Inferring contagion in regulatory networks

Area of Science:

• Systems Biology
• Bioinformatics
• Computational Biology

Background:

• Gene regulatory networks (GRNs) are crucial for understanding cellular processes.
• Inferring directionality in GRNs is challenging, with limited interpretable definitions.
• Existing models often rely on causality, which may not fully capture biological regulation.

Purpose of the Study:

• To introduce a novel concept of "contagion" for inferring gene regulatory network directionality.
• To develop and validate a bootstrap algorithm for testing the contagion concept.
• To apply the contagion method to both simulated and real biological data.

Main Methods:

• Introduced the "contagion" concept to define edge directionality based on expression dependencies.
• Developed a bootstrap algorithm to statistically assess the contagion measure.
• Applied the method to simulated datasets and a large-scale biological dataset.

Main Results:

• The contagion concept provides an interpretable measure of directionality in gene regulatory networks.
• The bootstrap algorithm effectively tested the significance of inferred directionality.
• Application to real data identified several genes confirmed by literature to be part of the TP53 pathway.

Conclusions:

• The contagion concept offers a valuable alternative for inferring directionality in gene regulatory networks.
• This approach enhances the interpretability of regulatory relationships.
• The method shows promise for discovering biologically relevant regulatory pathways, such as the TP53 pathway.