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Parallel Exchange of Randomized SubGraphs for Optimization of Network Alignment: PERSONA.

Erhun Giray Tuncay, Rza Cenk Erdur, Tim Conrad

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 4, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Network alignment in protein-protein interaction networks identifies similar biological regions across organisms. PERSONA is a novel parallel approach that improves alignment by enabling continuous sharing of partial solutions among multiple agents, optimizing both topological and node similarity.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Network alignment in protein-protein interaction networks (PPINs) aims to identify conserved functional regions across different organisms.
    • A key challenge is balancing multiple similarity objectives (e.g., topological, functional) within alignment strategies, which can compromise biological relevance.
    • Existing methods often face trade-offs, potentially overlooking critical biological insights by prioritizing certain objectives over others.

    Purpose of the Study:

    • To address the multi-objective optimization challenge in network alignment.
    • To propose a novel parallel approach, PERSONA, for enhanced network alignment.
    • To improve the biological relevance and balance of network alignment solutions.

    Main Methods:

    • Developed PERSONA, a parallel network alignment approach utilizing a reactive actor environment.
    • Employed a particle swarm optimization strategy where multiple aligners (actors) continuously share and refine partial solutions.
    • Incorporated randomized parameter values for individual actor heuristics to explore the search space comprehensively.
    • Actors exchange and evaluate subgraphs representing partial solutions, iteratively improving the overall alignment.

    Main Results:

    • Achieved remarkably optimized and balanced solutions for network alignment problems.
    • Demonstrated significant improvements in both topological and node similarity objectives simultaneously.
    • PERSONA effectively mitigates the trade-offs inherent in multi-objective network alignment.

    Conclusions:

    • PERSONA offers a robust framework for multi-objective network alignment by leveraging parallel processing and collaborative solution refinement.
    • The approach enhances the discovery of functionally similar regions in protein-protein interaction networks.
    • This method provides a more biologically relevant and balanced outcome compared to traditional network alignment strategies.