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An Efficient Approach Towards the Source-Target Control of Boolean Networks.

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    We developed a new decomposition-based method for source-target control in Boolean networks. This approach efficiently identifies the minimal nodes to perturb for state transitions in large biological networks.

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    Area of Science:

    • Systems Biology
    • Computational Biology
    • Network Science

    Background:

    • Asynchronous Boolean networks model complex biological systems.
    • Controlling network dynamics to reach specific states is crucial for understanding biological functions.
    • State-space explosion poses a significant challenge for large-scale network analysis.

    Purpose of the Study:

    • To develop an efficient method for source-target control in asynchronous Boolean networks.
    • To address the scalability limitations of existing global control approaches.
    • To identify the minimal set of nodes for single-step state transitions.

    Main Methods:

    • Derived a decomposition-based algorithm for minimal source-target control.
    • Exploited network structure and dynamics for computational efficiency.
    • Incorporated source state information for further optimization.

    Main Results:

    • The decomposition-based solution significantly outperforms existing approaches on large networks.
    • The optimized solution demonstrates enhanced efficiency.
    • The method was successfully applied to both real-life and random biological networks.

    Conclusions:

    • The proposed decomposition-based approach offers an efficient and scalable solution for source-target control in Boolean networks.
    • This method is effective for analyzing and controlling complex biological systems.
    • The findings pave the way for better understanding and manipulation of biological network dynamics.