自动构建用于分子相互作用网络计算模拟的 Petri 网模型。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Xuefei Lin, Xiao Chang, Yizheng Zhang, Zhanyu Gao, Xu Chi
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引用次数: 0

摘要

Petri 网通常用于生物系统建模。然而,为复杂的生物系统构建 Petri 网模型往往非常耗时,而且需要研究领域的专业知识,这限制了 Petri 网的应用。为了应对这一挑战,我们开发了 GINtoSPN 这个 R 软件包,它能自动将从全球整合网络(GIN)中提取的多组学分子相互作用网络转换成 GraphML 格式的 Petri 网。这些 GraphML 文件可直接用于信号 Petri 网(SPN)模拟。为了证明这一工具的实用性,我们为 I 型神经纤维瘤病建立了一个 Petri 网模型。与正常皮肤成纤维细胞相比,NF1 基因敲除的模拟结果显示,Ras-GTPs 的持续积累符合预期。此外,我们还发现了其他几个基因因 NF1 功能缺失而受到严重影响,并表现出个体特异性。这些结果凸显了 GINtoSPN 在简化复杂生物系统建模和仿真方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic construction of Petri net models for computational simulations of molecular interaction network.

Petri nets are commonly applied in modeling biological systems. However, construction of a Petri net model for complex biological systems is often time consuming, and requires expertise in the research area, limiting their application. To address this challenge, we developed GINtoSPN, an R package that automates the conversion of multi-omics molecular interaction network extracted from the Global Integrative Network (GIN) into Petri nets in GraphML format. These GraphML files can be directly used for Signaling Petri Net (SPN) simulation. To demonstrate the utility of this tool, we built a Petri net model for neurofibromatosis type I. Simulation of NF1 gene knockout, compared to normal skin fibroblast cells, revealed persistent accumulation of Ras-GTPs as expected. Additionally, we identified several other genes substantially affected by the loss of NF1's function, exhibiting individual-specific variability. These results highlight the effectiveness of GINtoSPN in streamlining the modeling and simulation of complex biological systems.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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