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    Boolean networks (BNs) can be stabilized by flipping node states, a method termed state-flipped control. This study presents algorithms for achieving weak and global stabilization in BNs using state-flipped control strategies.

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    Area of Science:

    • Control Theory
    • Computational Biology
    • Network Science

    Background:

    • Boolean networks (BNs) are widely used to model complex biological systems.
    • Controlling the dynamics of BNs is crucial for understanding and manipulating system behavior.
    • Existing control methods may not be suitable for all network structures or desired outcomes.

    Purpose of the Study:

    • To introduce and analyze state-flipped control for stabilizing Boolean networks.
    • To develop criteria and algorithms for achieving weak and global stabilization in BNs.
    • To explore the use of reinforcement learning for control sequence generation.

    Main Methods:

    • Definition of state-flipped control, involving flipping node logical variables.
    • Introduction of a state-flipped-transition matrix to model control effects.
    • Development of algorithms for finding stabilizing kernels and flip sequences.
    • Application of Q-learning for model-free reinforcement learning to achieve global stabilization.

    Main Results:

    • Criteria for judging weak stabilization are presented.
    • An algorithm is proposed to find a stabilizing kernel for weak stabilization.
    • A reachable set approach is used to verify weak stabilization and find flip sequences.
    • A Q-learning algorithm is developed to find flip sequences for global stabilization.

    Conclusions:

    • State-flipped control offers a viable strategy for stabilizing Boolean networks.
    • The proposed algorithms effectively achieve weak and global stabilization.
    • Reinforcement learning, specifically Q-learning, provides a powerful tool for control sequence discovery in BNs.