Target controllability: a feed-forward greedy algorithm in complex networks, meeting Kalman's rank condition

Area of Science:

• Complex Networks
• Systems Biology
• Control Theory

Background:

• Network controllability is crucial for understanding system dynamics and external signal influence.
• Target controllability and structural controllability are NP-hard problems, often requiring separate considerations.
• Kalman's rank condition is vital for effective driver set control, but not always met by structural approaches.

Purpose of the Study:

• To develop an efficient algorithm for target controllability in large complex networks.
• To integrate Kalman's rank condition with structural controllability for enhanced network control.
• To identify potential drug repurposing candidates in breast cancer networks.

Main Methods:

• A feed-forward greedy algorithm was developed for efficient target controllability.
• The algorithm was integrated with Barabasi et al.'s structural controllability approach.
• Empirical evaluations were conducted across diverse network topologies and protein-interaction networks.

Main Results:

• The proposed algorithm consistently requires fewer driver vertices for effective network control compared to existing methods.
• Integration of structural and dynamical approaches yielded a more comprehensive control strategy.
• Application to breast cancer networks identified potential drug repurposing candidates.

Conclusions:

• Addressing both structural and dynamical aspects of network controllability is essential for advanced control strategies.
• The developed algorithm offers a superior approach for efficient target controllability in complex systems.
• The findings have significant implications for biomedical research, particularly in cancer treatment.