{"doi":"10.1007/s10534-025-00758-7","title":"A kinetic mathematical model of comprehensive iron metabolism in a respiring yeast cell: a basic-pathways approach to solving a large system dynamically","abstract":"The individual functions of most iron-containing species in Saccharomyces cerevisiae are fairly-well understood, but less is known regarding how they function collectively as a unified system. Here, an ODE-based kinetic cell model was developed to reveal system's-level behavior of iron metabolism. The dimensionally-accurate in silico cell was divided into 5 compartments. It contained 80 components that engaged in 169 reactions. The cell grew on nutrients IRON, CARBON and OXYGEN. All major iron-related processes were represented including the biosynthesis and metallation of iron-containing proteins, trafficking of labile iron pools, homeostatic regulation, respiration, the TCA cycle, iron-sulfur-cluster and heme biosynthesis, the synthesis of DNA, phospholipids, amino acids, and nucleotide triphosphates, and reactions involving oxygen and reactive-oxygen-species. Iron and carbon were conserved in reaction stoichiometries. The time-dependent model was solved using the Basic Pathways approach, despite limited kinetic information. Once regulated appropriately, the system could withstand perturbations in component concentrations by returning to its original steady-state. It responded to changes in nutrient iron and oxygen concentrations and to changes in rate-constants, yielding altered sets of steady-state component concentrations. The latter type of perturbation is tantamount to altering the expression level of a gene. This ability offers the potential to explain phenotypic changes of genetic mutations on the mechanistic molecular level. The model included all established iron-related cellular processes (albeit in combined forms), and a highly interrelated reaction network reflecting a mutually autocatalytic system. Steady-state iron concentrations in the cell, organelles, and components were reasonably near to those observed/estimated experimentally.","journal":"BioMetals","year":2025,"id":549328,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5952,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1147290,"name":"Jay R. Walton","orcid":null,"position":1,"is_corresponding":false},{"id":434341,"name":"Paul A. Lindahl","orcid":"0000-0001-8307-9647","position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":null,"created_at":"2026-07-19T02:54:07.823422Z","pmid":"41372682","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}