{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL2110210002?filename=Suppl_Data_S1.xml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Sascha Schäuble"],"curationStatus":["Non-curated"],"modellingApproach":["constraint-based model"],"levelVersion":["L3V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL2110210002"],"isPrivate":["false"],"repository":["BioModels"],"modelFormat":["SBML"],"omics_type":["Models"],"tokenised_name":["MirhakkakSchaeuble2021   a Candida albicans genome scale metabolic model reconstruction"],"publication_year":["2021"],"submissionId":["MODEL2110210002"],"publication_authors":["Mirhakkak MH, Sascha Schäuble, Klassert TE, Brunke S, Brandt P, Loos D, Uribe RV, Senne de Oliveira Lino F, Ni Y, Vylkova S, Slevogt H, Hube B, Weiss GJ, Sommer MOA, Panagiotou G"],"first_author":["Mirhakkak MH"],"publication":["10.1038/s41396-020-00848-z,\n                            Candida albicans is a leading cause of life-threatening hospital-acquired infections and can lead to Candidemia with sepsis-like symptoms and high mortality rates. We reconstructed a genome-scale C. albicans metabolic model to investigate bacterial-fungal metabolic interactions in the gut as determinants of fungal abundance. We optimized the predictive capacity of our model using wild type and mutant C. albicans growth data and used it for in silico metabolic interaction predictions. Our analysis of more than 900 paired fungal-bacterial metabolic models predicted key gut bacterial species modulating C. albicans colonization levels. Among the studied microbes, Alistipes putredinis was predicted to negatively affect C. albicans levels. We confirmed these findings by metagenomic sequencing of stool samples from 24 human subjects and by fungal growth experiments in bacterial spent media. Furthermore, our pairwise simulations guided us to specific metabolites with promoting or inhibitory effect to the fungus when exposed in defined media under carbon and nitrogen limitation. Our study demonstrates that in silico metabolic prediction can lead to the identification of gut microbiome features that can significantly affect potentially harmful levels of C. albicans.. 5, 15.\n                            Systems Biology & Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, 07745, Jena, Germany."],"submitter_mail":["sascha.schaeuble@leibniz-hki.de"],"publication_doi":["10.1038/s41396-020-00848-z"],"submitter_affiliation":["Jena University Language and Information Engineering Lab, Friedrich-Schiller-University Jena, Jena, Germany sascha.schaeuble@uni-jena.de.Systems Biology and Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany."],"additional_accession":[]},"is_claimable":false,"name":"MirhakkakSchaeuble2021 - a Candida albicans genome-scale metabolic model reconstruction","description":"A genome-scale metabolic model (GEM) of C. albicans was reconstructed and curated by using phenotypic microarray data and resolving erroneous energy-generating cycles.\nIt comprises 771 genes, 3082 metabolic reactions and 2733 metabolites.\nIn Mirhakkak et al. 2021 (https://doi.org/10.1038/s41396-020-00848-z) the C. albicans GEM was simulated together with bacterial GEMs to assess key determinants of C. albicans gut colonization levels.","dates":{"last_modification":"2021-10-21","publication":"2023-04-25","submission":"2021-10-21"},"accession":"MODEL2110210002","cross_references":{"doi":["10.1038/s41396-020-00848-z"]}}