Biological systems are very complex, with thousands of genes, proteins, and other molecules interacting in a directed or undirected manner. These systems can be modeled with networks . Networks allow for visualizing and analyzing interactions between entities in complex systems, e.g., people in social networks or genes in biological networks. Networks are modeled as graphs with a given set of nodes and edges. The nodes represent the investigated entities, while the edges represent the relationships between these entities.
Using networks, different types of analyses of biological systems can be done, such as predicting the system’s response to a drug [2, 3, 4]. Testing drugs and their combination with patients or in a lab is costly and time-consuming. Therefore, computational testing of these drugs and consequently reducing the number of potential drugs for a specific disease is beneficial for medical research. The administration of drugs causes perturbations of the underlying system that can be analyzed using a representative biological network. For these analyses, several different information layers, i.e., omics (genomics, proteomics, and metabolomics) levels, can be utilized. Thus, multi-omics networks combine multiple layers of biological information like gene expression levels, protein quantities, and other available biological information. We are developing pipelines to predict the responses to (multiple) drugs using networks from multi-omics data.