WDNfinder: A method for minimum driver node set detection and analysis in directed and weighted biological network.

Area of Science:

• Systems Biology
• Network Analysis
• Computational Biology

Background:

• Structural controllability is crucial for analyzing dynamical systems and biological networks.
• Existing biological network analyses are hindered by inaccurate node and edge data from public sources.
• Accurate identification of essential network components is vital for understanding biological processes.

Purpose of the Study:

• To develop a novel analysis package, WDNfinder, for structural controllability in biological networks.
• To incorporate node connection strength into structural controllability analysis.
• To improve the accuracy of essential node prediction and minimum driver node set (MDS) identification.

Main Methods:

• WDNfinder package developed in Python.
• Structural controllability analysis considering node connection strength.
• Application to human cancer signaling and p53-mediated DNA damage response networks.

Main Results:

• WDNfinder demonstrates high accuracy in predicting essential nodes in tested biological networks.
• The package effectively narrows down the minimum driver node set (MDS) using domain knowledge.
• More biologically meaningful MDSs were identified in the p53-mediated DNA damage response network.

Conclusions:

• WDNfinder offers a robust approach to structural controllability analysis in biological networks.
• The consideration of node connection strength improves the reliability of network analysis.
• WDNfinder provides a valuable tool for discovering critical nodes and pathways in complex biological systems.