2133. Evaluation of Minimal Cut Sets in Biological Systems Modeled by Petri Nets
Invited abstract in session MC-56: Methods & Models in Computational Biology, stream Computational Biology, Bioinformatics and Medicine.
Monday, 12:30-14:00Room: Liberty 1.11
Authors (first author is the speaker)
| 1. | Agnieszka Rybarczyk
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| Institute of Computing Science, Poznan University of Technology | |
| 2. | Marcin Radom
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| Institute of Computing Science, Poznan University of Technology | |
| 3. | Piotr Formanowicz
|
| Institute of Computing Science, Poznan University of Technology |
Abstract
Biological systems are complex networks of interactions that define their structure and function. To understand these systems, Petri nets are widely used, enabling both structural representation and analysis of biological processes. A key approach in this context is identifying minimal cut sets (MCSs), which help determine how disabling specific processes affects system behavior. While algorithms exist for finding MCSs, assessing their quality remains challenging, as some may unintentionally disrupt essential components. To address this, we proposed two evaluation methodologies: (1) analyzing t-invariants to identify structural dependencies and (2) assessing the impact on potentially starved transitions to capture dynamic network behavior. The effectiveness of the proposed methodologies is demonstrated through selected case studies.
Keywords
- Computational Biology, Bioinformatics and Medicine
- Computer Science/Applications
- System Dynamics and Theory
Status: accepted
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