Complex diseases are caused by a combination of genetic and environmental factors, thus a disease phenotype is rarely a consequence of an abnormality in a single effector gene product, but reflects various pathobiological processes that interact in a complex network. In recent years, system biology approaches and, more specifically, network-based approaches emerged as powerful tools for studying complex diseases. These methods are often built on the knowledge of physical or functional interactions between biological entities, which are usually represented as an interaction network. The interactions can be conveniently represented as networks (graphs) with nodes (vertices) which denote molecules, and links (edges) which denote interaction between them. Depending on the type of interaction, the corresponding edge might be directed (i.e. protein activation) or undirected (i.e. binding between two proteins). The physical and functional interaction networks are increasing applied to understand and to analyze complex diseases.

The network construction also helps to facilitate a more personalized approach for the disease diagnosis and treatment. The first step of rational drug design is to understand the cellular dysfunction that is caused by a disease. Single-target drugs may, perhaps, correct some dysfunctional aspects of the disease module, but they could also alter the activity of molecules that are situated in the neighborhood of the disease module, leading to detectable side effects. This network-based view of drug action implies that most disease phenotypes are  difficult to reverse through the use of a single ‘magic bullet’, that is, an intervention that affects a single node  in the network. Increasing attention is therefore being given to therapies that involve multiple targets, which may be more effective in reversing the disease phenotype than a single drug. This new drug discovery approach is called polypharmacology. The efficacy of this approach has been demonstrated by combinatorial therapies for AIDS, cancer and depression, raising an important question: can one systematically identify multiple drug targets that have an optimal impact on the disease phenotype? This is an archetypical network problem and has led to the development of methods to identify optimal drug combinations, starting either from the metabolic network or from the bipartite network that links compounds to their drug-response phenotypes. Such research has led to potentially safer multi-target combinations for inflammatory conditions and to the optimization of anticancer drug combinations.

In this context, we developed a method that, given a complex disease, starts by constructing a network that integrates different data sources, including gene expression data sets, protein interactions and disease-related pathways. Our strategies exploit the topological features of the network to identify the entities involved in the disease and the core disease causative pathways. In this way, we are able to identify possible combinations of hit targets where it is desirable to act with a multicomponent therapy. The best ranked combinations are selected based on a synergistic score: for each of them a potential new therapy could be discovered.

Our strategies are furthermore focused on the analysis of interaction networks between drugs (small molecules) and genes (proteins) in order to develop methods useful for pharmacogenomic discovery. Pharmacogenomics has the potential to transform the way medicine is practiced, by replacing broad methods of screening and treatment with a more personalized approach that takes into account both clinical factors and patient’s genetics. One area where gene-based prescribing is steadily advancing is the area of cancer genomics.

Knowledge on the mutational status of genes can be better understood when integrated with information about gene expression and related to alterations in: the copy number of each gene (CNVs), a very common phenomenon in cancer; mutations in promoters and enhancers; variations in the affinity of transcription factors and DNA binding proteins; or dysregulation of epigenetic control.

The construction and analysis of a network that integrates all these data can aid the discovery of the suitable genes to target. Once the potential gene or pathway targets are identified, bioinformatics methods can be used to generate prediction for potential “leads” (or drug candidates) for a high-throughput drug screen.