Attractor-Transition Control of Complex Biological Networks: A Constant Control Approach

Area of Science:

• Systems Biology
• Computational Biology
• Network Science

Background:

• Complex biological networks are often modeled as Boolean networks (BNs).
• Controlling the dynamics of these networks, specifically transitioning between stable states (attractors), is crucial for understanding and manipulating biological functions.
• Existing methods may lack the versatility to handle different types of attractors or require extensive computational resources.

Purpose of the Study:

• To develop a robust and versatile control strategy for attractor-transition in Boolean networks.
• To steer a BN from an initial attractor to a desired target attractor.
• To provide a computationally efficient and reproducible method for biological network control.

Main Methods:

• Leveraging Boolean algebraic properties of state transition equations for attractor analysis.
• Employing a coordinate transformation to simplify the network dynamics and identify self-stabilizing variables.
• Applying the feedback vertex set (FVS) control scheme to determine control inputs for non-stabilizing variables.
• Validating the method on both random BNs and complex biological system models.

Main Results:

• Successfully demonstrated attractor-transition control for BNs, guiding them from initial to desired attractors.
• Identified self-stabilizing state variables that do not require external control.
• Developed control strategies applicable to both fixed-point and cyclic attractors.
• Validated the effectiveness through extensive numerical experiments on diverse network models.

Conclusions:

• The proposed attractor-transition control method is effective and versatile for Boolean networks.
• The integration of Boolean algebra and FVS offers a powerful approach for biological network engineering.
• The method facilitates reproducible research with publicly available code and detailed results.