In this paper, we propose a hybrid modelling approach that combines the advantages of knowledge-driven and data-driven approaches for modelling cancer signalling pathways and cell fate decisions. From a generic apoptotic network, we simulate the time series data of signalling proteins using ODEs based on a network candidate modified from the generic network by in silico rewiring; then the simulated data are compared with the real data. This in in silico rewiring, simulation and data fitting is repeated iteratively to improve the goodness of fit using Genetic Algorithm. Through such an optimisation we can detect the topology alterations of the network that allows close fitting of model to the real data. As such, network rewiring can be inferred from real data. Most of rewiring events predicted by our model are supported by existing literature.