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A Pathway Analysis Approach Using Petri Net.

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    A new Pathway Analysis using Petri net (PAPet) method effectively identifies perturbed cell signaling pathways in diseases. PAPet outperforms existing methods, offering a robust approach for disease mechanism investigation.

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    Area of Science:

    • Computational Biology
    • Systems Biology
    • Bioinformatics

    Background:

    • Cell signaling pathway perturbations are linked to various diseases.
    • Accurate identification of these perturbed pathways is crucial for understanding disease mechanisms.
    • Existing pathway analysis methods, particularly topology-based ones, have limitations due to simplified models.

    Purpose of the Study:

    • To introduce a novel Pathway Analysis method using Petri nets (PAPet) for modeling and analyzing cell signaling pathways.
    • To develop an algorithm for measuring pathway perturbation under specific conditions.
    • To overcome the limitations of traditional graph-based models in pathway analysis.

    Main Methods:

    • Modeling cell signaling pathways using Petri nets.
    • Developing a perturbation measurement algorithm for Petri net models.
    • Evaluating PAPet using sensitivity, prioritization, mean reciprocal rank, and false-positive rate metrics on 36 real disease datasets.
    • Comparing PAPet against five established methods: FoPA, PADOG, GSEA, CePa, and SPIA.
    • Applying PAPet to gene expression profiles from normal and Pancreatic Ductal Adenocarcinoma (PDAC) samples.

    Main Results:

    • PAPet demonstrated a superior balance across sensitivity, prioritization, mean reciprocal rank, and false-positive rate metrics compared to FoPA, PADOG, GSEA, CePa, and SPIA.
    • PAPet achieved the highest rank in identifying known PDAC-associated pathways when applied to gene expression data.
    • The Petri net modeling approach in PAPet effectively addresses shortcomings of simpler graph-based models.

    Conclusions:

    • PAPet represents a significant advancement in pathway analysis, offering improved accuracy and robustness.
    • The method provides a valuable tool for investigating disease mechanisms through the analysis of perturbed cell signaling pathways.
    • PAPet is recommended for its comprehensive performance and applicability in disease research, including cancer studies.