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    Area of Science:

    • Computational biology
    • Network science
    • Systems biology

    Background:

    • Intra-cellular molecular interaction networks are crucial for understanding systemic diseases like cancer.
    • Network control theory offers powerful tools for analyzing and manipulating these bio-medical networks.
    • Previous algorithms for minimal set control and target control in linear networks exist but have limitations.

    Purpose of the Study:

    • To determine the computational complexity of the target controllability problem in biological networks.
    • To identify and rectify limitations in existing algorithms for network control.
    • To develop an improved algorithm for efficient and accurate network control.

    Main Methods:

    • Proving the NP-hard nature of the target controllability problem under practical constraints.
    • Analyzing the existing Greedy approximation algorithm by Gao et al. for target control.
    • Developing and implementing heuristic-based improvements to the existing algorithm.

    Main Results:

    • The target controllability problem is proven to be NP-hard for bounded control power.
    • The algorithm by Gao et al. was found to be insufficient in certain cases, requiring validation.
    • The improved algorithm demonstrates up to a 10-fold reduction in running time and smaller solution sets.

    Conclusions:

    • Network control theory is vital for advancing cancer therapeutics.
    • The NP-hard nature of target controllability necessitates efficient approximation algorithms.
    • The enhanced algorithm provides a more robust and efficient solution for controlling biological networks.