Robustness of complex feedback systems: application to oncological biochemical networks

Biochemical transduction networks can be modelled through a proper set of differential equations, and at the same time they can be experimentally analysed only by measuring a few signals at the end of the cascades. The study of those networks is of special importance in oncology. The approach proposed in this paper is aimed at characterising the network robustness with respect to both parameters and initial condition perturbations. The key idea is to introduce a robustness index, the proliferation index, and to study its behaviour over the parameter space. The index relates the measurable signals, and the shape of their time behaviour, to the model parameters and initial conditions. In addition, the paper will also provide specific results for a dynamic model of the EGFR–IGFR receptor pathway, which turns out to be relevant to the study of some specific pathologies, such as lung cancer. The connection between the proposed robustness index and the pathology will also be addressed.