Understanding cellular metabolism often involves an accurate estimation of metabolic fluxes–the rates at which metabolites are converted in biochemical pathways. Flux Variability Analysis (FVA) is the gold standard for computing reaction flux intervals. However, its reliance on linear programming makes it computationally intensive, often requiring hours or days for large cohorts on complex genome-scale metabolic network models. To address this limitation, we propose mFLIP, a machine learning-based framework for predicting flux intervals across metabolic pathways. The models were trained using large-scale metabolomics datasets comprising over 22,000 samples obtained from Metabolomics Workbench and MetaboLights. Multiple machine learning and deep learning approaches, including Random Forest, XGBoost, CNN, GNN, VAE, and FT-Transformer, were evaluated. Model performance was independently validated across six independent cancer datasets (Breast, Colon, Pancreatic, Prostate, and two stages of Clear Cell Renal Carcinoma). The proposed approach significantly reduces computation time from minutes to under a second during inference. Among the evaluated models, Random Forest and XGBoost achieved the best overall performance, with the lowest regression errors and highest classification scores. Deep learning models, particularly CNN and FT-Transformer, also demonstrated competitive results. Overall, all proposed methods outperformed the state-of-the-art baseline in terms of both accuracy and computational efficiency. mFLIP provides a fast and accurate alternative to traditional FVA-based approaches for metabolic flux interval estimation. By leveraging supervised learning on FVA-derived data, it enables scalable analysis of large cohorts while maintaining high predictive performance, making it a practical tool for large-scale metabolic studies.
Can et al. (Tue,) studied this question.