Abstract
It is well known that iron is an essential element for life but is toxic when in excess or in certain forms. Accordingly there are many diseases that result directly from either lack or excess of iron. Yet many molecular and physiological aspects of iron regulation have only been discovered recently and others are still elusive. There is still no good quantitative and dynamic description of iron absorption, distribution, storage and mobilization that agrees with the wide array of phenotypes presented in several iron-related diseases. The present work addresses this issue by developing a mathematical model of iron distribution in mice calibrated with ferrokinetic data and subsequently validated against data from mouse models of iron disorders, such as hemochromatosis, β-thalassemia, atransferrinemia and anemia of inflammation. To adequately fit the ferrokinetic data required inclusion of the following mechanisms: a) transferrin-mediated iron delivery to tissues, b) induction of hepcidin by transferrin-bound iron, c) ferroportin-dependent iron export regulated by hepcidin, d) erythropoietin regulation of erythropoiesis, and e) liver uptake of NTBI. The utility of the model to simulate disease interventions was demonstrated by using it to investigate the outcome of different schedules of transferrin treatment in β-thalassemia.
Highlights
Iron is an essential metal in living organisms, which is required as a co-factor for many proteins with roles in electron transport and oxygen binding
We created a computational model of the regulation of iron distribution in the body of a mouse based on experimental data
In the case of the adequate iron diet, the steady state values of total iron, Tf saturation, hepcidin and EPO for the adequate diet (S2 Table) are in the range observed in the literature [24,25,26,27,28]
Summary
All simulations and computational analyses were carried out with the open source software COPASI versions 4.20 to 4.23 [61,62]. Model calibration was carried out by non-linear leastsquares as implemented in COPASI and detailed below. Model validation was carried out for a series of physiological and disease conditions different from those used for parameter estimation (see Results section). Validation is important to establish generality of the model and as a check that the parameter estimation did not significantly overfit the data. The differential equation-based model was constructed based on a previous version [19] with important modifications as described below. Full details of the model are included in the supplementary information: equations, parameter values (S1 Table), and initial conditions (S2 Table). Simulation files in the COPASI format and in the SBML standard [65] are included in the supplementary information.
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