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Related Experiment Video

Updated: Mar 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Design of Probabilistic Boolean Networks Based on Network Structure and Steady-State Probabilities.

Koichi Kobayashi, Kunihiko Hiraishi

    IEEE Transactions on Neural Networks and Learning Systems
    |June 14, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for determining probabilistic Boolean networks (PBNs) using network structure and steady-state properties. The approach addresses the complexity of PBN inverse problems in systems biology.

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    Last Updated: Mar 19, 2026

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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    Area of Science:

    • Systems Biology
    • Synthetic Biology
    • Computational Biology

    Background:

    • Probabilistic Boolean networks (PBNs) are crucial for modeling complex biological systems.
    • Determining PBNs from network structure and steady-state properties is a challenging inverse problem.
    • Existing methods for Boolean networks (BNs) do not fully address the probabilistic aspects.

    Purpose of the Study:

    • To propose a novel solution method for finding PBNs based on network structure and desired steady-state properties.
    • To address the inverse problem of PBN construction in systems and synthetic biology.
    • To provide a computationally tractable approach for PBN inference.

    Main Methods:

    • A matrix-based representation for PBNs is utilized.
    • The method calculates Boolean functions, probabilities of function selection, and candidate function counts.
    • The approach is designed to handle the increased complexity compared to standard Boolean networks.

    Main Results:

    • A new method for solving the PBN inverse problem is presented.
    • Numerical examples demonstrate the effectiveness of the proposed solution.
    • The method successfully infers PBNs with specified steady-state behaviors.

    Conclusions:

    • The proposed matrix-based method offers an effective solution for inferring PBNs.
    • This work advances the capability to model biological networks with probabilistic dynamics.
    • The findings have implications for the design and analysis of synthetic biological systems.