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

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions occurring in a cell. Constraint-based modeling tools like flux balance analysis (FBA) developed for the purposes of predicting metabolic flux distribution using GEMs face considerable difficulties in estimating metabolic flux alterations between experimental conditions. Particularly, the most appropriate metabolic objective for FBA is not always obvious, likely context-specific, and not necessarily the same between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that employs constraint-based modeling, in combination with differential gene expression data, to evaluate changes in the intracellular flux distribution between two conditions. Notably, ΔFBA does not require specifying the cellular objective to produce the flux change predictions. We showcased the performance of ΔFBA 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.