Fig. 3

Applications of GEMs for interspecies metabolic interactions and understanding human diseases. a GEM-based simulation of the effects of costless metabolites (i.e., metabolites that have no effects on the producing organism’s growth rate) secreted by at least one of two paired microorganisms on their growth under anaerobic and aerobic conditions [144]. The number of growth-supporting environments was increased as a result of cross-feeding. b Prediction of the metabolites (e.g., short-chain fatty acids [SCFAs]) required or produced by four representative gut microbiota species, Escherichia sp., Akkermansia muciniphila, Subdoligranulum variabile, and Intestinibacter bartlettii, which are known to be affected by the type 2 diabetes (T2D) drug metformin [146]. c Prediction of the metabolites produced by gut microbiota species from malnourished children using community GEMs that describe the metabolism of multiple gut microbiota species [147]. The prediction results were consistent with the children’s plasma metabolite profiles. d Prediction of the suppressed photosynthesis of a potato plant (Solanum tuberosum) upon infection by the plant pathogen Phytophthora infestans, which triggers the plant’s defense responses against pathogen attack through oxygenation of ribulose1,5-bisphosphate (RuBP) and subsequently increases in the intracellular levels of reactive oxygen species (ROS) [148]. Formation of glyceraldehyde-3-phosphate (GAP) and starch were also decreased as a result of the infection. e Identification of metabolic differences between liver cancer stem cells (LCSCs) and non-LCSCs, and of the transcription factors responsible for the metabolic changes, by using GEMs integrated with transcriptome data [149]. f Characterization of the reprogrammed metabolism of the endothelium cells of sepsis patients using a human endothelium GEM, iEC2812 [150]. Context-specific GEMs were created using transcriptome and metabolome data obtained from human umbilical vein endothelial cells (HUVECs) treated with lipopolysaccharide (LPS) and/or interferon-γ (IFN-γ). Simulation of the context-specific GEMs indicated that increased glycan and fatty acid metabolism led to increased glycocalyx shedding and endothelial permeability where there was endothelial inflammation. HPAEC human pulmonary artery endothelial cell, HMVEC human microvascular endothelial cell