ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data

Metabolic alterations are often used as hallmarks of observable phenotypes. In this regard, reconstructed genome-scale metabolic models (GEMs) provide a rich and computable representation of the entire set of biochemical reactions in a cell. However, the performance of analytical tools for predicting metabolic reaction rates or fluxes using GEMs is sensitive to the assumed metabolic objective that is often unknown and likely context-specific. Here, we propose a novel method called Delta FBA that combines differential gene expression data and GEMs to evaluate differences in the metabolic fluxes between two conditions (perturbation vs. control) without the need for specifying a metabolic objective. In our demonstration, Delta FBA outperformed other existing methods in predicting metabolic flux alterations. Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called Delta FBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, Delta FBA does not require specifying the cellular objective. Rather, Delta FBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of Delta FBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, Delta FBA gives a more accurate prediction of flux differences.

Automated summary

Genome-scale metabolic models (GEMs) provide a comprehensive representation of biochemical reactions in a cell, but predicting metabolic flux alterations using GEMs is sensitive to the assumed metabolic objective. The proposed Delta FBA method integrates differential gene expression data to evaluate metabolic flux differences between conditions directly without specifying a metabolic objective, outperforming existing methods in accuracy.