HUman MEtabolism Specific Signature
Transcriptomic analysis is a key tool for exploring gene expression, but the complexity of biological systems often limits its insights. In particular, the lack of intermodal or multi-layered analysis hinders the ability to fully capture key cellular functions such as metabolism from transcriptomic data alone. Here, we introduce a novel approach that informs transcriptomic data analysis with metabolic network modeling to address this. Unlike traditional methods, HUman MEtabolism Specific Signature (HUMESS) uses genome-scale metabolic modeling and flux analysis to highlight reactions and involved genes based on their metabolic significance, offering a deeper understanding of transcriptomic data. Our computational pipeline, supported by a user-friendly Rshiny application, enhances gene expression analysis by uncovering metabolic phenotypic signatures.
The project is based on the expertise of LS2N in relation to the integration of heterogeneous data and multi-scale modeling. The ComBi team proposes to formalize the reconstruction of the metabolic network as a constraint-based problem (MILP).
References
Article
Paré, L., Bordron, P., David, L., Mahé, M., Bihouée, A., & Eveillard, D. (2025). HUMESS: integrating quantitative transcriptomic analysis and metabolic modeling to unveil condition-specific gene signatures. Bioinformatics, 41(8). Read
Pipeline : Gitlab
R-shiny for results analysis : Shinymess
This project has been funded for 2 years by Biogenouest and now for one year by SysMics Labex
