Institute of Computing Science, Poznan University of Technology, Poznan, Poland.
Institute of Computing Science, Poznan University of Technology, Poznan, Poland.
Comput Biol Med. 2024 Jan;168:107729. doi: 10.1016/j.compbiomed.2023.107729. Epub 2023 Nov 20.
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.
这项研究的主要目的是提出算法，以识别使用经典 Petri 网构建的生物系统模型中的重要反应和子过程。这些解决方案允许执行两种分析方法：重要性分析用于识别对模型功能至关重要的单个反应，以及出现分析用于找到基本子过程。为了演示这些方法的实用性，已经对一个示例模型进行了分析。在这种情况下，它是与 DNA 损伤反应机制相关的模型。值得注意的是，所提出的分析可以应用于使用 Petri 网形式化表示的任何生物现象。所提出的分析方法是对基于经典 Petri 网的分析的扩展。它们的用途在于它们能够增强我们对所研究的生物现象的理解。此外，它们可以导致更有效的医疗疗法的发展，因为它们可以帮助确定药物的潜在分子靶标。
