2016 Conference on Computational Modelling with COPASI
Manchester Institute of Biotechnology, 12th – 13th May, 2016

Using Copasi to model the effect of protein stoichiometry on the unfolded protein response in cancer cells

Neil Mcdonald1, Chris Redfern1, Daryl Shanley1

1 - Newcastle University, UK

Keywords: Unfolded protein response, ER stress, Cancer, Dynamic modelling


Increase in cellular stress levels leads to an increase in the amount of unfolded proteins (UFP) within a cell and initiates a protective mechanism, the unfolded protein response (UPR). The UPR consists of three distinct networks that control the level of UFP within the cell by regulating pro-autophagic and, under irresolvable stress, pro-apoptotic mechanisms. The UPR is dependent upon interactions between GRP78 and the three activators, IRE1, PERK and ATF6; perturbations in these interactions are implicated in various diseases including cancer. Work carried out in Melanoma, Glioblastoma and Neuroblastoma cell lines revealed that the stoichiometry of the three activators varies between cell types and has a significant impact upon the sensitivities of different cell lines to the proteasome inhibitor Bortezomib. We have used Copasi to develop a dynamic model of the UPR, focusing on the interactions between the three activating proteins and their inhibitor GRP78. Employing experimental data for the downstream effectors of each of the three activators, parameter estimation was carried out for three different cell types and six different cell lines. The model predicted the outputs for the downstream effectors of each branch of the UPR in both of the melanoma and glioblastoma cell lines as well as one of the neuroblastoma cell lines. The model was validated using GRP78 overexpression data. Initial concentrations for IRE1, ATF6, PERK and GRP78 were adjusted to reflect experimental data for cells over-expressing GRP78. After four hours of simulation, the model output replicated the experimental data at the relevant time points. This model will facilitate a better understanding of the UPR in cancer and reveal novel targets for biomarker discovery and drug development.

Conference Program