An approach to the conditional robustness problem for biochemical networks

Robustness analysis of mathematical models is of high importance for studying cancer and its proliferation. In this paper, we introduce the concept of “conditional robustness” for nonlinear ODE models of cancer, and we propose a method to identify regions in the parameter space which exhibits desired behaviors. The proposed approach allows the selection of key parameters influencing system robustness, that is, the selection of key nodes in the biochemical network whose inhibition should improve drug response. We illustrate our approach using a model of the EGFR-IGF1R signal transduction system, which is an important network for translational oncology and cancer therapy.