{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Soleymani F"],"funding":["Deutsche Forschungsgemeinschaft","European Research Council","Novo Nordisk Foundation Center for Basic Metabolic Research"],"pagination":["1572-1583"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12489276"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["248(3)"],"pubmed_abstract":["Comparative molecular and physiological analyses of organisms from one taxonomic group grown under similar conditions offer a strategy to identify gene targets for trait improvement. While this strategy can also be performed in silico using genome-scale metabolic models for the compared organisms, we continue to lack solutions for the de novo generation of such models, particularly for eukaryotes. To facilitate model-driven identification of gene targets for growth improvement in green algae, here we present a semiautomated platform for de novo generation of genome-scale algal metabolic models. We deployed this platform to reconstruct an enzyme-constrained, genome-scale metabolic model of Chlorella ohadii, the fastest growing green alga reported to date, and validated the growth prediction"],"journal":["The New phytologist"],"pubmed_title":["Constraint-based metabolic modeling reveals metabolic properties underpinning the unprecedented growth of Chlorella ohadii."],"pmcid":["PMC12489276"],"funding_grant_id":["SCHR 617/13‐1","NNF23OC0085412","NI 1472/16‐1","NI 1472/16-1","SCHR 617/13-1","101076560"],"pubmed_authors":["Correa SM","Treves H","Nikoloski Z","Arend M","Soleymani F","Razaghi-Moghadam Z","Forghanisardaghi N"],"additional_accession":[]},"is_claimable":false,"name":"Constraint-based metabolic modeling reveals metabolic properties underpinning the unprecedented growth of Chlorella ohadii.","description":"Comparative molecular and physiological analyses of organisms from one taxonomic group grown under similar conditions offer a strategy to identify gene targets for trait improvement. While this strategy can also be performed in silico using genome-scale metabolic models for the compared organisms, we continue to lack solutions for the de novo generation of such models, particularly for eukaryotes. To facilitate model-driven identification of gene targets for growth improvement in green algae, here we present a semiautomated platform for de novo generation of genome-scale algal metabolic models. We deployed this platform to reconstruct an enzyme-constrained, genome-scale metabolic model of Chlorella ohadii, the fastest growing green alga reported to date, and validated the growth prediction","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-04T01:48:41.488Z","creation":"2026-05-04T03:13:57.714Z"},"accession":"S-EPMC12489276","cross_references":{"pubmed":["40913342"],"doi":["10.1111/nph.70528"]}}