Inferring the tumorigenic network N2
From network N1, we will infer the rewired network N2 corresponding to cancer cell lines by fitting simulated data to the cancer specific dataset. To this end, we adopt Genetic Algorithm for multiple generations to search for the most likely network rewiring events and collected 50 rewired networks in each generations along with corresponding scores. The Genetic Algorithm performed rewiring on the basic apoptotic network of Figure 1. The comparison for simulated data from network N1 and Yaffe's data for control treatments using Genetic Algorithm, before and after rewiring event in network N1, is shown in Figure 3 and 4.
Figure 3  Simulation results for Genetic Algorithm: fitting experimental data with simulation data. (A) Simulation result for basic apoptotic network, (B) Simulation result for DMSO treatment. (C) Comparing N2 based simulation data with ERL-DOX treatments data by correlation of apoptosis rates over time points. (D) Simulation result for ERL-DOX treatments.
Figure 4 Due to inherent randomness in Genetic Algorithm, the edges added and deleted in N1 (while inferring N2) were different among different run of simulations. Some of the changes in the network were found to be more frequent compared to other. So, just few runs of simulation couldn't be relied upon to find the changes in the N1 network causing cancer. Therefore, to find consistent (or conserved re-wirings) changes to the network, we performed the simulation for 150 times for inferring N2 from N1 and another 150 rounds of simulation for inferring N3 from N2. The changes which occurred in the network with the highest frequency were selected to construct N2 (and subsequently N3). The most frequently removed edges in the N1 network while inferring N2 were SMAC-XIAP, Casp8-Casp3 and p53-PUMA. For 150 rounds of simulations, each of these deletions occurred with a frequency of 146, 143 and 141 respectively. The network edges found to be inserted in the N1 by the Genetic Algorithm with the highest frequencies were XIAP-Casp8 and TNFR-Stat3, with frequencies of 149 and 146.
We verified the various rewiring events using the existing literature and confirmed the validity for the above mentioned network changes. p53 is a tumour suppressor and controls the regulation of PUMA. PUMA induces apoptotic signals inside the cells. Less activity of PUMA leads to apoptosis deficiency which in turn leads to the increased risk of cancer [31]. We also realised that SMAC down-regulates XIAP and XIAP down-regulates Casp3, a pro-apoptotic protein. So if SMAC is working properly, there will be more cell death leading to less tumorigenic behaviour by cells. But if SMAC is not able to down-regulates XIAP, there will be less apoptosis and so more chances of cancer [2]. We also came across literature evidence highlighting the critical role of Casp8 for affecting breast cancer directly [5,32]. STAT3 interacts with various molecules involved in programmed cell death, regulating their functions including MOMP formation, which leads to the release of the cytochrome c [33]. XIAP inhibits the processing of Casp8. In fact, proteolytic processing of several caspases, including Casp9, is not allowed in the presences of XIAP. The lack of processing of Casp8 and Casp9 allows them to interact with Casp3 and promote apoptosis [34]. TNFR activates the Stat3 signalling. The stimulation of TNFR leads the phosphorylation of the Stat3 and Stat5b and the phosphorylated molecules are trans-located into the nucleus [35].