Depending on the amount of data to process, file generation may take longer.

If it takes too long to generate, you can limit the data by, for example, reducing the range of years.

Article

Download BibTeX

Title

Searching for significant reactions and subprocesses in models of biological systems based on Petri nets

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

Computers in Biology and Medicine

Journal year: 2024 | Journal volume: vol. 168

Article type

scientific article

Publication language

english

Keywords
EN
  • biological systems
  • Petri net-based models
  • t-invariants analysis
  • importance analysis
  • occurrence analysis
  • significant reactions and subprocesses
Abstract

EN The primary aim of this research was to propose algorithms enabling the identification of significant reactions and subprocesses within models of biological systems constructed using classical Petri nets. These solutions allow to performance of two analysis methods: an importance analysis for identifying individual reactions critical to the functioning of the model and an occurrence analysis for finding essential subprocesses. To demonstrate the utility of these methods, analyses of an example model have been performed. In this case, it was a model related to the DNA damage response mechanism. It is worth noting that the proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods represent an extension of classical Petri net-based analyses. Their utility lies in their potential to enhance our comprehension of the biological phenomena under investigation. Furthermore, they can lead to the development of more effective medical therapies, as they can aid in the identification of potential molecular targets for drugs.

Date of online publication

20.11.2023

Pages (from - to)

107729-1 - 107729-10

DOI

10.1016/j.compbiomed.2023.107729

URL

https://www.sciencedirect.com/science/article/pii/S0010482523011940?via%3Dihub

Comments

Article Number: 107729

License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

czasopismo hybrydowe

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100

Impact Factor

7 [List 2023]

This website uses cookies to remember the authenticated session of the user. For more information, read about Cookies and Privacy Policy.