Based on public and own microarray data sets and by integrating additional available interaction data, a bioinformatics pipeline was established for the fast and convenient extraction of cell line- and experiment-specific candidate networks. The new automated workflow allows obtaining such networks and identifying genes which play significant roles in signalling pathways of interest. Two subsequent filtering steps are thus applied: 1) cell type-specific array data available from public databases is used for co-expression analysis (assuming that co-expressed genes are also co-regulated) to identify cell type-specific clusters and general interactions; 2) additional own stimulation data of specific expression profiles helps to further filter the co-expression network. Only genes with an absolute fold change over a certain threshold were included in the candidate networks. To avoid the exclusion of important central nodes of the network, IPA Knowledge Base (Ingenuity) was used to ensure significance and completeness of the networks. The candidate networks can then be subjected to mathematical modelling and further analysis, e.g. using Probabilistic Boolean Modelling. The pipeline was applied to atherosclerosis related microarray data from the human umbilical vein endothelial cell line (HUVEC) which was chosen to mimic endothelial behaviour during the process of atherosclerosis development. Cells were stimulated during 1, 4 and 24 hours with the cytokine TNF-alpha and/or the micronutrient 1alpha,25(OH)2 vitamin D3, looking for interactions between stress-sensing nuclear factors and nutrient-sensing nuclear receptors which could be part of an imbalanced molecular mechanism towards atherosclerosis. The process was applied to HUVEC and THP-1, a cell model to mimic the behaviour of monocytes in atherosclerosis development, resulting in co-expression networks with 1519 and 2554 nodes, respectively. For HUVEC, this network was already filtered to a final core network of 84 genes.