Genome-scale metabolic models are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as algae and plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active under a given condition, including: developmental stage, cell type, or environmental state. As a result, several approaches have been proposed to reconstruct condition-specific (sub)networks from existing genome-scale models by integrating high-throughput data (e.g., transcriptomics, proteomics, or metabolomics). Application of these approaches to crop plants aims at generating more accurate predictions and realistic metabolic engineering strategies. Here we revisit the existing computational approaches for extraction of condition-specific networks, show that they can all be placed in a generalized framework, and propose a novel direction for addressing this problem.