Aucello, RiccardoPernice, SimoneTortarolo, DoraCalogero, Raffaele A.Herrera Rincón, CeliaRonchi, GiuliaGeuna, StefanoCordero, FrancescaLió, PietroBeccuti, Marco2025-05-222025-05-222025Riccardo Aucello, Simone Pernice, Dora Tortarolo, Raffaele A Calogero, Celia Herrera-Rincon, Giulia Ronchi, Stefano Geuna, Francesca Cordero, Pietro Lió, Marco Beccuti, UnifiedGreatMod: a new holistic modelling paradigm for studying biological systems on a complete and harmonious scale, Bioinformatics, Volume 41, Issue 3, March 2025, btaf103, https://doi.org/10.1093/bioinformatics/btaf1031367-480310.1093/bioinformatics/btaf103https://hdl.handle.net/20.500.14352/120389This project has received funding from: Ministero dell’Univerisita’ e della Ricerca (MUR) PRIN 2022 project MEDICA: Modelling and vErification of alkaptonuria and multiple sclerosis Driven by biomedICAl data [No 2022RNTYWZ] (MEDICA project to Marco Beccuti) and from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 825410 (ONCOBIOME project to Francesca Cordero and Simone Pernice). The scientific activities of the CINI InfoLife Laboratory supported this research.Motivation 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. Results 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. Availability and implementation 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.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/journal article1367-4811https://doi.org/10.1093/bioinformatics/btaf103https://academic.oup.com/bioinformatics/article/41/3/btaf103/8071862?login=trueopen access57.08573519.8577.1BiologíaInformática (Informática)Física-Modelos matemáticos2406 Biofísica1203.17 Informática2302 Bioquímica24 Ciencias de la Vida