A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological...

Area of Science:

• Systems Biology
• Network Science
• Computational Biology

Background:

• Network controllability integrates control theory with network structure for biological systems.
• Identifying critical nodes is crucial for understanding network control, but existing methods struggle with probabilistic interactions.

Purpose of the Study:

• To develop an efficient algorithm for determining critical control nodes in probabilistic directed networks.
• To integrate the probabilistic nature of molecular interactions into network control models.

Main Methods:

• A probabilistic control model based on the minimum dominating set framework was developed.
• Mathematical tools were created to enhance the efficiency of determining critical control nodes in large networks.

Main Results:

• The algorithm efficiently identifies critical control nodes in probabilistic directed networks.
• Application to the human intracellular signal transduction network linked critical nodes to disease-associated genes, including SARS-CoV-2 targets.

Conclusions:

• The proposed methodology provides a practical and efficient way to analyze critical control nodes in probabilistic biological networks.
• This approach can advance the study of diverse biological systems with uncertain or probabilistic interactions.