Open Access Research article

Mathematical modeling of the dynamic storage of iron in ferritin

J Cristian Salgado17*, Alvaro Olivera-Nappa27, Ziomara P Gerdtzen27, Victoria Tapia37, Elizabeth C Theil45, Carlos Conca67 and Marco T Nuñez37

Author Affiliations

1 Laboratory of Process Modeling and Distributed Computing, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago, Chile

2 Centre for Biochemical Engineering and Biotechnology, Department of Chemical Engineering and Biotechnology, University of Chile, Santiago, Chile

3 Department of Biology, University of Chile, Santiago, Chile

4 Council on BioIron at Children's Hospital Oakland Research Institute, 5700 Martin Luther King, Jr. Way, Oakland, CA 94609

5 Department of Nutritional Sciences and Toxicology, University of California, Berkeley, CA 94720

6 Department of Mathematical Engineering, Centre for Mathematical Modeling, University of Chile, Santiago, Chile

7 Millennium Institute for Cell Dynamics and Biotechnology: a Centre for Systems Biology, University of Chile, Santiago, Chile

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BMC Systems Biology 2010, 4:147  doi:10.1186/1752-0509-4-147

Published: 3 November 2010

Abstract

Background

Iron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content.

Results

Simulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated in vitro with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded.

Conclusions

The results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption.