Aucello Riccardo, Pernice Simone, Tortarolo Dora, Calogero Raffaele A, Herrera-Rincon Celia, Ronchi Giulia, Geuna Stefano, Cordero Francesca, Lió Pietro, Beccuti Marco
Department of Computer Science, University of Turin, Via Pessinetto 12, Torino, 10149, Italy.
Department of Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza 52, Torino, 10126, Italy.
Bioinformatics. 2025 Mar 4;41(3). doi: 10.1093/bioinformatics/btaf103.
Computational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognizable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, e.g. to the cancer evolution study.
To address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It enables functional studies by combining the analysis of the system's multi-level stable states with its fluctuating conditions. This approach helps to investigate the functional relationships and dependencies among biological entities. This is achieved, thanks to the hybridization of two analysis approaches that capture a system's different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposal's effectiveness was demonstrated by mechanistically simulating the metabolic output of Escherichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection.
GreatMod https://qbioturin.github.io/epimod/, epimod_FBAfunctions https://github.com/qBioTurin/epimod_FBAfunctions, first case study E. coli https://github.com/qBioTurin/Ec_coli_modelling, second case study C. difficile https://github.com/qBioTurin/EpiCell_CDifficile.
计算模型对于解决有关系统进化的关键问题以及解读系统联系至关重要。要使这一概念在生物学和临床领域得到认可，其关键特性在于能够快速审视整个系统，同时牢记其组成部分的不同粒度级别。这种对系统行为的整体观点通过识别适用于例如癌症进化研究的异质行为，扩展了进化研究。
为解决这一方面的问题，我们提出了一种新的建模范式UnifiedGreatMod，它允许建模者将细粒度和粗粒度的生物学信息整合到一个独特的模型中。通过将系统多级稳定状态的分析与其波动条件相结合，它能够进行功能研究。这种方法有助于研究生物实体之间的功能关系和依赖性。这是通过两种捕获系统不同粒度级别的分析方法的混合实现的。然后，将所提出的范式应用于开源通用建模框架GreatMod中，其中利用图形元形式主义简化模型创建阶段，并使用R语言定义用户定义的分析工作流程。通过机械模拟环境营养扰动下大肠杆菌的代谢输出并整合基因表达数据集，证明了该提议的有效性。此外，UnifiedGreatMod被用于研究管腔上皮细胞对艰难梭菌感染的反应。
GreatMod https://qbioturin.github.io/epimod/，epimod_FBAfunctions https://github.com/qBioTurin/epimod_FBAfunctions，第一个案例研究大肠杆菌 https://github.com/qBioTurin/Ec_coli_modelling，第二个案例研究艰难梭菌 https://github.com/qBioTurin/EpiCell_CDifficile。
