Graphlet Degree Distribution Sensibility in Petri Net-Based Models of Biological Systems

Graphlet Degree Distribution Agreement (GDDA) is a comparison metric created for graphlets. Unfortunately, it has been reported and confirmed that it has an issue with the stability of result values in low-density graphs. Recently, graphlets have been modified for comparison of Petri net-based models of biological systems. It is crucial to find out if those problems reaper in the case of the modified graphlets, establish a scale of the instability impact on the GDDA value, and find out if it is a common problem for models of biological systems represented by Petri nets. Our results confirmed that the GDDA metric when used for the new graphlets is also unstable for part of the searched density spectrum. Because of that, some biological networks represented by Petri nets are vulnerable to this problem. While instability occurs in the Petri net environment, its characteristics have changed. The scale of an instability impact on the GDDA value is around half of its original size. The localization of vulnerable density areas is different from that in the case of undirected Protein-Protein Interaction (PPI) models. In contrast to the latter, the existence of a high stability area for very sparse models has been observed.

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Journal
IEEE Transactions on Computational Biology and Bioinformatics
Published
2025-03-31
DOI
https://doi.org/10.1109/tcbbio.2025.3555827
Primary Topic
Gene Regulatory Network Analysis
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article
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article

Graphlet Degree Distribution Sensibility in Petri Net-Based Models of Biological Systems

Piotr Formanowicz, Bartłomiej Szawulak
IEEE Transactions on Computational Biology and Bioinformatics
Gene Regulatory Network Analysis
article

Graphlet Degree Distribution Sensibility in Petri Net-Based Models of Biological Systems

Piotr Formanowicz, Bartłomiej Szawulak
article en

Abstract

Graphlet Degree Distribution Agreement (GDDA) is a comparison metric created for graphlets. Unfortunately, it has been reported and confirmed that it has an issue with the stability of result values in low-density graphs. Recently, graphlets have been modified for comparison of Petri net-based models of biological systems. It is crucial to find out if those problems reaper in the case of the modified graphlets, establish a scale of the instability impact on the GDDA value, and find out if it is a common problem for models of biological systems represented by Petri nets. Our results confirmed that the GDDA metric when used for the new graphlets is also unstable for part of the searched density spectrum. Because of that, some biological networks represented by Petri nets are vulnerable to this problem. While instability occurs in the Petri net environment, its characteristics have changed. The scale of an instability impact on the GDDA value is around half of its original size. The localization of vulnerable density areas is different from that in the case of undirected Protein-Protein Interaction (PPI) models. In contrast to the latter, the existence of a high stability area for very sparse models has been observed.

IEEE Transactions on Computational Biology and BioinformaticsVol. 22(4)
Poznań University of Technology (PL)
Openalex Percentile: Top 94%
Gene Regulatory Network Analysis
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Graphlet Degree Distribution Sensibility in Petri Net-Based Models of Biological Systems — Piotr Formanowicz, Bartłomiej Szawulak · IEEE Transactions on Computational Biology and Bioinformatics (2025) | TGRS Research Map | TGRS