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Cellular iron metabolism is a prime example of this as multiple cell types tend to rely on a similar set of components (proteins and regulatory mechanisms) to ensure iron balance. One recurrent component, ferritin, is the primary iron storage protein in mammalian cells and is necessary for cellular iron homeostasis. Its ability to sequester iron protects cells from rising concentrations of ferrous iron limiting oxidative cell damage. The focus of the present work is establishing a model that tractably represents the ferritin iron sequestration kinetics such that it can be incorporated into larger cell models, in addition to contributing to the understanding of general ferritin iron sequestration dynamics within cells. The model\u2019s parameter values were determined from published kinetic and binding experiments and the model was validated against independent data not used in its construction. Simulation results indicate that FT concentration is the most impactful on overall sequestration dynamics, while the FT iron saturation (number of iron atoms sequestered per FT cage) fine tunes the initial rates. Finally, because this model has a small number of reactions and species, was built to represent important details of FT kinetics, and has flexibility to include subtle changes in subunit composition, we propose it to be used as a building block in a variety of specific cell type models of iron metabolism.<\/jats:p>","DOI":"10.1371\/journal.pone.0281401","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T13:32:57Z","timestamp":1675690377000},"page":"e0281401","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Modeling the iron storage protein ferritin reveals how residual ferrihydrite iron determines initial ferritin iron sequestration kinetics"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2113-9282","authenticated-orcid":true,"given":"Joseph","family":"Masison","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6507-9168","authenticated-orcid":true,"given":"Pedro","family":"Mendes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"pone.0281401.ref001","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/s41540-019-0114-3","article-title":"ModelBricks\u2014modules for reproducible modeling improving model annotation and provenance","volume":"5","author":"AE Cowan","year":"2019","journal-title":"NPJ Syst Biol Appl"},{"issue":"12","key":"pone.0281401.ref002","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1007\/s11538-018-0498-z","article-title":"Reproducible Research Using Biomodels","volume":"80","author":"P. 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