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    This study introduces a novel control algorithm for stabilizing biological systems modeled by Boolean networks (BNs). The method simplifies control by reformulating the problem into a smaller graph, reducing computational load and control inputs.

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    Area of Science:

    • Systems Biology
    • Computational Biology
    • Control Theory

    Background:

    • Output stabilizing control is crucial in systems biology for understanding biological network phenotypes.
    • Key phenotypes are often determined by a small subset of phenotypic marker nodes.
    • Complex biological systems are frequently modeled using Boolean networks (BNs).

    Purpose of the Study:

    • To develop a novel control algorithm for output stabilizing control of Boolean networks (BNs).
    • To identify constant control inputs that drive BNs towards desired long-term behaviors concerning specified output nodes.

    Main Methods:

    • Leveraging algebraic properties of Boolean logic.
    • Reformulating the output stabilizing control problem into a graph theoretic problem using auxiliary BNs.
    • Reducing the scale of the problem by analyzing auxiliary BNs.

    Main Results:

    • The proposed method significantly reduces the scale of the control problem compared to the original BN.
    • The algorithm demonstrates superiority over previous methods in terms of the number of control inputs required.
    • Reduced computational loads are achieved by searching within the reduced BNs.

    Conclusions:

    • The novel control algorithm effectively stabilizes complex biological systems modeled by Boolean networks.
    • The method offers an efficient approach for identifying control strategies in systems biology.
    • The approach is validated through extensive numerical experiments on random and real biological networks.