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    Area of Science:

    • Computational Biology
    • Systems Biology
    • Genomics

    Background:

    • Gene regulatory networks (GRNs) are crucial for cellular functions, but their dysregulation is linked to diseases like cancer.
    • Interventions in GRNs are complicated by cellular adaptive resistance and incomplete gene expression data.

    Purpose of the Study:

    • To develop a decentralized deep reinforcement learning framework for effective intervention in gene regulatory networks.
    • To address challenges posed by cellular adaptive resistance and limited state information during interventions.

    Main Methods:

    • Formulating GRN intervention as an asymmetric two-player zero-sum game.
    • Deriving a history-dependent intervention policy against a cell with complete gene state knowledge.
    • Utilizing a deep policy gradient approach to approximate the Nash equilibrium policy.

    Main Results:

    • The proposed intervention policy demonstrates robust performance and higher-than-expected gains, even against complex cellular adaptive responses.
    • The method converges to the full-state Nash equilibrium when system state information is complete.
    • Validation on p53-MDM2 and melanoma GRN models shows superior adaptability under uncertainty.

    Conclusions:

    • The developed framework offers a powerful approach for designing robust interventions in gene regulatory networks.
    • This method shows significant promise for improving therapeutic strategies in complex diseases characterized by GRN dysregulation.