Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective

Abstract

Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built.

Control of Intracellular Molecular Networks Using Algebraic Methods

“…Identifying the attractors of a system is an important step towards system control. For example, a steady state might represent a cellular phenotype characterized by low expression of an iron exporter [3], a particularly deleterious phenotype in several cancers [46].…”

“…For example, the steady states in [37], discussed in more detail below, correspond to proliferative, apoptotic, or growth-arrest phenotypes of a cancer cell. In [3], the steady states of the model correspond to the observed altered iron metabolism phenotypes in a breast epithelial cell with and without a certain RAS mutation. We present here a method to systematically identify modifications to a model that can change the attractor landscape in prescribed ways.…”

“…Discrete networks defined in this way can be represented in the richer mathematical framework of polynomial dynamical systems (PDSs) [48,19], as can models in other common frameworks, such as Boolean networks [1], logical regulatory graphs [2], or multistate networks [37,3,11,45]. For instance, the mathematical tools associated with PDSs allow for the computation of all steady states and cycles up to a certain length of a system as the solutions to a system of polynomial equations (in a suitably chosen finite field) without explicit simulation of the entire state space.…”

“…The method was applied to a mathematical model of cellular response to DNA damage [4] and a model of large granular lymphocyte apoptosis escape [60]. However, an increasing number of discrete mathematical models in this context are not Boolean, e.g., [37,3,11,45], so that a more general method is desirable, and such a generalization is the focus of this paper.…”

“…The method can find combinatorial interventions that block proliferative steady states. The second is an intracellular iron network model in breast epithelial cells presented in [3]. We identify interventions to recover basal expression of the iron export protein of a malignant breast epithelial cell with RAS over-expression.…”