Control of Large-Scale Boolean Networks via Network Aggregation

Area of Science:

• Systems Biology
• Computational Neuroscience
• Control Theory

Background:

• Boolean networks are widely used to model complex biological and computational systems.
• Analyzing the controllability and stabilizability of large Boolean networks presents significant computational challenges due to exponential complexity.
• Existing methods often struggle with scalability as the number of nodes increases.

Purpose of the Study:

• To develop computationally efficient methods for assessing the controllability and stabilizability of Boolean control networks.
• To address the scalability issue by proposing a network partitioning strategy.
• To establish conditions for controllability and stabilizability in partitioned Boolean networks.

Main Methods:

• Partitioning the Boolean control network graph into smaller subnetworks.
• Analyzing the controllability and stabilizability of individual subnetworks.
• Developing necessary conditions for controllability and stabilizability based on a general aggregation structure.
• Deriving a sufficient condition for stabilizability specifically for acyclic aggregation structures.

Main Results:

• Proposed easily verifiable necessary conditions for controllability and stabilizability applicable to general aggregation structures.
• Developed a sufficient condition for stabilizability in the case of acyclic aggregation.
• Demonstrated that this partitioning approach significantly reduces computational complexity when subnetwork sizes are small relative to the entire network.

Conclusions:

• Network partitioning offers a viable strategy to overcome the computational bottleneck in analyzing Boolean control networks.
• The proposed conditions provide practical tools for evaluating controllability and stabilizability.
• The method is particularly effective for acyclic network structures, enabling efficient analysis of large-scale systems.