An open-source, genome-scale human metabolic model and a web portal to explore the model are generated.

Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications. Significance Animal models and genome-scale metabolic models (GEMs) are two important tools for studying human diseases, but a proper integration of the two is lacking. To bridge them, we developed a reproducible and systematic framework by which the GEMs for major model animals are reconstructed and maintained. Due to the shared namespace, biological insights can be inferred from comparative analysis of the omics data from different animal models of the same disease. The drug targets and biomarkers derived from integrative metabolic analysis can thus be validated and translated to treatments and/or diagnostic methods. As an example, we demonstrated their utility in discovering potential biomarkers for early diagnosis of Alzheimer’s disease, where there is a lack of effective diagnostics and treatments.

This work aims to conduct flux balance analysis of serine alkaline protease overproduction in Bacillus subtilis using enzyme-constrained genome scale model and to compare the results with fluxes obtained from a smaller, bioreaction-based model. Fluxes of the enzyme constraint genome scale model were calculated using CobraToolbox v3.0 and compared with those of bioreaction-based model for the specific growth rate of zero. The active reaction number first increased and then remained constant with specific growth rate for enzyme constrained genome scale model. The SAP synthesis flux increased with a decrease in specific growth rate for both models. The TCA cycle was active for both models, but with lower fluxes for enzyme-constrained genome scale model. Anaplerotic reactions were active only for bioreaction-based model. Glycolysis pathway fluxes were active for enzyme-constrained genome scale model, meanwhile gluconeogenesis pathway fluxes were active for bioreaction-based model. Oxidative pentose phosphate pathway was inactive for both models and generally higher pentose phosphate pathway fluxes were obtained using bioreaction-based model. The fluxes toward amino acid synthesis pathways and serine alkaline protease synthesis were higher with bioreaction-based model. Since TCA cycle fluxes were lower with enzyme constrained genome scale model, ATP synthesis was lower with enzyme constrained genome scale model compared to bioreaction-based model. For both models, active pathways were the same for TCA cycle, pentose phosphate pathway, amino acid synthesis pathways except glycolysis pathway. The results showed that bioreaction-based model gave more sound results compared to enzyme constrained genome scale model since gluconeogenesis should be active with the carbon source of citrate.

Saccharomyces cerevisiae (baker’s yeast) is both a highly valuable industrial microorganism and a central model system in biological and medical research. Its importance is strengthened by being the first eukaryote to have its genome fully sequenced and publicly released, providing the basis for systematic studies of cellular function, regulation, and metabolism. Using this genomic foundation, researchers have developed at least 16 genome-scale metabolic models over the past two decades. These reconstructions integrate extensive biochemical, genetic, and physiological data to map the yeast metabolic network and its gene–reaction relationships. Genome scale metabolic models are commonly analyzed using flux balance analysis, a constraint-based computational framework that represents metabolism through stoichiometric matrices and applies mass-balance. By optimizing biological objectives such as biomass production, flux balance analysis predicts feasible flux distributions without requiring detailed kinetic parameters. Together, genome scale metabolic models and flux balance analysis form a powerful platform for studying yeast metabolism, enabling large-scale predictions of growth, gene essentiality, metabolic bottlenecks, theoretical yields, and responses to genetic or environmental changes. This review summarizes key advances in genome-scale metabolic modeling of Saccharomyces cerevisiae over the past twenty three years.