized model system for connectome studies.

Purpose of the Study:

• To experimentally validate control principles in a biological network.
• To predict and identify specific neurons involved in C. elegans locomotion.
• To explore the applicability of control theory to complex neural networks.

Main Methods:

• Application of a control framework to the C. elegans connectome.
• Prediction of neuronal involvement in locomotor behaviors.
• Experimental validation using laser ablation techniques on single neurons and neuronal classes.
• Analysis of robustness to connectome variations.

Main Results:

• Identified 12 neuronal classes critical for controlling C. elegans locomotion.
• Predicted and experimentally confirmed the involvement of the previously uncharacterized PDB neuron.
• Demonstrated that only specific neurons within a class (e.g., DD motor neurons) are essential for locomotion.
• Showed that single-cell ablations of DD04 and DD05 neurons affect posterior body movements.

Conclusions:

• Control principles provide a valid framework for predicting neuronal function in C. elegans.
• The PDB neuron plays a significant role in maintaining body bend polarity.
• The study highlights the potential of control theory for analyzing complex connectomes.
• Findings are robust to connectome imperfections, suggesting broad applicability.