Global Uncertainty Analysis for a Model of TNF-Induced NF-κB Signalling

In this work, we study the problem of computing outer bounds for the region of steady states of biochemical reaction networks modelled by ordinary differential equations, with respect to parameters that are allowed to vary within a predefined region. An improved implementation of an algorithm which we presented earlier is developed in order to increase the computational efficiency. The gain in efficiency enables the analysis of medium scale biochemical network models. The applicability of the algorithm to such networks is illustrated by studying a newly developed model for a tumor necrosis factor signalling pathway. This pathway is of major importance for the inflammatory response in mammals and therefore of high biomedical interest. The proposed uncertainty analysis algorithm is applied to the model in order to understand how variations in the parameters and co-stimulation of different receptor types may affect the signalling response in this pathway.