Background: Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging.The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitorerlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated proteinsignalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds incase of overexpression or mutations. Dimerisation with other receptors allows to bypass pathwayblockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. Weused longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplifiedbreast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab orpertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modellingapproach, signalling networks were reconstructed based on these data in a cell line and time coursespecific manner, including prior literature knowledge. Finally, we simulated network response toinhibitor combinations to detect signalling nodes reflecting growth inhibition. Results: The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. InBT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPKsignalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncoveredfeedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cellline. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT andp70S6K. The results indicated a pathway specific drug response, driven by the type of growth factorstimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumabinefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB andRPS6, relating to phenotypic events like cell growth or proliferation. Therefore, we additionallyanalysed RB and RPS6 in the long-term networks. Conclusions: We derived protein interaction models for three breast cancer cell lines. Changes compared to thecommon reference network hint towards individual characteristics and potential drug resistancemechanisms. Simulation of perturbations were consistent with the experimental data, confirming ourcombined reverse and forward engineering approach as valuable for drug discovery and personalisedmedicine.