A Model of the Cellular Iron Homeostasis Network Using Semi-Formal Methods for Parameter Space Exploration

This paper presents a novel framework for the modeling of biological networks. It makes use of recent tools analyzing the robust satisfaction of properties of (hybrid) dynamical systems. The main challenge of this approach as applied to biological systems is to get access to the relevant parameter sets despite gaps in the available knowledge. An initial estimate of useful parameters was sought by formalizing the known behavior of the biological network in the STL logic using the tool Breach. Then, once a set of parameter values consistent with known biological properties was found, we tried to locally expand it into the largest possible valid region. We applied this methodology in an effort to model and better understand the complex network regulating iron homeostasis in mammalian cells. This system plays an important role in many biological functions, including erythropoiesis, resistance against infections, and proliferation of cancer cells.Comment: In Proceedings HSB 2012, arXiv:1208.315

Use of Mathematical Modeling and Other Biophysical Methods for Insights into Iron-Related Phenomena of Biological Systems

Iron is a crucial nutrient in most living systems. It forms the active centers of many proteins that are critical for many cellular functions, either by themselves or as Fe-S clusters and hemes. However, Fe is potentially toxic to the cell in high concentrations and must be tightly regulated. There has been much work into understanding various pieces of Fe trafficking and regulation, but integrating all of this information into a coherent model has proven difficult. Past research has focused on different Fe species, including cytosolic labile Fe or mitochondrial Fe-S clusters, as being the main regulator of Fe trafficking in yeast. Our initial modeling efforts demonstrate that both cytosolic Fe and mitochondrial ISC assembly are required for proper regulation. More recent modeling efforts involved a more rigorous multi tiered approach. Model simulations were optimized against experimental results involving respiring wild-type and Mrs3/4-deleted yeast. Simulations from both modeling studies suggest that mitochondria possess a “respiratory shield” that prevents a vicious cycle of nanoparticle formation, ISC loss, and subsequent loading of mitochondria with iron. Work has also been done in understanding an accumulation of Fe in stationary grown yeast cells. This accumulated Fe was found to be localized to the cell wall, and can be used as cells are metabolically reactivating by being placed into fresh media. A mathematical model has been developed to describe the metabolism of oxygen and nutrients in the autocatalytic production of active cells, with subsequent deactivation of cells as nutrients became limiting. E. coli have similar Fe contents relative to mitochondria, and they also appear to also employ a “respiratory shield”. This hypothesis was tested by either inhibiting respiratory complexes with CN, or by growing cells into a metabolically inactive stationary growth state. The generated nanoparticles were not associated with ferritins, which is surprising given that much of the literature claims that ferritin Fe makes up a large portion of cellular Fe. The iron content of murine hearts was also studied. Previous work from the Lindahl lab focused on murine brains and livers, which contain ferritin at young and old ages, while losing it in middle age. Hearts differ from these two organs, in that they mainly contain respiratory iron sulfur clusters, and only gain ferritin as the mice approach old age